customer performance measurement · using appropriate tools and methods of customer relationship...

Master Thesis

Presented to the Faculty of Economics, University of Fribourg, Switzerland

In Partial Fulfilment of the Requirements for the Degree of

Master of Arts in Management

CUSTOMER PERFORMANCE MEASUREMENT

Analysis of the Benefit of a Fuzzy Classification Approach in Customer Relationship Management

Author: Darius Zumstein Address: Route du Champ-des-Fontaines 24 1700 Fribourg (Switzerland) E-Mail: dzumstein(at)gmx.ch Homepage: www.dzumstein.ch Mobile: +41 (0)78 870 95 55 Place of origin: Burgdorf/Seeberg BE Matriculation #: 00-201-327 1st Reader: Prof. Dr. Maurizio Vanetti 2nd Reader: Prof. Dr. Andreas Meier Tutor: Nicolas Werro

Date: Fribourg, 7th of March 2007

SEMINAR FOR MARKETING AND COMMUNICATION

SEMINAR FÜR MARKETING & UNTERNEHMENSKOMMUNIKATION

DEPARTMENT OF INFORMATICS

DEPARTEMENT FÜR INFORMATIK

UNIVERSITY OF FRIBOURG SWITZERLAND

UNIVERSITÄT FREIBURG SCHWEIZ

- I -

Abstract

Customers are the most valuable asset of a company. As a result, customers have to be clas-

sified, analysed, evaluated, segmented and managed according to their value for the company

using appropriate tools and methods of Customer Relationship Management (CRM).

This master thesis proposes fuzzy classification as a multidimensional data analysis and man-

agement method suitable for realising these CRM processes and for establishing profitable

customer relationships. In contrast to other data mining and statistical methods, fuzzy classifi-

cation and fCQL (fuzzy Classification Query Language) allow the classification of customers

into more than one class at the same time.

The application of the fuzzy classification approach to widely used management tools like the

SWOT, portfolio and ABC analysis and to scoring models enables a better and fairer classifi-

cation, segmentation and management of customers. So far, these methods have mostly been

applied uncritically with sharp classes, although sharp segmentation can obviously be very

arbitrary, imprecise, unfair and discriminatory and may have negative effects.

The application of the fuzzy portfolio analysis within the scope of performance measurement is

especially suited to classifying, analysing, evaluating and improving important monetary cus-

tomer performance indicators, like turnover, contribution margins, profit and customer equity,

and non-monetary indicators, such as customer value, satisfaction, loyalty and retention.

Surprisingly, little research has been done on Customer Performance Measurement (CPM)

and customer performance indicators despite the increasing theoretical and practical impor-

tance of CRM. This work discusses a holistic customer performance measurement framework

with 170+ Customer Performance Indicators (CPIs) and relevant Key Customer Performance

Indicators (KCPIs). To avoid misclassifications, to improve the quality of customer evaluations

and to exploit customer potential, it is suggested to classify all indicators fuzzily.

Customer performance indicators for revenue and profitability, and customer investment, rela-

tionship, recommendation, information and cooperation indicators allow to segment customers

precisely, to optimise fuzzy classified customer portfolios, to drive the proposed CRM success

chain and to define customer strategies in order to increase corporate profits and growth.

Key words:

Fuzzy classification, fuzzy Classification Query Language (fCQL), Customer Relationship Management (CRM), analytical CRM (aCRM), customer performance measurement, customer performance indicators, fuzzy customer segmentation, management tools, fuzzy portfolio analysis, fuzzy credit rating.

- II -

Kurzfassung

Kunden sind die wertvollste Ressource eines Unternehmens. Deshalb müssen Kunden durch

geeignete Instrumente des Customer Relationship Managements (CRM) entsprechend ihrem

Wert für das Unternehmen analysiert, klassifiziert, beurteilt, segmentiert und behandelt wer-

den. Diese Arbeit schlägt unscharfe Klassifikation als eine Analyse- und Managementmethode

vor, um solche CRM-Prozesse umzusetzen und profitable Kundenbeziehungen aufzubauen.

Die unscharfe Klassifikation und fCQL (fuzzy Classification Query Language) verbinden fuzzy

logic mit relationalen Datenbanken und erlauben im Gegensatz zu anderen Data Mining und

statistischen Methoden, dass Kunden mehreren Klassen gleichzeitig angehören können.

Wird der Ansatz der unscharfen Klassifikation auf weit verbreitete Managementinstrumente

wie etwa auf die Portfolio-, SWOT-, ABC-Analyse oder Scoring-Modelle angewendet, können

Kunden besser und fair klassifiziert, segmentiert und gehandhabt werden. Bis anhin wurden

diese Methoden der Kundensegmentierung unkritisch mit trennscharfen Klassen durchgeführt,

obwohl scharfe Segmentierung offensichtlich sehr willkürlich, ungenau und diskriminierend

sein kann, und womöglich negative Auswirkungen nach sich zieht.

Unscharfe Klassifikationen und unscharfe Portfolioanalysen können gerade im Rahmen des

Performance Measurement nutzbringend eingesetzt werden, um monetäre Kundenkennzah-

len (z.B. Umsätze, Deckungsbeiträge, Gewinne, Kundenwert) und nicht-monetäre Kennzahlen

(Kundennutzen, -zufriedenheit, -loyalität oder -bindung) zu beurteilen und zu verbessern.

Trotz der grossen theoretischen und praktischen Bedeutung des CRMs gibt es erstaunlicher-

weise wenig Literatur zu Kundenkennzahlen und zu Kundenkennzahlensystemen. Deshalb

diskutiert diese Arbeit ein Customer Performance Measurement (CPM) Framework mit 170+

Customer Performance Indicators (CPIs) und zentralen Key Customer Performance Indicators

(KCPIs), und empfiehlt diese unscharf zu klassifizieren and zu bewerten. Kennzahlen über die

Kundenprofitabilität, -investitionen und die Kundenbeziehung, sowie über das Weiterempfeh-

lungs-, Informations- und Kooperationsverhalten von Kunden, erlauben den Verantwortlichen,

unscharf klassifizierte Kundenportfolios zu optimieren, sowie an den wichtigen und richtigen

Stellen der vorgeschlagenen CRM-Erfolgskette die Hebel anzusetzen und Kundenstrategien

umzusetzen, um damit dem Unternehmen zu höherem Gewinn und Wachstum zu verhelfen.

Stichworte:

Unscharfe Klassifikation, fuzzy Classification Query Language (fCQL), Customer Relationship Management (CRM), analytisches CRM, Kundenkennzahlensystem, Kundenkennzahlen, unscharfe Kundensegmentierung, Management Tools, unscharfe Portfolioanalyse, unscharfe Kreditwürdigkeitsprüfung.

- III -

Contents

Abstract .................................................................................................................................... I

Contents ................................................................................................................................ III

List of Figures.........................................................................................................................V

List of Tables .....................................................................................................................VIII

List of Abbreviations........................................................................................................... IX

Acknowledgement ...............................................................................................................XI

CHAPTER 1: INTRODUCTION.............................................................................. 1

1.1 Motivation ...............................................................................................................2 1.2 Problem Statement .................................................................................................3 1.3 Objectives ...............................................................................................................4 1.4 Outline of the Thesis...............................................................................................5

CHAPTER 2: FUZZY CLASSIFICATION .................................................................7

2.1 The Approach of Fuzzy Classification ....................................................................8 2.1.1 Classification as a Database Schema Extension...................................................8 2.1.2 Fuzzy Classification with Linguistic Variables......................................................10 2.1.3 Aggregation Operator...........................................................................................11 2.1.4 Multidimensional Fuzzy Classification..................................................................13 2.1.5 Dynamic Fuzzy Classification...............................................................................16

2.2 Fuzzy Classification Query Language (fCQL) ......................................................17 2.2.1 Introduction...........................................................................................................17 2.2.2 Fuzzy Classification Query Examples..................................................................17 2.2.3 Architecture of the fCQL Toolkit ...........................................................................18 2.2.4 Advantages of Fuzzy Classification and fCQL.....................................................20

CHAPTER 3: FUZZY CLASSIFICATION MANAGEMENT TOOLS ...........................22

3.1 Potential Business Applications for Fuzzy Classification......................................23 3.1.1 Overview ..............................................................................................................23 3.1.2 Existing Literature on Marketing and Fuzzy Classification...................................24

3.2 Fuzzy Portfolio Analysis........................................................................................25 3.2.1 Definition ..............................................................................................................25 3.2.2 Sharp Classification and Disadvantages..............................................................26 3.2.3 Fuzzy Classification and Advantages ..................................................................27

3.3 Fuzzy SWOT Analysis ..........................................................................................31 3.3.1 Definition ..............................................................................................................31 3.3.2 Sharp Classification and Disadvantages..............................................................31 3.3.3 Fuzzy Classification and Advantages ..................................................................32

3.4 Fuzzy ABC Analysis .............................................................................................35 3.4.1 Definition ..............................................................................................................35 3.4.2 Sharp Classification and Disadvantages..............................................................35 3.4.3 Fuzzy Classification and Advantages ..................................................................36

3.5 Fuzzy Scoring Methods ........................................................................................39 3.5.1 Definition ..............................................................................................................39 3.5.2 Sharp Classification and Disadvantages..............................................................39 3.5.3 Fuzzy Classification and Advantages ..................................................................40

Contents

- IV -

CHAPTER 4: ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT.............44

4.1 Customer Relationship Management (CRM)........................................................45 4.1.1 Overview ..............................................................................................................45 4.1.2 The Development to the Customer Oriented Company.......................................46 4.1.3 CRM and Customer Management .......................................................................47 4.1.4 Definition of CRM .................................................................................................49 4.1.5 Objectives and Key Points of CRM......................................................................51

4.2 Customer Performance Measurement..................................................................54 4.2.1 Definitions.............................................................................................................54 4.2.2 Processes of Customer Performance Measurement ...........................................55

4.3 Customer Performance Indicators ........................................................................57 4.3.1 Definitions.............................................................................................................57 4.3.2 Categories of Customer Performance Indicators.................................................58 4.3.3 Customer Performance Indicators in Business Practice......................................61

CHAPTER 5: FUZZY CUSTOMER SEGMENTATION .............................................62

5.1 Fuzzy Customer Segmentation with Important Indicators ....................................63 5.1.1 Definitions.............................................................................................................63 5.1.2 Fuzzy Clustering...................................................................................................64 5.1.3 Methods of Customer Segmentation....................................................................65 5.1.4 Selected Indicators for Fuzzy Customer Segmentation.......................................69 5.1.5 Customer Orientation ...........................................................................................70 5.1.6 Customer Value....................................................................................................71 5.1.7 Customer Satisfaction ..........................................................................................72 5.1.8 Customer Loyalty .................................................................................................73 5.1.9 Customer Retention .............................................................................................75 5.1.10 Repurchases ........................................................................................................78 5.1.11 Add-on Selling ......................................................................................................78 5.1.12 Share of Wallet.....................................................................................................79 5.1.13 Turnover ...............................................................................................................81 5.1.14 Contribution Margins ............................................................................................82 5.1.15 Profitability............................................................................................................84 5.1.16 Customer Equity and Customer Lifetime Value (CLV).........................................86

5.2 Fuzzy Market Segmentation .................................................................................91

CHAPTER 6: FUZZY CREDIT RATING................................................................94

6.1 Methods of Sharp Credit Rating ...........................................................................95 6.1.1 Definitions.............................................................................................................95 6.1.2 Subjective Expertise.............................................................................................95 6.1.3 Statistical Methods ...............................................................................................96 6.1.4 Disadvantages of Sharp Credit Rating.................................................................98

6.2 Methods of Fuzzy Credit Rating ...........................................................................99 6.2.1 Existing Literature on Fuzzy Credit Rating...........................................................99 6.2.2 Fuzzy Credit Rating with fCQL...........................................................................101 6.2.3 Other Applications for Fuzzy Classification in Banking......................................106

CHAPTER 7: CONCLUSION ............................................................................. 107

7.1 Summary ............................................................................................................108 7.2 Critical Remarks .................................................................................................115 7.3 Outlook ...............................................................................................................117

References and Further Reading ..................................................................................... 118

Appendix............................................................................................................................. 133

Statement............................................................................................................................. 144

- V -

List of Figures

Figure 1: Application of Fuzzy Classification to Popular Management Tools .......................... 2 Figure 2: Theoretical Classification of the Master Thesis ........................................................ 5 Figure 3: Structure of the Master Thesis ................................................................................. 6 Figure 4: Structure of Chapter 2: Fuzzy Classification ............................................................ 8 Figure 5: Classification Space defined by Customer Attractiveness & Competitive Position ..9 Figure 6: Concept of Linguistic Variables .............................................................................. 10 Figure 7: Fuzzy Classification with Membership Functions ................................................... 11 Figure 8: t-Norms, t-Conorms and Averaging Operator......................................................... 12 Figure 9: Three-Dimensional Sharp (a) and Fuzzy (b) Classification .................................... 14 Figure 10: Example of Hierarchical Multidimensional Fuzzy Classification ............................. 15 Figure 11: Dynamic Fuzzy Classification and Implementation of a Trigger Mechanism.......... 16 Figure 12: Architecture of the fQCL Toolkit ............................................................................. 18 Figure 13: Screenshots of the fCQL Toolkit Query Panel........................................................ 19 Figure 14: Examples of Tasks and Methods of Data Mining ................................................... 21 Figure 15: Fuzzy Classification as a Promising Management Tool for Different Fields........... 23 Figure 16: The Boston Consulting Group Matrix (a) and Norm Strategies (b)......................... 26 Figure 17: Sharp (a) and Fuzzy (b) BCG Portfolio................................................................... 26 Figure 18: Sharp (a) and Fuzzy (b) Investments ..................................................................... 28 Figure 19: Balancing of Fuzzy Classified Portfolios................................................................. 29 Figure 20: Sharp (a) and Fuzzy Classified (b) McKinsey/General Electrics Portfolio .............. 30 Figure 21: Sharp (a) and Fuzzy (b) SWOT Matrix ................................................................... 31 Figure 22: Fuzzy Strength (a), Weakness (b), Opportunity (c) and Threat (d) Matrices.......... 32 Figure 23: Sharp (a) and Fuzzy (b) Risk Matrix ....................................................................... 34 Figure 24: Fuzzy ABC Analysis ............................................................................................... 36 Figure 25: Fuzzy ABC Analysis with Different Customer Performance Indicators................... 37 Figure 26: Combination of the Fuzzy Portfolio and ABC Analysis ........................................... 38 Figure 27: Sharp (a) and Fuzzy (b) RFM Method.................................................................... 41 Figure 28: Fuzzy RFM Incentives ............................................................................................ 42 Figure 29: Structure of Chapter 4 and 5 .................................................................................. 45 Figure 30: The Development to the Customer-Oriented Company ......................................... 46 Figure 31: Applications of Fuzzy Classification in the Domain of Customer Management...... 47 Figure 32: Fuzzy Classification and Individual Marketing ........................................................ 48 Figure 33: The Use of Fuzzy Classification in Typical Tasks of CRM ..................................... 49

List of Figures

- VI -

Figure 34: CRM Application Architecture................................................................................. 50 Figure 35: Mobile Analytical Customer Relationship Management ......................................... 51 Figure 36: CRM Success Chain .............................................................................................. 52 Figure 37: Dimensions of Customer Performance Measurement............................................ 54 Figure 38: Processes of Customer Performance Measurement.............................................. 55 Figure 39: Measurement Dimensions of the CPIP ‘Customer Profit’ ....................................... 58 Figure 40: Measurement Dimensions of the CRI ‘Customer Loyalty’ ...................................... 59 Figure 41: Measurement Dimensions of the CReI ‘Number of Customer Recommendations’ 59 Figure 42: CRM Success Chain with 170+ Customer Performance Indicators ....................... 60 Figure 43: Empirical Results of Customer Performance Measurement in Companies............ 61 Figure 44: Fuzzy Methods of Cluster Analysis ........................................................................ 64 Figure 45: Sharp (a) and Fuzzy (b) Customer Segments ........................................................ 65 Figure 46: Methods of Customer Segmentation ...................................................................... 66 Figure 47: Information Dashboard of Relevant Customer Data............................................... 67 Figure 48: Context of Fuzzy Customer Segmentation............................................................. 68 Figure 49: Indicators of the CRM Success Chain for Fuzzy Customer Segmentation ............ 69 Figure 50: Driving the CRM Success Chain by Optimising Fuzzy Classified Portfolios .......... 69 Figure 51: Fuzzy Cost-Benefit Analysis (a) and Portfolio of Customer Orientation (b)............ 70 Figure 52: Examples of Fuzzy Classified Customer Satisfaction Portfolios............................. 72 Figure 53: Loyalty Ladder ........................................................................................................ 73 Figure 54: Examples of Fuzzy Classified Customer Loyalty Portfolios.................................... 74 Figure 55: Determinants of Customer Retention ..................................................................... 75 Figure 56: Controlling Level and Indicators of Customer Retention ........................................ 76 Figure 57: Fuzzy Classified Portfolios of Customer Retention Indicators ................................ 77 Figure 58: Examples of Fuzzy Classified Repurchase Portfolios ............................................ 78 Figure 59: Examples of Fuzzy Classified Add-on Selling Portfolios ........................................ 79 Figure 60: Crisp (a) and Fuzzy (b) Choice............................................................................... 80 Figure 61: Examples of Fuzzy Classified Share of Wallet Portfolios ....................................... 80 Figure 62: Examples of Fuzzy Classified Turnover Portfolios ................................................. 81 Figure 63: Customer Contribution Margin Accounting............................................................. 82 Figure 64: Fuzzy Classified Customer Contribution Margins Portfolios................................... 83 Figure 65: Fuzzy Classification of Customer Profitability......................................................... 84 Figure 66: Customer Growth Strategies .................................................................................. 85 Figure 67: Examples of Fuzzy Classified Customer Equity Portfolios ..................................... 86 Figure 68: Fuzzy Classified Customer Satisfaction/Equity Portfolio ........................................ 87 Figure 69: Three-Dimensional Fuzzy Classification of Customer Equity ................................. 89 Figure 70: Fuzzy Classified Customer (a) and Prospect (b) Lifetime Value Portfolios ............ 89

List of Figures

- VII -

Figure 71: Sharp (a) and Fuzzy Classified (b) Customer Equity Pyramid ............................... 90 Figure 72: Sharp Market Segmentation................................................................................... 91 Figure 73: Basic Market-Preferences Patterns........................................................................ 92 Figure 74: Fuzzy Market Segmentation of Income and Age.................................................... 92 Figure 75: Fuzzy Market Segments and Strategies................................................................. 93 Figure 76: Discriminant Function and Type I and II Errors ...................................................... 96 Figure 77: Architecture of a Neural Network for Credit Rating................................................. 97 Figure 78: Discriminant Functions in Discriminant Analysis and ANN..................................... 97 Figure 79: Hierarchy of Creditworthiness with Weights δ and Parameters γ........................... 99 Figure 80: Credit Rating Hierarchy with the Degree of Importance gi of each Criterion ........ 100 Figure 81: fCQL as a Method of Artificial Intelligence............................................................ 101 Figure 82: Practice-Related Example of a Hierarchy of Creditworthiness ............................. 102 Figure 83: Examples of a Qualitative and a Quantitative Attribute of Fuzzy Credit Scoring ..102 Figure 84: Hierarchical Fuzzy Classification of Creditworthiness .......................................... 103 Figure 85: Thee-Dimensional Sharp (a) and Fuzzy (b) Credit Rating.................................... 105 Figure 86: Promising Management Tools, Methods and Concepts for Fuzzy Classification .109 Figure 87: Fuzzy Classified Customer Portfolio (a) and Fuzzy ABC Analysis (b) .................. 110 Figure 88: Tools and Indicators for Customer Performance Measurement ........................... 113 Figure 89: The Main Challenges of Marketing Controlling in Practice ................................... 116

- VIII -

List of Tables

Table 1: Research Questions and Objectives ........................................................................ 4 Table 2: Selected Indicators for Customer Attractiveness and Competitive Position ............. 9 Table 3: Absolute and Normalised Membership Degress of Customer Smith...................... 12 Table 4: Membership Degress of the Customers ................................................................. 14 Table 5: Basic Scheme of SQL and fCQL ............................................................................ 17 Table 6: Criteria for Assessing Industry Attractiveness and Competitive Strength............... 30 Table 7: Sharp ABC Analysis ............................................................................................... 35 Table 8: Fuzzy ABC Analysis ............................................................................................... 36 Table 9: Example of the RFM Method with Sharp Classes .................................................. 39 Table 10: RFM Method: Definition of Classes and Scores..................................................... 40 Table 11: Sharp RFM Scoring of Customers.......................................................................... 41 Table 12: Fuzzy RFM Scoring of Customers.......................................................................... 42 Table 13: Mass vs. One-to-One Marketing and Applications for Fuzzy Classification ........... 48 Table 14: Drivers of Customer Value and Satisfaction........................................................... 71 Table 15: Determinants and Indicators of Customer Equity ................................................... 88 Table 16: Interest Rates for Different Loan Categories ........................................................ 105 Table 17: Sharp Classification of the Loan Applicants ......................................................... 105 Table 18: Results of Research Question (RQ) 1 .................................................................. 109 Table 19: Results of Research Question 2........................................................................... 111 Table 20: Results of Research Question 3........................................................................... 111 Table 21: Results of Research Question 4........................................................................... 112 Table 22: Results of Research Question 5........................................................................... 112 Table 23: Results of Research Question 6........................................................................... 114 Table 24: Results of Research Question 7........................................................................... 114

- IX -

List of Abbreviations

# Number aCRM analytical Customer Relationship Management AI Artificial Intelligence ANN Artificial Neural Networks BCG Boston Consulting Group BE Balance Error BP Balanced Portfolio BPR Business Process Re-Engineering BSC Balanced Scorecard C Class CAS Computer Aided Selling CCI Customer Cooperation Indicator CCO Chief Customer Officer CII Customer Investment Indicator CInfI Customer Information Indicator CIM Computer Integrated Manufacturing CLV Customer Lifetime Value CP Customer Performance CPI Customer Performance Indicator CPM Customer Performance Measurement CPMS Customer Performance Measurement System CPIP Customer Performance Indicator for Revenue and Profitability CR Customer Relation CRA Customer Relationship Analytics CRC Customer Relationship Communication CReI Customer Recommendation Indicator CRI Customer Relationship Indicator CRM Customer Relationship Management CRO Customer Relationship Operations Cu. Customer DB Database DBMS Database Management System DWH(S) Data Warehouse (System) e electronic EDGE Enhanced Data rates for → GSM Evolution EDI Electronic Data Interface Ed(s). Editor(s) EGPRS Enhanced GPRS (→ GPRS plus → EDGE) ERP Enterprise Ressource Planning fc fuzzy classification FCM fuzzy-C-Means (algorithm) fCMT fuzzy Classification Management Tools

List of Abbreviations

- X -

fCQL fuzzy Classification Query Language FMLE Fuzzy-Maximum-Likelihood-Estimation (algorithm) GPRS General Packet Radio Service GSM Global System for Mobile Communication HSDPA High Speed Downlink Packed Access I Indicator ICT Information and Communication Technology IM Information Management IS Information System IT Information Technology KAM Key Account Management KDD Knowledge Discovery in Databases KCPI Key Customer Performance Indicators KPI Key Performance Indicators KSF Key Success Factor L Level MD Membership Degrees MIS Management Information System MOA Market Opportunity Analysis No. Number OLAP On-Line Analytical Processing p(p). page(s) PDA Personal Digital Assistant PM Performance Measurement PMS Performance Measurement System R&D Research & Development RDBMS Relational Database Management System RFM Recency, Frequency, Monetary value ROC(I) Return on Customer (Investment) ROI Return on Investment ROM(I) Return on Marketing (Investment) ROQ Return on Quality ROR Return on Relationship ROS Return on Sales RQ Research Question SCM Supply Chain Management SFA Sales Force Automation SME Small and Medium Enterprises SWOT Strengths, Weaknesses, Opportunities, Threats sc sharp classification SBF Strategic Business Field SBU Strategic Business Units SQL Structured Query Language TQM Total Quality Management UMTS Universal Mobile Telecommunications System Vol. Volume WLAN Wireless Local Area Network

- XI -

Acknowledgement

Firstly, I thank Nicolas Werro for asking me to write about fuzzy classification and for the ex-

cellent assistance. He gave me the opportunity for a very interesting and exciting trip into

new worlds.

In addition, I want to thank Prof. Dr. Andreas Meier and Prof. Dr. Maurizio Vanetti that this

interdisciplinary project could be realised.

I am particularly grateful to my parents, Gabriela, Beatus and Jürg, for supporting me in all

the years. They made possible, what was and is so important to me.

I am also thankful for all the interesting discussions and the good advice of Florian Schramm

and Martin Zöller, and for the corrections of Tau Kevin Musa.

This thesis is dedicated to Ela, who supported and loved me so much in the last three years

– the best ones of my life.

- 1 -

Chapter 1

Introduction

Chapter 1: Introduction

- 2 -

1.1 Motivation

Since Zadeh first published the article “Fuzzy Sets” in the Journal “Information and Control” in

1965, much scientific research has been done in the field of fuzzy control over all the years.

In basic research, many publications have been written on fuzzy logic, fuzzy sets or on fuzzy

classification, on different mathematical definitions of the fuzzy classification approach, on its

implementations in information systems and on diverse applications in the field of engineering.

In fact, fuzzy logic and fuzzy classification is well known in many rather technical disciplines

like electronics or engineering, but also in mathematics, statistics, informatics and data mining.

In contrast, in marketing and business management fuzzy classification is still largely un-

known and rarely used, in both theory and practice. This gap motivated to write this master

thesis about the potential and benefit of fuzzy classification in business activities.

Consequently, following initial questions raised, which will be discussed in this work in detail

and summarised in Chapter 7: there exists an approach of fuzzy classification and the fuzzy

Classification Query Language (fCQL). However,

‘where’ can fuzzy classification be used in business management?

‘How’ could fuzzy classification be used?

‘What for’ can fuzzy classification be used?

‘Why’ should fuzzy classification be used?

First of all, one management field, which seems to be promising for fuzzy classification, is

Customer Relationship Management (CRM).

Most researchers and managers have recognised that customers are the most valuable and

scarcest assets of a company. As a result, CRM has become very important in the last years.

Figure 1: Application of Fuzzy Classification to Popular Management Tools

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Usage of tool (in how many of the asked companies a management tool was used)

Price optimization models

Open market innovation Mass customisation

Ave

rage

ove

rall

satis

fact

ion

with

a to

ol

Strategic planning

CRM Outsourcing

Mission and vision statement

Core competencies

Customer segmentation

Strategic alliances

Benchmarking

Growth strategies (customer acquisition)

Business process reengineering

TQM

Change management

Supply chain management

Knowledge management

Scenario and contingency planning

Activity-basedmanagement

Economical value-added analysis

Loyality management

Six sigma

Offshoring

Balanced scorecard

Scale of satisfaction: 1/2: extremely/somewhat dissatisfied; 3: neither dis-/ nor satisfied; 4/5: somewhat/extremely satisfied; Source data: [Bain & Company 2005, p.13]

Promising management tools and fields for fuzzy classification

4.2

4.1

4.0 3.9 3.8

3.7

3.6


- 3 -

An international study of [Bain & Company 2005], shown in Figure 1, confirms that CRM was

applied in 75% of all asked companies and is the second most important management tool in

business practice, behind strategic planning (79%) and before customer segmentation (72%)

and performance measurement (with the balanced scorecard: 57%). Obviously, most compa-

nies were somewhat or very satisfied with the tools. In addition, the management tools seem

to be promising for the fuzzy classification approach, as reasoned in the problem statement.

1.2 Problem Statement

This thesis will discuss several research questions and problems in the following domains,

‘where’ fuzzy classification can be used:

Fuzzy classification has not often been applied to CRM, although it seems to be suited to

improve CRM. As a result, a first research question has to be answered: ‘where’, in which

CRM fields and processes, ‘how’ and ‘why’ could fuzzy classification improve CRM?

To analyse and control marketing or CRM, management needs adequate methods, instru-

ment or tools to evaluate customers. However, what are widely used management tools

and methods in business practice suited for fuzzy classification? The thesis will explain,

‘how’ and ‘why’ fuzzy classification can be applied to such management tools.

To manage customers according to their importance for the firm, CRM and marketing need

a measurement system to analyse and evaluate the performance of customers. Defining

another research question and creating a new term: what is the benefit of fuzzy classifica-

tion in customer performance measurement? Although many authors of literature on

CRM, marketing, accounting and information management emphasise the importance of

measuring customer relationships and customer performance, there exist surprisingly few

reviews and little literature about the measurement of customer performance.

To measure customer performance, CRM requires indicators. However, what kind of indica-

tors does CRM need? What are important customer performance indicators for customer

performance measurement? The thesis will work out a concept, ‘how’ customer perform-

ance indicators could be applied, and ‘why’ and ‘what for’ they are relevant.

Customer segmentation, an important task of analytical CRM and data mining, seems to

be particularly interesting for fuzzy classification (‘what for’), because it is evidently danger-

ous to label and classify customers sharply just as "good" (profitable) or "bad" (unprofitable).

However, this work argues, ‘how’ and ‘why’ fuzzy classification can be used for an exact,

fair and enhanced customer segmentation.

With a specific problem of customer segmentation, each loan officer of a bank is confronted:

when is a loan applicant creditworthy, and when is he not? Answering this last research

question, it will be shown, ‘how’ and ‘why’ fuzzy classification can be used for credit rating.


- 4 -

1.3 Objectives

This thesis tries to answer seven Research Questions (RQ) and their objectives, mentioned

in the problem statement and summarised in Table 1. Answering the research questions and

discussing all the points on the right hand side of Table 1, new theoretical insights should be

gained about the benefit of the application of the fuzzy classification approach in customer

relationship management. The main aim of this master thesis is to show the possibilities for an

improved evaluation and management of customers using fuzzy classification.

Table 1: Research Questions and Objectives

# Research Questions (RQ): Objectives: analysis, discussion and evaluation of the …

1 What are potential fields and topics for business applications for fuzzy classification in marketing?

… possible spectrum of marketing concepts and management tools for the applications of fuzzy classification: overview of different promising marketing fields and concepts for applications of fuzzy classification overview of different potential fuzzy management tools and methods discussion of existing literature on marketing and fuzzy classification

2 What are potential management tools and methods for fuzzy classification?

… application of the fuzzy classification approach to different management tools and instruments of analysis and control: definition and advantages of fuzzy portfolio analysis definition and advantages of fuzzy SWOT analysis definition and advantages of fuzzy ABC analysis definition and advantages of fuzzy scoring methods and RFM method

3 What are potential fields, processes and instruments for fuzzy classification in CRM?

… application of fuzzy classification to fields, tasks, processes and instru-ments of marketing and Customer Relationship Management (CRM): discussion of promising marketing concepts for fuzzy classification, promising fields for fuzzy classification in customer management/CRM definition, processes, architecture, objectives and key points of CRM definition of a small CRM success chain

4 What are the benefits of fuzzy classification in customer performance measurement?

… application of the fuzzy classification approach to customer performance measurement and management: definition of the term customer performance definition of the term customer performance measurement characteristics and processes of customer performance measurement customer performance measurement in business practice

5 What are important customer performance indicators for customer performance measurement?

… customer performance indicators and key customer performance indicators for holistic customer performance measurement: definition of the term customer performance indicator collection, discussion and categorisation of a comprehensive number of customer performance indicators mapping of customer performance indicators to a big CRM success chain

6 How can customers be segmented fuzzily?

… fuzzy segmentation of customers into fuzzy customer segments using important customer performance indicators: definition of the term fuzzy customer segmentation methods and context of fuzzy customer evaluation and segmentation fuzzy customer segmentation with 12 Key Customer Performance Indi-cators (KCPIs) of the CRM success chain using fuzzy portfolio analysis

7 What are the benefits of the fuzzy classification approach in credit rating?

… fuzzy credit rating approach for the evaluation of the creditworthiness of private loan applicants: discussion and disadvantages of methods of sharp credit rating discussion and advantages of methods of fuzzy credit rating example of a hierarchical fuzzy classification for credit scoring calculation of personalised interest rates using fuzzy classification

The following outline shows, how the thesis is structured and organised in order to work out a

clear concept by discussing the seven research questions.


- 5 -

1.4 Outline of the Thesis

The research questions indicate that the objects of research of this work are intersections of

different fields in information management: the fuzzy classification approach with the fuzzy

Classification Query Language (fCQL) is actually a topic of computer science and information

technology. However, this thesis discusses different business applications for fuzzy classifica-

tion and is therefore an approach of business management. The main issues of the thesis,

customer performance measurement and customer segmentation (research questions 4 to 6),

belong both to marketing and managerial accounting. In addition, research question 7, the

task of credit rating, can be assigned to finance (compare Figure 2).

Figure 2: Theoretical Classification of the Master Thesis

The master thesis has the following structure:

The introducing Chapter 2 will summarise the essential ideas, logic, concepts and the

model of fuzzy classification and the fuzzy classification Query Language (fCQL).

Chapter 3 discusses different potential fields for fuzzy classification (RQ 1) and different

fuzzy classification management tools (RQ 2): fuzzy portfolio analysis, fuzzy SWOT

analysis, fuzzy ABC analysis and fuzzy scoring methods.

Chapter 4 deals with the applications of fuzzy classification within analytical CRM (RQ 3),

with the conception of customer performance measurement (RQ 4), and with customer

performance indicators (RQ 5).

In Chapter 5, the developed concept of customer performance measurement and different

important indicators are applied to fuzzy customer segmentation (RQ 6).

Methods of sharp and fuzzy credit rating (RQ 7) are discussed in Chapter 6.

In Chapter 7, conclusion, all findings are summarised. Critical remarks and an outlook on

further research questions about business applications for fuzzy classification will round off

the thesis.

Marketing

Accounting

Finance

Information management

Fuzzy classification

Credit rating

Obj

ects

of

rese

arch

Customer Relationship Management (CRM)

Customer performance measurement



- 6 -

Figure 3 shows the structure of the thesis, including the seven Research Questions (RQ).

Figure 3: Structure of the Master Thesis

2. Fuzzy Classification

1. Introduction

1.2 Problem Statement

1.3 Objectives

1.4 Outline

2.1 The Approach of Fuzzy Classification

4. Analytical Customer Relationship Management

3. Fuzzy Classification Management Tools

3.2 Fuzzy Portfolio Analysis

1.1 Motivation

3.4 Fuzzy ABC Analysis

4.1 Customer Relationship Management (CRM)

4.2 Customer Performance Measurement

2.2 Fuzzy Classification Query Language (fCQL)

3.1 Potential Business Applications for Fuzzy Classification

3.3 Fuzzy SWOT Analysis

3.5 Fuzzy Scoring Methods

Sha

rp c

lass

ifica

tion

and

disa

dvan

tage

s

Fuzz

y cl

assi

ficat

ion

and

adva

ntag

es

RQ 1

RQ 2

RQ 3

RQ 4

6. Fuzzy Credit Rating

6.1 Methods of Sharp Credit Rating

6.2 Methods of Fuzzy Credit Rating

7. Conclusion

RQ 7

5. Fuzzy Customer Segmentation

4.3 Customer Performance Indicators

5.1 Fuzzy Customer Segmentation with Important Indicators

RQ 5

RQ 6

5.2 Fuzzy Market Segmentation

- 7 -

Chapter 2

Fuzzy Classification

Chapter 2: Fuzzy Classification

- 8 -

2.1 The Approach of Fuzzy Classification 2.1.1 Classification as a Database Schema Extension

This technical chapter recapitulates the main findings and research on fuzzy classification of

the Information System Research Group at the Department of Informatics at the University

of Fribourg (Switzerland). This chapter is based on the research of [Schindler 1998a, Meier et

al. 2001, 2003, 2005, Werro 2005, Werro et al. 2005a/b, 2006].

However, the concepts are explained using a new example of fuzzy classification, a customer

attractiveness/competitive position portfolio. Figure 4 shows the structure of the chapter.

Figure 4: Structure of Chapter 2: Fuzzy Classification To define classes in the relational database schema, a context model proposed by [Chen

1998] was extended [Meier et al. 2001]: To every attribute Aj, defined by a domain D(Aj), a

context K(Aj) is added. A context K(Aj) is a partition of D(Aj) into equivalence classes. A rela-

tional database schema with contexts R(A, K) is then the set A = (A1,…, An) of attributes with

associated contexts K = (K1(Aj),…,Kn(An)) [Shenoi 1995].

Classification as a Database Schema Extension

Sharp vs. Fuzzy classification

Fuzzy Classification with Linguistic Variables

Aggregation Operator

Multidimensional Fuzzy Classification

Subsections Keywords References

Dynamic Fuzzy Classification

New

exa

mpl

e: c

usto

mer

attr

activ

enes

s/co

mpe

titiv

e po

sitio

n po

rtfol

io

Fuzzy Classification Query Examples

Architecture of the fCQL Toolkit

Advantages of Fuzzy Classification and fCQL

Fuzzy Classification Query Language (fCQL)

Attribute, domain, context, database scheme, equivalence class; Sharp classification and fuzzy classification; Linguistic variables, verbal terms, membership degree, continuous and discrete member functions; Aggregation Operator, compensatory, t-norms, t-conorms Multidimensional fuzzy classification, hierarchical fuzzy classification, decomposition principle; Fuzzy classification over time, monitoring, trigger mechanism; fCQL, relational database, fuzzy queries; SQL, fCQL/SQL basic scheme, fCQL syntax; fCQL toolkit, fCQL tool’s architecture, fCQL interpreter; Improved classification, reduction of complexity, extraction of hidden data, no migration, easy to use, etc.

[Che

n 19

98]

[She

noi 1

995]

[M

eier

200

3a]

[Zim

mer

man

n 19

92],

[Zim

mer

man

n an

d Zy

sno

1980

]

Sch

indl

er 1

998a

]

[

Mei

er e

t al.

2005

a/b]

[Wer

ro e

t al.

2005

]

[ W

erro

et a

l. 20

06]

Sec

tion

2.2

fCQ

L

Sec

tion

2.1:

App

roac

h of

Fuz

zy C

lass

ifica

tion


- 9 -

In this chapter, the following example of CRM with two attributes is considered: customers

who buy the company’s products can be evaluated by ‘customer attractiveness’ (attribute A1),

which is measured by an index, and ‘competitive position’ (A2). ‘Customer attractiveness’ and

‘competitive position’ can be specified by one or several indicators (combined and weighted by

a scoring model or decomposed by a hierarchical classification) shown in Table 2. For expla-

nations, details and more customer indicators see Appendix 4 (pages 136ff).

Table 2: Selected Indicators for Customer Attractiveness and Competitive Position

Customer attractiveness (A1) Competitive position (A2) Customer turnover or sales (I 31-I 34) Customer contribution margins I – IV (I 70-I 74) Customer gross/net profit (I 75,I 76), profitability (I 78) Customer equity (I 79), Customer Lifetime Value (I 80) Growth of turnover (I 35) or profit (I 77); potential (I 83) Probability of repurchases (I 57), price sensitivity (I 60) Customer’s product mix at the company/competitor (I 58) Punctuality of payment (I 63), creditworthiness (I 68) Number of recommendations (I 152), cooperation (I 171)

Customer penetration (I 38) Share of wallet (I 39-I 41) Market share of customer (I 42) Cross- and up-selling (I 50- I 54) Duration of customer relationship (I 147) Satisfaction (I 126), commitment (I 131) Customer loyalty (I 134), retention (I 137) Image of company (I 115), products (I 116) Competitive advantages

The pertinent contexts K(Aj) of the two qualifying attributes can be defined as follows:

Customer attractiveness (A1): The attribute domain D(A1) of the index ‘customer attrac-

tiveness’ is defined by [0, 100] and is divided into the two equivalence classes [0, 49] for

‘unpromising’ customer attractiveness and [50, 100] for ‘promising’ attractiveness.

Competitive position (A2): The domain D(A2) is {very bad, bad, insufficient, sufficient,

good, excellent} and has two equivalence classes: {very bad, bad, insufficient} for a ‘weak’

competitive position, and {sufficient, good, excellent} for a ‘strong’ one.

The definition of the equivalence classes of the two attributes ‘customer attractiveness’ and

‘competitive position’ determines a two-dimensional classification space shown in Figure 5.

The four resulting classes C1 to C4 could be characterised as ‘star customers’ (C1) with the

strategy to maintain, as ‘development customers’ (C2) in which has to be invested, ‘absorption

customers’ (C3) are to skim, and in ‘renunciation customers’ (C4) a firm should not invest.

Customer attractiveness 100

50

C2)

Development customers (to invest)

C1)

Star customers (to maintain)

49

0

C4)

Renunciation customers (not to invest)

C3)

Absorption customers (to skim)

very bad bad insufficient sufficient good excellent Competitive position

Figure 5: Classification Space defined by Customer Attractiveness and Competitive Position


- 10 -

This portfolio, originally described by [Link and Hildebrand 1993] and discussed by [Homburg

and Krohmer 2006], is adapted from the market growth/market share portfolio of the Boston

Consulting Group (with the classes C1: ‘stars’, C2: ‘question marks’, C3: ‘cash cows’ and C4:

‘poor dogs’) and from the industry attractiveness/competitive strength portfolio of McKinsey

and General Electrics (see Section 3.2 Fuzzy Portfolio Analysis).

2.1.2 Fuzzy Classification with Linguistic Variables

In this section, the model will be extended by defining a fuzzy classification database scheme

in order to determine the customer’s membership degrees to the different classes.

To derive fuzzy classes from sharp contexts, the attributes are considered as linguistic vari-

ables, and verbal terms are assigned to each equivalence class [Zimmermann 1992]. With the

help of linguistic variables, that means words or word combinations, the equivalence classes

of the attributes can be described more intuitively. In the example, the linguistic variable ‘cus-

tomer attractiveness’ is described by the terms ‘unpromising’ and ‘promising’. The linguistic

variable, ‘competitive position’, is divided into the terms ‘weak’ and ‘strong’ (see Figure 6).

Figure 6: Concept of Linguistic Variables

Every term of the linguistic variable represents a fuzzy set. Each fuzzy set is determined by a membership function (μ) over the whole domain of the corresponding attribute. The attribute

‘customer attractiveness’ contains numerical values in the interval [0,100]. Consequently, the

membership functions μunpromising and μpromising are continuous functions. In contrast, the attribute

‘competitive position’ does not hold numerical values, but general terms. In this case, the

terms of the linguistic variable ‘weak’ and ‘strong’ are associated to discrete functions, i. e.

each term corresponds to a discrete value. Using the context model, linguistic variables and

membership functions, the classification space becomes fuzzy. This fuzzy partition has an

important outcome, it implies the disappearance of the classes’ sharp borders, that means

there are continuous transitions between the different classes [Werro et al. 2006].

0 49 50 100

Customer attractiveness Competitive position

very bad bad insufficient sufficient good excellent

[0,..., 49] [50,..., 100] [very bad, bad, insufficient] [sufficient, good, excellent]

Linguistic variable(attribute)

Term

Domain

Context

Equivalence class Equivalence class

unpromising weak promising strong



- 11 -

Figure 7: Fuzzy Classification with Membership Functions

In terms of colour: at the transitions of the classes, the four colours of the classes (in Figure 7),

light and dark green, and light and dark blue, become blurred, melt or flow together.

Classified fuzzily, a customer can belong to more than one class at the same time and his

membership degrees to the different classes can be calculated using an aggregation operator,

for instance the γ-operator discussed in the following subsection.

The sizes of the circles in Figure 7 are assumed to provide additional information about the

customer’s amount of turnover of the last year, since it is common in portfolio analysis to sym-

bolise the importance of a business unit (here: of a customer) by the diameter of the circle.

However, the size of circle has nothing to do with the fuzzy classification of the customer itself.

2.1.3 Aggregation Operator

The membership degree M(Oi│Ck) of an object Oi (of a customer in the example) in class Ck

can be calculated by an aggregation over all terms of the linguistic variables that define the

class. Class C1 in the discussed example is described by the terms ‘promising’ and ‘strong’.

The membership grade of class C1 (star customers) is therefore a conjunction of the corre-

sponding values of the membership functions μpromising and μstrong. Analogically, C2 is defined by

the membership functions μpromising and μweak, C3 (absorption customers) by μunpromising and

μstrong and C4 (renunciation customers) by μunpromising and μweak.

C1)

C4)

.

very bad bad insufficient sufficient good excellent

0

49

50

10

0

C2)

C3)

μ strong μ weak

0

0.2

0.4

0.6

0.8

1

D(Customer attractiveness)

D(Competitive position)

μ un

prom

isin

g μ

prom

isin

g

0 1

Ford

Smith

Brown

Miller

31.5% C1: Star customer

24.5% C2: Development customer

18.9%: C4 Renunciation customer

100% C1: Star customer

100% C4: Renunciation customer

26.4% C3: Absorption customer

22.9% C2: Development

customer 33.7% C4: Renunciation customer

17% C1: Star customer

25.1% C3: Absorption customer

Sharply classified customer turnover of the last year

Fuzzily classified customer turnover of the last year

.

.

.

un

prom

isin

g

p

rom

isin

g C2)

Development customers (to invest)

C1)

Star customers

(to maintain)

C4)

Renunciation customers

(not to invest)

C3)

Absorption customers (to skim )

D(Customer attractiveness)

D(C

ompe

titiv

e p

ositi

on)

weak strong

Sharp classification

Brown

Miller

Smith

Ford

. .

.

.

0.65 0.35


- 12 -

The used operator of the aggregation is the γ-operator, the “compensatory and”, which was

suggested and empirically tested by [Zimmermann and Zysno 1980, Zimmermann 1992]:

(γ-operator)

The γ-operator is composed by the algebraic product operator, a t-norm and its counterpart,

a t-conorm (compare Figure 8). The t-norms and t-conorms are non-compensatory opera-

tions, what means that there is no compensatory effect between the considered elements. In

contrast, the averaging operators have a compensation mechanism to reflect the reasoning

of humans, who intuitively weigh up elements (for details see [Werro 2007, p. 15]).

Figure 8: t-Norms, t-Conorms and Averaging Operator

In all examples of fuzzy classification in this thesis it is assumed that gamma is 0.5 (γ = 0.5;

red curve in Figure 8), That means that the γ-operator is .

The membership degrees of all classified objects Oi (customers) to each class Ck can be cal-

culated be the γ-operator using the fCQL toolkit or the Microsoft® Excel file “GammaOperator”

in Appendix 1 (see p. 133). For customer Smith in Figure 7, for instance, the membership

degrees are calculated in Table 3. To receive the relative membership degrees Mnorm(Oi│Ck),

the absolute degrees M(Oi│Ck) have to be normalised, because the membership degree of

an object to a class depends on all other classes and therefore indirectly on the number of

classes k (see [Krishnapuram and Keller 1993, Schindler 1998a, p. 165]).

Table 3: Absolute and Normalised Membership Degress of Customer Smith

( ) 1γ0 , ,(x)μ - 1 1(x)μ )x(μγm

1ii

γ)(1m

1 iiAi

≤≤∈⎟⎠

⎞⎜⎝

⎛−⎟

⎠

⎞⎜⎝

⎛= ∏∏

=

−

=

Xx

Corresponding membership functions of each class

Membership degrees of attributes Class

(C) Customer attractiveness

Competitive position

Customer attractiveness


Absolute membership degrees M(OSmith│Ck):

Normalised membership degrees Mnorm(OSmith│Ck):

C1 μpromising μstrong 0.35 0.40 (.35·.40).5 ·(1 - ((1-.35)·(1-.40))).5 = 0.37417 · 0.78102 = 0.29223 0.29223 / 1.71879 = 0.17002

C2 μpromising μweak 0.35 0.60 (.35·.60).5·(1 - ((1-.35)·(1-.60))).5 = 0.45826 · 0.86023 = 0.39421 0.39421 / 1.71879 = 0.22935

C3 μunpromising μstrong 0.65 0.40 (.65·.40).5·(1 - ((1-.65)·(1-.40))).5 = 0.50990 · 0.88882 = 0.45321 0.45321 / 1.71879 = 0.26368

C4 μunpromising μweak 0.65 0.60 (.65·.60).5·(1 - ((1-.65)·(1-.60))).5 = 0.62450 · 0.92736 = 0.57914 0.57914 / 1.71879 = 0.33695

Total 1.71879 1

( ) ( )( )∑=

k

n 1kiki COMCOM( )

0.5m

1ii

)5.0(1m

1 iiA (x)μ - 1 1(x)μ )x(μ

i⎟⎠

⎞⎜⎝

⎛−⎟

⎠

⎞⎜⎝

⎛= ∏∏

=

−

=

( )0.5m

1ii

)5.0(1m

1 iiA (x)μ - 1 1(x)μ )x(μ

i⎟⎠

⎞⎜⎝

⎛−⎟

⎠

⎞⎜⎝

⎛= ∏∏

=

−

=

Source: adapted from [Werro 2007, p. 15]

Averaging operators

t-norms

Averaging operators

μ

xMo

1 t-conorms

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101x

Mu

A B Gamma = 0 Gamma = 0.2 Gamma = 0.4Gamma = 0.5 Gamma = 0.6 Gamma = 0.8 Gamma = 1μ

x


- 13 -

Classified sharply (compare Figure 7), customer Smith belongs to one class only, 100% to

C4, and he is managed as a renunciation customer not to invest in, like customer Ford, who

has much lower values. This is arbitrary, not precise and not fair since Smith has nearly the

same values as Brown, who is classified in class C1 as a star customer to maintain. However,

Brown’s attractiveness and competitive position is not as high as the ‘real star customer’ Miller

ones and still should be improved. These two types of misclassifications are typical for sharp

classifications: elements with very similar values can be classified in total different classes,

and elements, which have very different values, may be classified in the same class. There

often exist high discrepancies between the classes and within a class.

With fuzzy classification, this does not happen and the misclassification problem is solved.

Classified fuzzily, Smith is no longer discriminated and belongs partly to all classes at same

time, as calculated in Table 3 and shown in Figure 7 (17% to C1, 22.9% to C2, 26.4% to C3

and 33.7% to C4). Brown also belongs simultaneously to four classes (31.5% to C1, 24.5% to

C2, 25.1% to C3, 18.9% to C4). With fuzzy classification both customer Smith and Brown are

not separated anymore and can be managed according to their real degree of ‘customer at-

tractiveness’ and ‘competitive position’. However, Miller (C1) and Ford (C4) still belong to one

class only.

A class of the fuzzy classification can also be considered as a fuzzy customer segment. For

instance, all customers with absolute membership degrees between 0 and 1 to C1 ‘stars’ (in

Figure 7) can be considered as a fuzzy segment. Consequently, the fuzzy segment C1 con-

sists of the Miller (with a absolute membership degree 100% to C1), Brown (54.4% to C1) and

Smith (29.2% to C1). From the management point of view, this raises important questions:

how much should be invested in Brown or Smith, who are positioned in the middle of the ma-

trix and belong partially to different segments (i.e. classes) at the same time?

One answer could be that managers have to define customer strategies more specifically,

differentiated, and, most important, more individually and personally.

2.1.4 Multidimensional Fuzzy Classification

Fuzzy classification as a multidimensional analysis and classification method is not limited to

two dimensions. Several attributes or criteria can be considered at the same time. In Figure

9a, a third dimension, the future-oriented attribute ‘customer potential’, with the two terms ‘low

potential’ and ‘high potential’, is added to the discussed example. Classified sharply (Figure

9a), Brown and Miller belong to same class (C5), although their positions are very different.

Brown and Smith, however, are almost in the same position, but belong to different classes,

Brown to C5 and Smith to C3. Classified fuzzily (Figure 9b), the eight classes’ sharp borders

disappear and the three dimensional classification space becomes fuzzy.


- 14 -

Figure 9: Three-Dimensional Sharp (a) and Fuzzy (b) Classification

Brown and Smith belong now to eight different classes, with following absolute M(Oi│Ck) and

normalised Mnorm(Oi│Ck) membership degrees in Table 4. Calculation example for Smith to C1: M(OSmith│C1) = (0.43·0.56·0.68)0.5 · ((1 - ((1-0.43)·(1-0.56)·(1-0.68)))0.5 = 0.40465·0.95903 = 0.38808

Mnorm(OSmith│C1) = 0.38808 / 2.60418 = 0.14902 = 14.9% (for details see Appendix 1).

Table 4: Membership Degress of the Customers

M(Oi│Ck) Mnorm(Oi│Ck) Class Corresponding membership functions to each class Ford Smith Brown Miller Ford Smith Brown Miller

C1 μpromising μstrong μlow potential 0 0.38808 0.33409 0 0 0.14902 0.12810 0 C2 μpromising μweak μlow potential 0 0.33987 0.25146 0 0 0.13051 0.09641 0 C3 μunpromising μstrong μlow potential 0.6 0.45157 0.29126 0 0.42857 0.17340 0.11167 0 C4 μunpromising μweak μlow potential 0.8 0.39674 0.21637 0 0.57143 0.15235 0.08296 0 C5 μpromising μstrong μhigh potential 0 0.25281 0.45698 1 0 0.09708 0.17521 1 C6 μpromising μweak μhigh potential 0 0.21772 0.35045 0 0 0.08360 0.13437 0 C7 μunpromising μstrong μhigh potential 0 0.29833 0.40151 0 0 0.11456 0.15395 0 C8 μunpromising μweak μhigh potential 0 0.25906 0.30599 0 0 0.09948 0.11732 0

Total 1.4 2.60418 2.60811 1 1 1 1 1

The example of the three-dimensional fuzzy classification in Figure 9b will now be extended to

a hierarchical fuzzy classification. The decomposition principle facilitates the definition

and the optimisation of the classification while maintaining a small number of classes with a

proper semantic even if many attributes are taken into account (see [Werro et al. 2006]).

The three attributes ‘customer attractiveness’, ‘competitive position’ and ‘customer potential’,

which define the ‘customer equity’ on the top level of the hierarchy, can be decomposed as

shown in Figure 10a. The attribute ‘customer attractiveness’, for instance, is defined by three

other attributes (in Figure 10c): ‘RFM score’, ‘profit’, and ‘payment history’. The attribute ‘RFM

score’ (described in detail in Section 3.5 and shown in Figure 10d) again, consists of the at-

tributes ‘Recency’, ‘Frequency’ and ‘Monetary value’ of the customer’s repurchases.


Customer Potential


010

1

μ weak μ strong

μ pr

omis

ing

μ un

prom

isin

g

Ford

Smith Brown

μ low potential

C3) 42.9% C4) 57.1%

C5) 100%

μ high potential

C1) 12.8% C2) 9.6% C3) 11.2% C4) 8.3% C5) 17.5% C6) 13.4% C7) 15.4% C8) 11.7%

Miller

b) Three-Dimensional Fuzzy Classification

C1) 14.9% C2) 13.1% C3) 17.3% C4) 15.2% C5) 9.7% C6) 8.4% C7) 11.5% C8) 9.9%


CustomerPotential

high potential

low potential

prom

isin

g u

npro

mis

ing

strong weak


Miller C5) 100% Brown

C5) 100% Smith C3) 100% Ford

C4) 100%

a) Three-Dimensional Sharp Classification

Smith 0.57

0.44

0.56

0.32 0.68

0.43

0.38

0.62

0.56 0.44

0.36 0.64

0.36

0.64


- 15 -

Figure 10: Example of Hierarchical Multidimensional Fuzzy Classification

In comparison to sharp classifications, for fuzzy classifications fewer terms (equivalence

classes) have to be defined in order to describe each attribute. The fuzzy classification of the

attribute ‘profit’, for instance, needs two terms only: ‘low profit’ and ‘high profit’. If turnover is

classified sharply, much more terms (equivalence classes) have to be defined: e.g. ‘very low’,

‘low’, ‘medium’, ‘high’ and ‘very high’ turnover. However, the lowest hierarchical level of the

fuzzy classification in Figure 10a still consists of 27 (3·9) attributes and 54 terms. To reduce

complexity and receive a clear semantic, all groups of three attributes at the bottom-level

(e.g. ‘Recency’, ‘Frequency’ and ‘Monetary value’) have to be aggregated (to the term ‘RFM

score’). The ‘RFM score’, in turn, ‘profits’ and ‘payment history’ express a higher semantic

(‘customer attractiveness’) on the next level. Depending on the information demand, customer

performance can be analysed on different levels of the hierarchy from a bottom-up or from a

top-down approach. The combination of this decomposition principle with a scoring model (see

Section 3.5) is a promising approach to develop a comprehensive customer evaluation model

based on any customer criteria, which are important for a company.

010

μ weak μ strong

μ at

tract

ive

μ un

attra

ctiv

e

C1) C2)

C3) C4)

C5) C6)

C7) C8)

1


Cus

tom

er

pote

ntia

l


010

μ low profit μ high profit

μ hi

gh s

core

μ

low

sco

re

C1-1) C1-2)

C1-3) C1-4)

C1-5) C1-6)

C1-7) C1-8)

1

Profit

Pay

men

t hi

stor

y

RFM score

010

μ rare μ frequent

μ re

cent

μ

long

ago

μ low value

μ highvalue

C1-1-1) C1-1-2)

C1-1-3) C1-1-4)

C1-1-5) C1-1-6)

C1-1-7) C1-1-8)

1

Frequency

M

onet

ary

val

ue

Recency

Customer equity

Recency Frequency Monetary value

μ bad

μ good

μ low potential

μ highpotential

RFM score

Turnover product Turnover service Transaction costs

Profit

Punct. of payment Method of payment Outstanding bills

Payment history

Profits Turnover Cash flow

Growth of

Down-selling pot. Cross-selling pot. Up-selling pot.

Add-on selling

Information Cooperation Recommendations

Relationship

b) Fuzzy Classification of Customer Equity a) Hierarchical Fuzzy Classification of Customers

c) Fuzzy Classification of Customer Attractivenessd) Fuzzy Classification of RFM Score

Cu. penetration Share of wallet Cu. market share

Products Services Relationship

Satisfaction with

Loyalty Duration or CR Intention to switch

Retention


Customer potential


Penetration


- 16 -

2.1.5 Dynamic Fuzzy Classification

Dynamically, fuzzy classification enables a precise monitoring of the positions and evolutions

of a customer, a customer segment or of all customers in a portfolio (see [Werro et al. 2006]).

By comparing different values over time, it can be controlled, if customer performance (in

Figure 11a) is increasing, steady or decreasing. This facilitates, on the one hand, a com-

parison of the target and actual business results of a single customer or segment, and, on the

other hand, to react quickly and to launch adequate counter measures, if the performance of a

customer, of a segment or of the whole portfolio is decreasing rapidly.

The identification of important leading-indicators of customer performance, such as the num-

ber of orders (I 17), order quote (I 19), repurchase intentions (I 55), cross-buying intentions (I

54), intention to recommend (I 151), customer (dis)satisfaction (I 126), or the intention to

switch (I 141), enables the implementation of a trigger mechanism (red-flag-function) which

warns at an early stage, if a good customer (Smith in Figure 11) shows a churning behaviour.

Figure 11: Dynamic Fuzzy Classification and Implementation of a Trigger Mechanism

In the fictive example of Figure 11b, Smith suddenly was very dissatisfied with a product or the

company itself, therefore his intention to switch highly increased and his intention to repur-

chase or cross-buy from the company decreased. Brown’s number of orders, and later his

turnover, also decreased, when he found an option or substitute elsewhere. As a result, his

membership degrees to the "good classes" (C1, C2, C3) steadily decreased from the first term

in 2006 to the first term in 2007, and the membership degree to C4 increased accordingly.

However, by modelling different leading- and lagging-indicators of customer performance, a

trigger mechanism warns, if the membership degrees to different classes fall below a minimum

(or exceed a maximum) level. To stop or weaken the negative trend, adequate CRM actions

can be launched in order to increase customer satisfaction, loyalty or retention again.

Implementing a Trigger Mechanism C2

C1

C4

C3

μ strong

0 0

1

1

μ weak

μ un

prom

isin

g

μ

pro

mis

ing


Com

petit

ive

posi

tion

Ford

Smith

Miller 2006(1)

Brown

Trigger mechanism for churn management

Smith’s member-ship degrees

to the classes C1, C2, C3, C4

t

1

0 very

low

ve

ry h

igh

2006(1)

Smith’s leading-indicators:

2007(1)

satisfaction

Number of orders

Intention to switch

Intention to repur-chase/cross-buy

2006(2) 2007(1)

2006(2)

2006(1) 2006(2)

2007(1)

2006(1)

2006(2)

2007(1)

2006(1) 2006(2)

2007(1)

min

imum

leve

ls

Turnover in term (t)

max

imum

leve

l

b) a)


- 17 -

2.2 Fuzzy Classification Query Language (fCQL) 2.2.1 Introduction

The fuzzy Classification Query Language (fCQL) is a data analysis or a data mining tool,

which combines relational databases with fuzzy logic. This enables the use of numerical and

non-numerical values and allows the formulation of linguistic variables, which results in a more

human-oriented querying process. fCQL enables the formulation of unsharp queries on a lin-

guistic level, so the user does not need to deal with a fuzzy SQL or with fuzzy predicates,

which could lead to varying semantics and different interpretations of the original data collec-

tion [Finnerty and Shenoi 1993]. As a result, the user can easily formulate classification que-

ries as they are intuitive, i.e. the meaning of the queries is linguistically expressed.

2.2.2 Fuzzy Classification Query Examples

fCQL is an extension of the Structured Query Language (SQL), the international standard for

defining and query relational databases. Table 5 shows the comparison of the two languages.

Table 5: Basic Scheme of SQL and fCQL

SQL fCQL Select Attribute from Relation where Condition of Selection

classify Object from Relation with ClassificationCondition

Source: [Schindler 1998a]

As shown in Table 5, the select clause from the SQL schema is changed to classify, the

name of the object column to be fuzzily classified. The where clause turns to with, the

predicate for a classification. In the with clause, the user enters the predefined linguistic vari-

ables and their associated verbal terms. In combination with keywords, the user generates the

classification conditions.

After the definition of the fCQL syntax, following fuzzy classification query examples of the

discussed customer attractiveness/competitive position portfolio can be undertaken.

To query all customers of the customer portfolio, the fCQL query will be: classify customer from customer portfolio

In order to ask for new classes, a specific term of a linguistic variable can be queried.

For instance, the customer manager wants to know all unpromising customers of the customer

attractiveness/competitive position portfolio discussed in Subsection 2.1.2. He queries:


- 18 -

classify customer from customer portfolio with customer attractiveness is unpromising Now all membership degrees of the customer based on the function μunpromising are calculated.

If the user wants to know the promising and strong customers to invest, he queries:

classify customer from customer portfolio with customer attractiveness is promising and competitive position is strong Now he receives the membership degrees of all customers with a membership degree based

on the functions μpromising and μstrong. The query returns the predefined class C1 with the seman-

tic ‘star customers’. A simpler way to specify class C1 named ‘star customers’ is as follows:

classify customer from customer portfolio with class is star customers Particularly in complex databases, the utility of querying on linguistic variables becomes im-

portant. The ability of querying on linguistic variables can be considered as the slicing and

dicing operations on a fuzzy multidimensional classification space [Werro et al. 2005a].

2.2.3 Architecture of the fCQL Toolkit In this subsection, the architecture and the implementation of the fuzzy Classification Query

Language (fCQL) will be resumed. By extending the relational database scheme, meta-tables

( in Figure 12) are added to the Relational Database Management System (RDBMS; ).

Figure 12: Architecture of the fQCL Toolkit

Membership Function Editor

Definition and chart of discrete [a), b)] or continuous, i.e. linear [c), d)] or s-shaped [e), f)] membership functions.

User or application Data architect

SQL queries

Generated SQL queries

fCQL queries

Graphical interaction

Case 1

Case 2

Case 3

Source: adapted from [Werro et al. 2006, p.3]

Relational Database Management System (RDBMS)

Raw data Meta-tables

1

0

1

0

1

0

a) b)

c) d)

e) f)

Ser

ver

fCQL toolkit


- 19 -

These meta-tables contain the definition of the linguistic variables and terms, the description of

the classes and all the meta-information regarding the membership functions. The architecture

of the fCQL toolkit illustrates the interactions between the user ( ), the fCQL toolkit ( ) and

the RDBMS. The fCQL toolkit is an additional layer above the relational database system

[Werro et al. 2005b]. This particularity makes fCQL independent from underlying database

systems based on SQL. Consequently, fCQL can operate with every RDBMS without migrat-

ing data and the user can always query the database also with standard SQL queries ( ;

case 1 in Figure 12). In case 2, the user, or an application, can formulate fuzzy queries ( ) to

the fCQL toolkit. The query panel of the fCQL toolkit allows analysing the data distribution

(1D), data space (2D) and the fuzzy classification (see screenshot in Figure 13).

Figure 13: Screenshots of the fCQL Toolkit Query Panel

The queries with fCQL are analysed and translated into corresponding SQL statements for the

RDBMS ( in Figure 12). In order to generate the fuzzy classification results, the toolkit ac-

cesses the raw data as well as the meta-data and computes the membership degrees of the

classified elements in the different classes. The classification results are then displayed to the

user or returned to the application. Before querying the fCQL toolkit, the data architect ( ) has

to define the fuzzy classification (case 3). This primarily includes the definition and chart of

linear or s-shaped functions with the membership function editor ( ).

In order to be easily accessible and platform independent, the fCQL toolkit has been imple-

mented as a standalone Java application. Therefore, the user can install the toolkit on all

main platforms or operating systems available on the market (e.g. Windows, Mac, Linux, etc.).


- 20 -

2.2.4 Advantages of Fuzzy Classification and fCQL

Ce que l'ordinateur a de peu plaisant, c'est qu'il ne sait dire que oui ou

non, jamais un peut-être. (Brigitte Bardot, actrice française)

Das Unsympathische an Computern ist, dass sie nur ja oder nein sagen

können, aber nicht vielleicht. (Brigitte Bardot, französische Filmschauspielerin)

Saying this, Brigitte Bardot was definitively not aware of the idea of fuzzy classification dis-

cussed in this chapter: computers can say "perhaps" very well, they do not return a "yes" or a

"no" only. In fact, the main advantage of fuzzy classification and fCQL is that the membership

degree of a classified element to a class can be calculated in a range between 0 and 1.

In addition, a classified element is, in contrast to sharp classification, not limited to a single

class, but can be assigned to several classes at the same time. With fuzzy classification, the

transition between the different classes become fluent, that means continuous.

Further advantages of fuzzy classification and fCQL can be summarised so far: fCQL enables

the reduction of complexity without loss of information

a fair and non-discriminatory classification of customers according to their performance

the extraction of hidden data and a better description of the classified elements

to consider the potential as well as the possible weaknesses of each classified element

the use of numerical values, i.e. quantitative or monetary variables, and non-numerical

values, i.e. qualitative or non-monetary variables

queries on a linguistic level (that means the formulation of a word or word combinations),

an intuitive and human-oriented querying process and a clear semantic

the adoption of linguistic variables to the marketing terminology of every company and

therefore a facilitated cooperation between marketers and IT specialists

that the raw data of relational databases do not have to be migrated or modified

the multidimensional classification, evaluation and segmentation of objects, with any

number and any kind of attributes or criteria to decompose complex classifications into a hierarchy of classifications a dynamic classification, for instance the monitoring of classified elements over time and

the implementation of a trigger mechanism, which warns in case of a negative development

of the classified elements.


- 21 -

As it will be discussed in Chapter 4, fuzzy classification facilitates the personalisation, indi-

vidualisation and differentiation of marketing and therefore an improved one-to-one marketing

and mass customisation.

In addition, fuzzy classification and the fuzzy Classification Query Language (fCQL) as a data

mining tool can be used for customer and market segmentation, e.g. for the identification of

market segments (compare Figure 14), for classification (e.g. the identification of customer

with high turnover) or for performance measurement, e.g. for the detection of Key Customer

Performance Indicators (KCPIs). Another objective of data mining and fuzzy classification is the

description and visualisation of data.

Figure 14 shows these examples of possible tasks for fuzzy classification, other statistical data

analysis methods and data mining techniques, which are not considered in this master thesis.

For details on data mining and data mining techniques see for instance: [Ester and Sander,

2000, Berry and Linoff 2000, Berson et al. 2000, Drozdenko and Drake 2002, Witten and

Frank 2005, Neckel and Knobloch 2005, Han and Kamber 2006, Larose 2006, Williams 2006].

Figure 14: Examples of Tasks and Methods of Data Mining

Examples of problems Tasks of data mining Methods of data mining

Identification of profitable customer or market segments

Segmentation

Identification of customers with high turnover

Identification and analysis of purchase patterns

Market basket analysis

Presentation of results to the management

Description & visualisation

Detection of Key Customer Performance Indicators (KCPI)


Cluster analysis

Fuzzy classification & fCQL

Association analysis

Decision trees

Dependency analysis

Forecast of turnover X of customer Y for next year

Artificial neural network

Classification

Prediction & estimation

- 22 -

Chapter 3

Fuzzy Classification Management Tools

Chapter 3: Fuzzy Classification Management Tools

- 23 -

3.1 Potential Business Applications for Fuzzy Classification 3.1.1 Overview

The fuzzy classification approach and the fCQL toolkit are not just another concept or software

of information management. This data mining tool is a powerful instrument for management,

for the analysis and control of business processes and results.

If adequate data of marketing and accounting is available in an Information System (IS) or in a

Management Information System (MIS), fuzzy classification as a data analysis method can be

successfully used for Performance Measurement (PM). In the field of Customer Relationship

Management (CRM), fuzzy classification can therefore be used for Customer Performance

Measurement (CPM). Figure 15 gives a review of fuzzy Classification Management Tools

(fCMT) and the applications to different fields of marketing management.

Figure 15: Fuzzy Classification as a Promising Management Tool for Different Fields Figure 15 shows that fuzzy classification can be applied, for example, to

management tools; e.g. as fuzzy portfolio analysis, as fuzzy SWOT analysis, as fuzzy ABC

analysis and as fuzzy scoring model (content of the Sections 3.2 to 3.5), and to

performance measurement to analyse and control any kind of performance indicators of

production, marketing, managerial accounting or finance. Section 4.2 focuses on the per-

formance measurement of customers.

Customer relationship managementSection 4.1

Customer performance measurement Section 4.2

Fuzzy customer segmentation Section 5.1

Fuzzy credit rating Section 6.2

Customer Analysis

Value Satisfaction Loyalty Repurchases Retention Cross buying Turnover Costs Profits Equity, CLV Profitability Return on’s

Fuzzy portfolio analysis

Section 3.2

Fuzzy SWOT analysis Section 3.3

Fuzzy ABC analysis Section 3.4

Fuzzy scoring methods Section 3.5

Marketing and market research

Fuzzy market segmentation Section 5.2

Market analysis

Demand Development Products Prices Structure Competition

Manage-ment =

Analysis

Planning Implementation

Control

Inte

rnal

(c

ompa

ny)

Ext

erna

l (m

arke

t) Pe

rfor

man

ce

mea

sure

men

t

0 0.1

0.2 0.3

0.4 0.5 0.6

0.7 0.8

1

0.468 0.9 0

0.1

0.2 0.3

0.4 0.5 0.6

0.7 0.8

1

0.7390.9 0

0.1

0.2 0.3

0.4 0.5 0.6

0.7 0.8

1

0.964 0.9

Indicator 1 Indicator 2

μ high

0

1 μ high

0

1 μ high

0

1

Indicator n

Analysing and controlling of performance indicators for

Production Marketing

(e.g. products or customers) Accounting or finance

(e.g. sales, profits, etc.)


- 24 -

Fuzzy classification supports performance measurement, for instance, in the field of

Customer Relationship Management (CRM; discussed in Section 4.1); particularly the

task of customer analysis and customer segmentation (content of Section 5.1)

production and operations; to fuzzily categorise, analyse and control materials, products,

services and production processes and the utilisation of machines or employees.

supplier relationship management; to classify and evaluate different suppliers or products

segment marketing; to undertake fuzzy market segmentations, to fuzzily target market and

to fuzzily position products or companies in markets (content of Section 5.2)

credit rating; to fuzzily calculate the degrees of risk, creditworthiness or other data of bank

customers (content of Section 6.2).

3.1.2 Existing Literature on Marketing and Fuzzy Classification

Surprisingly, only a limited number of reviews, publications or books on fuzzy classification or

fuzzy system applications are available in literature on marketing and business management.

These cover, for instance: production and operations [Sárfi et al. 1996, Vasant et al. 2004],

web mining [Arotaritei and Mitra 2004] and portfolio selection [Inuiguchi and Ramik 2000].

Fuzzy systems have also been applied to credit rating [Levy et al. 1991, Romaniuk and Hall

1992, Weber 1996, Chen and Chiou 1999, Baetge and Heitmann 2000, Hoffmann et al. 2002,

Shin and Sohn 2004] and the modelling of fuzzy data in qualitative marketing research [Varki

et al. 2000]. In marketing, [Casabayo et al. 2004] used a fuzzy system to identify customers

who are most likely to defect to a different grocery retailer when a new retailer establishes

itself in the same area. As they state, the value added by fuzzy classification to customer re-

lationship management is the ability to transform customer data into real useful knowledge

for taking strategic marketing decisions (see [Vogues and Pope 2006]).

[Hruschka 1986] proposed a segmentation of customers using fuzzy clustering methods. A

clusterwise regression model for simultaneous fuzzy market structuring was discussed by

[Wedel and Steenkamp 1991]. [Fisher et al. 1995] analysed fuzzily socio-economic attributes

of older consumers. Hsu’s Fuzzy Grouping Positioning Model [Hsu 2000] enables an under-

standing of the relationship between consumer consumption patterns and a company’s com-

petitive situation and strategic positioning. However, most of the reviews are basic research

and usually do not deal with relational databases. If they do (see [Takahashi 1995, Biewer

1997, Bosc and Pivert 2000, Kacprzyk and Zadrozny 2000, Galindo et al. 2005]), they follow

another approach than fCQL does. In the authors’ point of view, practical applications of the

fuzzy classification approach both in literature and in business practice of marketing are still

insufficient. In view of that, the following chapters will point out further potential of fuzzy classi-

fication in the field of marketing, CRM and particularly in customer performance measurement.


- 25 -

3.2 Fuzzy Portfolio Analysis 3.2.1 Definition

The method of portfolio analysis is often used in the field of strategic management in order to

analyse and plan business strategies. According to [Kotler et al. 2005, p. 60], a business

portfolio is the collection of businesses, products or customers that make up the company.

The portfolio analysis is a tool by which management identifies and evaluates these various

businesses. The best business portfolio is the one that fits the company’s strengths to the op-

portunities in the environment (compare Section 3.3: Fuzzy SWOT Analysis). The company

first has to analyse its current business portfolio and decide which businesses should receive

more, less or no investment, and second to develop growth strategies for adding new busi-

nesses, or, in the case of a customer portfolio, to recruit and bind customers.

The most popular portfolio analysis is the market growth/market share matrix, which was

developed by the Boston Consulting Group (BCG). It has two axes: ‘real market growth’ and

‘relative market share’, which is determined in comparison with the position of the strongest

competitor. The axes define a matrix with four classes, which are usually labelled as ‘stars’,

‘question marks’, ‘cash cows’ and ‘poor dogs’. In the classes, Strategic Business Units (SBU)

or Strategic Business Fields (SBF), products, customers or other objects are positioned.

A Strategic Business Unit (SBU) is a unit of the company that has a separate mission and

objectives, and which can be planned independently from other businesses of the company.

[Grünig and Kühn 2005b, p. 133] define a SBU as a business which contributes critically to the

success, with its own independent market offer, but whose strategy must be adjusted to those

of other business units within the corporation because they operate in the same market and/or

share the same resources. The same authors define a Strategic Business Field (SBF) as a

business which contributes critically to the success and whose strategy can be planned inde-

pendently because it has an independent market offer and it does not to any significant extent

share markets and/or resources with any other business in the corporation. According to [Kot-

ler et al. 2005, p. 61], the four classes C1 to C4 in Figure 16a can be described as follows:

C1) Stars are high-growth, high-share businesses that require heavy investments to finance

their rapid growth. Eventually their growth will slow down and they turn into cash cows.

C2) Question marks are low-share business units in high-growth markets. They require cash

to hold the market share or become stars. Management has to think about the question

marks, which ones they should build into stars and which ones they should phase out.

C3) Cash cows are low-growth, high-share businesses, products or customers. These estab-

lished and successful units needs less investment to hold their market share. Thus they

produce cash the company uses to invest in other SBUs.


- 26 -

Figure 16: The Boston Consulting Group Matrix (a) and Norm Strategies (b)

C4) Poor dogs are low-growth, low-share businesses, products or customers. They generate

enough cash to maintain them, but do not promise to be large sources of cash.

Figure 16 shows the BCG portfolio and the norm strategies, which are related to each class.

3.2.2 Sharp Classification and Disadvantages

Considered sharply, for example SBU 2 in Figure 17a, belongs only to one class, C1 (stars).

Following the norm strategy (of Figure 16b), the SBU 2 is may be managed in the same way

as the real star, SBU 1. This could be problematic since the performance (growth and market

share) of SBU 2 still can be improved. The same classification problem occurs to SBU 3,

which is sharply classified as a poor dog (C4), like weak SBU 4. However, this is not right,

because SBU 3 is nearly in the same position as SBU 2 and is rather a star then a dog. Even

if more classes are defined: sharp classification does not solve the problem that elements lo-

cated near the borders of the classes are classified discriminatorily and imprecisely.

Figure 17: Sharp (a) and Fuzzy (b) BCG Portfolio

Low

Hig

h

Low High

C2)

Question marks

C1)

Stars

C4) Poor dogs

C3) Cash cows

Real market growth

Rel

ativ

e m

arke

t sha

re

a) b)

C2) Question marks

Improve dramatically rel- tive market share and then follow strategy for stars .

Or: ▪ Follow strategy for dogs

C1) Stars

Preserve or increase relative market share

Invest in resources or marketing

Tolerate negative cash flow

C4) Poor dogs

Minimize investment Continue while there is positive free cash flow

Sell/liquidate businesses, if cash flows are negative

C3) Cash cows

Preserve relative market share

Invest defensively in resources and marketing

Maximise cash flows

Rel

ativ

e m

arke

t sha

re

Real market growth

Source (b): adapted from [Grünig and Kühn 2005b, p. 172]

Life

-cyc

le

μ high market share

μ low market share

C2)

C1)

C4)

C3)

C2)

Question marks

C1)

Stars

C4) Poor dogs

C3) Cash cows

Low

Hig

h

Low High

Real market growth

Rel

ativ

e m

arke

t sha

re

a)

00

1

1

μ lo

w m

arke

t gro

wth

μ

high

mar

ket g

row

th

b)

SBU 1

SBU 3

SBU 4

Real market growth

Rel

ativ

e m

arke

t sha

re

100% Poor dog

SBU 2

Sharply classified sales, turnover or profit of a product, customer or SBU/SBF of the last year or average for recent years

Fuzzy classified sales, turnover or profit

100% Star

25.6% Cash cow

24.2% Question mark

29.8% Poor dog

20.4% Star .

.

.

.

.

.

.

.

48.4% Star 14% Cash cow

31% Question marks

6.6% Poor dog

SBU 1

SBU 3

SBU 4

SBU 2

0.9

0.42

0.3

0.7 0.440.56

0.48

0.1


- 27 -

3.2.3 Fuzzy Classification and Advantages

With fuzzy classification, misclassifications can be eliminated. Viewed through a fuzzy lens

(Figure 17b), SBU 2 belongs now to all classes at the same time with following normalised

membership degrees: 48.4% to the class ‘stars’, 31% to ‘question marks’, 14% to ‘cash cows’

and 6.6% to the ‘poor dog’ class. SBU 3 goes also with all classes (20.4% ‘star’, 24.2% ‘ques-

tion mark’, 25.6% ‘cash cow’, 29.8% ‘poor dog’) and can be separated from the real dog SBU

4 and managed accordingly. Considered sharply, there is no ‘question mark’ in the portfolio to

invest. Classified fuzzily, there is a certain percentage; with the fCQL toolkit, the membership

degrees of all the classified business units to the class C2 (‘question marks’) can be queried:

classify SBU from portfolio with class is question marks

In this simple example with four SBU’s, the user will receive following absolute membership

degrees to C2: SBU 2: 50.1% and SBU 3: 41.9%.

The portfolio manager now wants to find out which businesses are performing well, i.e. all

membership degrees of all fuzzy classified SBUs to the class ‘stars’ (C1). He queries:

classify SBU from portfolio with class is stars

By querying the following membership functions, he obtains the same results:

classify SBU from portfolio with real market growth is high market growth and relative market share is high market share

In this example the query results are also obvious (absolute degrees; SBU 1: 100%, SBU 2:

78.2%, SBU 3, 35.3%). However, in business practice, where portfolios of an enterprise may

consist of hundreds of businesses or products, the query process and the results are more

complicated. In this case, the potential and the benefit of the fCQL toolkit is much higher.

Applying fuzzy classification to strategic portfolios has several implications. Fuzzy classifica-

tion allow portfolio managers to analyse and decide differentiated and more precisely about

actual and future strategies of each classified business unit. Since the membership degrees to

the different classes, e.g. all membership degrees of all fuzzy classified businesses to the

class C2, can be exactly calculated, the degree of investments in businesses can be under-

taken proportionally to the share to a class. Figure 18 shows an example of such fuzzy in-

vestment. Since ‘question marks’ have to gain in market share, they require the highest capi-

tal spending proportion, for instance 60 percent of the investment expenditures. In addition,

‘stars’ (C1: medium investment of 30%) and ‘cash cows’ (C3: low investment 10%) need some

investments to hold the market share. There are no investments in ‘poor dogs’ (C4: 0%).


- 28 -

Figure 18: Sharp (a) and Fuzzy (b) Investments

Classified sharply (Figure 18a), 60% are invested in both SBU 2 and SBU 3. However, in the

fuzzy classified portfolio (Figure 18b), the investment in SBU 2 amounts only 48.1%, not 60%

anymore. Sharply, SBU 5 in the middle of the portfolio belongs only to class C3) with 10% in-

vestment. Fuzzily, in SBU 5 is invested much more: 21.5% (for calculation see Appendix 1).

In contrast to sharp portfolio analysis, fuzzy one enables to calculate a theoretically most

efficient investment and an optimal allocation of limited resources. However, these val-

ues have to be considered as benchmarks for the share to be invested. The exact sum for the

investment depends on what is necessary for the development of the business or the SBU.

Considered dynamically, fuzzy portfolio analysis enables to monitor the development or in-

vestments in each business unit and its performance over time. In addition, fuzzy classification

contributes to the achievement of a main objective of portfolio analysis: a well-balanced col-

lection of investments. Fuzzy portfolio analysis facilitates to balance mature cash producing

business units and future-oriented units, which require investments. This ensures, on the one

hand, that the company is investing in markets, which are highly attractive in the future, and on

the other hand, that the business units in mature markets must be self-financing and produce

free cash flow, which can be invested in other business units [Grant 2002, p. 410].

To balance a portfolio, management has first to define an ideal composition of the portfolio,

the optimal percentages of the normalised membership degrees of all classified business units

to each class. In Figure 19c), an optimally Balanced Portfolio (BP*k; symbol: ) consists of

33% membership degrees of all business units to class C1 (BP*1 = ∑Mnorm(Oi│C1) = 0.33),

21% membership degrees of all business units to class C2 (BP*2 = ∑Mnorm(Oi│C2) = 0.21),

37% membership degrees of all business units to class C3 (BP*3 = ∑Mnorm(Oi│C3) = 0.37) and at most

09% membership degrees of all business units to class C4 (BP*4 = ∑Mnorm(Oi│C4) = 0.09).

In this case, the ‘cash cows’ and ‘stars’ generate enough free cash flows, which can be in-

vested in ‘question marks’, i.e. in the future of the company.

μ high market share

μ low market share

C2)

C3)

C1)

C2)

High investment (60%)

C1)

Medium investment (30%)

C4) No investment

(0%)

C3) Low investment

(10%)

Low

Hig

h

Low High

Real market growth

Rel

ativ

e m

arke

t sha

re

a)

00

1

1

μ lo

w m

arke

t gro

wth

μ

hig

h m

arke

t gro

wth

b) Real market growth

Rel

ativ

e m

arke

t sha

re 26.1% medium investment

17.3% high investment 33.3% low investment 23.3% no investment

FUZZINV5 = 0.261·30 + 0.173·60 + 0.333·10 +

0.233·0 = 21.5%

FUZZINV2 = 48.1%

FUZZINV3 = 60% FUZZINV1 = 30%

FUZZINV4 = 26.2%

FUZZINV6 = 10%

SHARPINV2 = 60%

SHARPINV3 = 60%

SHARPINV1 = 30%

SHARPINV6 = 10%

SHARPINV4 = 30%

SHARPINV5 = 10%

SHARPINV7 = 0% FUZZINV7

= 4.7%FUZZINV8 = 0% SHARPINV8 = 0%

SHARPINVi = Sharp investment in SBUi (i = 1,…,8)

FUZZINVi = Fuzzy investment in SBUi (i = 1,…,8)

SBU 3

SBU 2 SBU 1

SBU 4

SBU 5

SBU 6 SBU 7

SBU 8

SBU 3 SBU 2

SBU 1

SBU 4

SBU 5 SBU 6

SBU 7 SBU 8


- 29 -

If the portfolio is optimally balanced (BP*k), the Balance Error (BE) is 0. The higher the sum of

the absolute deviations (│absolute values│) between the membership degrees Mnorm(Oi│Ck)

and the degrees of an optimally balanced portfolio, the higher is the balance error.

Example of C4 in Figure 19b/d: │.25-.33│+│.22-.21│+│.14-.37│+│.39-.09│ = .08 + .01 + .23 + .3 = 0.65

In addition to the BE, basic requirements should be defined for an optimal portfolio, e. g. not

less ‘cash cows’ than 25% of the portfolio (∑Mnorm(Oi│C3) > 0.25), at least 10% ‘questions

marks’ (∑Mnorm(Oi│C1) > 0.1) or not more ‘poor dogs’ than 15% (∑Mnorm(Oi│C4) < 0.15).

Figure 19: Balancing of Fuzzy Classified Portfolios

If there are too little membership degrees of all classified businesses to class C2-3 (portfolio

with insufficient actual cash flow in Figure 19a), to class C3-2 (portfolio with insufficient future

potential) or to C4-2 and C4-3 (portfolio with both insufficient actual and future cash flow), the

portfolio is not balanced (symbol: ). In this case, the basic requirements are not fulfilled

and/or the level of the balance error exceeds the maximum level (max. of 0.30 in Figure 19d).

A trigger mechanism (red-flag-function) warns the portfolio manager who can launch counter

measures to augment actual cash flow or/and future cash flows, by investing in R&D or by

acquiring new businesses in order to balance and optimise the composition of the portfolio.

d) Balance Error and Implementation of a Trigger Mechanism

Optimally balanced portfolio

Sha

re

Unbalanced customer portfolios Trigger mechanism (red-flag-function)

0.37

0.05

0.19

0.1

0.33 0.37

0.09

0.21

Bal

ance

er

ror (

BE

)

0.64

0.54

CX.1 Stars CX.2 Question marks CX.3 Cash cows CX.4 Poor dogs

C1-2)

Question Marks:

21%

C1-3)

Cash Cows: 37%

Real market growth

Rel

ativ

e m

arke

t sha

re

C1-1)

Stars: 33%

C1-4)

Poor Dogs: 9%

max.:0.30

a) Unbalanced Portfolios

Act

ual c

ash

flow

C3)

Insufficient future

potential

C1)

Balanced (diversified)

portfolio

C3-2)

C3-3)

C3-4)

C3-1)

C2-2)

C2-3)

C2-4)

C2-1)

Market growth Real market growth

Rel

ativ

e m

arke

t sha

re

c) Optimally Balanced Portfolio (BP*k)

Future cash flow b) Balance Indicator

Rel

ativ

e m

arke

t sha

re

C4)

Insufficient actual & future

cash flow

C2)

Insufficient actual

cash flow

C4-2)

C4-3)

C4-4)

C4-1)

Real market growth

Rel

ativ

e m

arke

t sha

re

0.25 0.22

0.14

Portfolio with insufficient actual cash flow

Portfolio with insufficient future potential

Portfolio with both insufficient actual & future cash flow

0

0.18

0.4 0.65 0.35 0.36 0.39

BP*1 = ∑Mnorm(Oi│C1) = 0.33

( ) ( ) ( ) ( ) ⎟⎠

⎞⎜⎝

⎛−+⎟

⎠

⎞⎜⎝

⎛−+⎟

⎠

⎞⎜⎝

⎛−+⎟

⎠

⎞⎜⎝

⎛−= ∑∑∑∑

====

09.0COM0.37 COM0.21 COM 33.0COMBE1

4inorm1

3inorm1

2inorm1

1inorm

n

i

n

i

n

i

n

i


- 30 -

The fuzzy portfolio analysis approach is not only promising for the BCG portfolio, but can be

applied to all other existing portfolio analyses. For instance, the more complex McKinsey/

General Electrics portfolio holds the dimensions ‘industry attractiveness’ and ‘competitive

strength’, which can be defined by one or several criteria shown in Table 6.

Table 6: Criteria for Assessing Industry Attractiveness and Competitive Strength

W

Criteria for assessing industry attractiveness Criteria for assessing competitive strength Industry or market size or volume Market share or growth of market share Industry or market growth or potential Corporate size and financial strength Industry or market profitability Market, customer or technological knowledge Capital, investment or cost intensity Product quality Technological level and stability Corporate or product image Innovation intensity and potential Production, sales or service effectiveness Competitive intensity or entrance barriers Low operating costs and productivity Cyclical, seasonal or inflation independence Price competitiveness Dependence of regulation and government Distribution network or geographic advantages Risks (industry, technology, environment, etc.) Competence of management and employees

Source: adapted from [Kotler et al. 2005, p. 62, Grünig and Kühn 2005b, p. 176, Waibel an Käppeli 2006, pp. 85f]

The two attributes ‘industry attractiveness’ and ‘competitive strength’ with the terms ‘low’, ‘me-

dium’ and ‘high’ define nine squares (classes C1 to C9 in Figure 20a). To each class in the

industry attractiveness/competitive strength portfolio, general recommendations for strategies

are assigned: the strategy ‘growth/investment’ (invest and tolerate any negative free cash

flow), ‘selective’ and ‘harvest/divestment’ (invest a minimum, divest if cash flow is negative).

Keeping strictly to these norm strategies could be problematic, as following examples show.

Although SBU 2 is almost in the same position as SBU 1, it is in a red field and may wrongly

disinvested, but in green SBU 1 is invested. In SBU 3 it also divested, although it is not in such

a bad position as SBU 4 and a selective strategy would be appropriate.

With fuzzy classification and the disappearance of the classes sharp borders (Figure 20b), the

problems are solved; it can be fuzzily invested or disinvested in businesses for optimal growth.

Figure 20: Sharp (a) and Fuzzy Classified (b) McKinsey/General Electrics Portfolio

C9)

Divest

C7)

Selected withdrawal

C4)

Double or quit

C5)

Proceed with care

C6)

Cash Generation

C3)

Try harder

C1)

Leader

C2)

Growth

C8)

Phased withdrawal

0 Com

petit

ive

stre

ngth

μ u

nattr

activ

e

1

μ a

ttrac

tive

μ low competitive μ high competitive 1

Industry attractiveness

0

μ medium competitive

μ m

ediu

m a

ttrac

tive

C3)

Try harder

C1)

Leader

C2)

Growth

Selective strategy

Harvest/divestment strategy

Growth/investment strategy

L

ow

Med

ium

Hig

h

Industry attractiveness

Low Medium High Com

petit

ive

stre

ngth

a) Sharp classification

C9)

Divest

C4)

Double or quit

C5)

Proceed with care

C6)

Cash Generation

C8)

Phased withdrawal

C7)

Selected withdrawal

Source: [Schawel and Billing 2004, p.147]

SBU 4

SBU 3

SBU 2

SBU 1

b)


- 31 -

3.3 Fuzzy SWOT Analysis 3.3.1 Definition

SWOT stands for Strengths, Weaknesses, Opportunities and Threats and is an often used

strategic analysis and planning method both in business management theory and practice.

According to [Kotler et al. 2005, pp. 58f], SWOT analysis is “a distillation of the findings of the

internal and external audits which draws attention to the critical organisational strengths and

weaknesses and the opportunities and threats facing the company.”

The strengths and weaknesses analysis is an internal audit that focuses on the corporate

performance, strategy, resources, capabilities or developments. For instance, the following

elements can be classified: company’s sales, materials, production, products, services, cus-

tomers, financial resources, employees and skills, know-how, innovative ability or flexibility.

The opportunities and threats analysis is carried out by examining external elements of

the demographic, economic, political, legal, sociological, technological or cultural environ-

ment and of competitors (e.g. their products, prices, distribution, resources or customers).

Figure 21: Sharp (a) and Fuzzy (b) SWOT Matrix 3.3.2 Sharp Classification and Disadvantages

So far, the SWOT analysis has been applied in a sharp manner in most cases: the elements

or attributes in the SWOT are classified sharply, i.e. an attribute is classified exactly and only

to one class (to C1, C2, C3 or to C4 in Figure 21a).

In fact, in SWOT dominates a "negative-or-positive-" and a "internal-or-external-thinking".

However, it is often inadequate to classify objects sharply, as Figure 21b shows:

E

xter

nal

Inte

rnal

C2)

Strengths

C1)

Weaknesses

Opportunities

C4)

Threats

C3)

Perspective

Eval

uatio

n

a) b)

Positive Negative

Mat

ch

Avo

id Reduce

Reduce

Convert

Convert

C2)

Strengths

C1)

Weaknesses

C4)

Opportunities

C3)

Threats

μ negative

00

1

1

μ positive

μ ex

tern

al

μ

inte

rnal

Element 3

Element 2 Element 1

Element 4

Element 5

Perspective

Eval

uatio

n


- 32 -

Elements 1 and 2 are considered as weaknesses. However, element 2 was evaluated not

as negative as 1 and should be viewed as positive too, as a weakness and as a strength.

Although element 3 and 4 are located nearly at the same position, they sharply belong to

different, opposite classes: element 3 is internal and negative, 4 external and positive.

Even though element 4 and 5 are both classified in the same class C4, their positions are

actually very different. While element 5 is certainly a high opportunity, for 4 this is not clear:

it could quickly change into strength, weakness or into a threat.


As these examples point out: it makes sense to consider transitions between different classes

step like. With a fuzzy SWOT analysis (Figure 21b), the classification problem can be avoided,

since the classified elements can be partly internal, external, positive and negative at the

same time. This reflects reality better, since it is rarely the case that a classified element,

characteristic, object or fact has only positive or only negative points. Fuzzy classification not

can be applied to the SWOT matrix only, but also to each of the four classes in Figure 22.

Figure 22: Fuzzy Strength (a), Weakness (b), Opportunity (c) and Threat (d) Matrices

c) Fuzzy Opportunity Matrix d) Fuzzy Threat Matrix

C3-2)

Latent but high threats

(to monitor; e.g. emergency planning)

C3-1)

Major threats

(to pay high attention and to avoid by

contingency plan-C3-4)

Minor threats

(to ignore)

C3-3)

Probable butlow threats

(to monitor and to avoid if necessary)

μ very probably

00

1

1

Seriousness of threat

P

roba

bilit

y of

occ

urre

nce

μ very improbably

μ no

t ser

ious

μ

very

ser

ious

C4-2)

Golden opportunities

(to improve success probability)

C4-1)

High opportunities

(to focus on and to invest)

C4-4)

Negligible opportunities

(to ignore)

C4-3)

Limited opportunities

(to improve attractiveness )

μ very probably

0 0

1

1

Attractiveness of opportunity

S

ucce

ss p

roba

bilit

y

μ very improbably

μ un

attra

ctiv

e

μ

very

attr

activ

e

a) Fuzzy Strength Matrix b) Fuzzy Weakness Matrix

C1-2)

Irrelevant weaknesses

(to monitor & reduce if necessary)

C1-1)

Primary weaknesses

(competitive disad-vanteges to reduce)

C1-4)

Unimportant weaknesses

(to keep low)

C1-3)

Secondary weaknesses

(to monitor and selectively reduce)

μ very important

00

1

1

Degree of weakness

Im

porta

nce

of w

eakn

ess

μ unimportant

μ lo

w w

eakn

ess

μ

high

wea

knes

s

C2-2)

Ineffectual strengths

(to selectively retain)

C2-1)

Strategic strengths

(competitive ad-vantages or KPIs or

KSF to retain) C2-4)

Unimportant strengths

(to keep or to selectively improve)

C2-3)

Development strengths

(to improve)

μ very important

0 0

1

1

Degree of strength

Im

porta

nce

of s

treng

th

μ unimportant

μ lo

w s

treng

th

C2)

Strengths

C1) Weak-nesses

C4) Opport-unities

C3)

Threats

μ hi

gh s

treng

th

Fuzzy Risk Matrix Prob

abili

ty

Damage

C3-2)

High risks

C3-1)

Extreme risks

C3-4)

Low risks

C3-3)

Moderate risks

analogue:


- 33 -

Each strength of the fuzzy strength matrix (Figure 22a) can be evaluated fuzzily by its de-

gree of importance, whether it is rather a ‘strategic strength’ (a Key Success Factors; KSF), an

‘ineffectual’, an ‘unimportant’ or a ‘development strength’ to improve. Management can acquire

information on all ‘strategic strengths’ by querying the membership degrees to C2-1:

classify strengths from strength matrix with class is strategic strengths Appendix 2 (Checklist for Performing; Fuzzy Strengths/Weaknesses Analysis) shows a list,

how marketing, finance, manufactering or organisation performance can be evaluated fuzzily.

Management also needs to monitor key macroenvironment forces (demographic-economic,

natural, technological, political-legal, and social-cultural) and significant microenvironment

actors (customers, competitors, suppliers, distributors, dealers) that affect its ability to earn

profits. For each development or trend of the external environment, management has to iden-

tify the associated opportunities and threats [Kotler and Keller 2005, p. 52]. To evaluate oppor-

tunities, the manager can use a Market Opportunity Analysis (MOA) to determine the attrac-

tiveness and probability of success, by defining a fuzzy opportunity matrix (see Figure 22c).

To determine fuzzily the best marketing opportunities to invest (C4-1), the user queries:

classify opportunities from opportunity matrix with class is high opportunities

In addition, threats have to be observed as well: an environmental threat is a challenge posed

by an unfavourable trend or development that would lead, in the absence of defensive market-

ing action, to lower sales or profit [Kotler and Keller 2005, p. 53]. Threats should be classified

according to their seriousness and their probability of occurrence (fuzzy threat matrix in

Figure 22d). Analogical to the threat matrix, a risk matrix classifies different risks by their

‘amount of damage’ and the ‘probability of occurrence’. The risk map is an important instru-

ment to analyse, evaluate and to classify potential risks for a division, a business unit or for the

whole company. The ‘probability of occurrence’ can be estimated either quantitatively on a

metric scale, or qualitatively on an ordinal scale from ‘very improbably’ (rare likelihood of risk

occurrence) to ‘very probably’ (risk occurs almost certain), and the ‘amount of damage’ from

‘insignificant’ to ‘catastrophic’.

As shown in Figure 23a, the classes C1 to C25 are assigned to four different level of risk: low,

moderate, high and extreme. Risks classified above the threshold (dashed diagonal) have to

be surveyed exactly. A risk manager can follow different strategies to accept, retain or limit,

reduce, avoid or to transfer risks to third parties (i.e. to assurances).

So far, the risk matrix has been applied in a sharp manner, although this can be problematic

as the examples in Figure 23a show: risk A and B are nearly in the same position, but they are

classified in very different classes: risk A is a low risk to accept and B is a high risk to reduce.


- 34 -

Figure 23: Sharp (a) and Fuzzy (b) Risk Matrix

Risk C and D, one the other hand, are classified in the same class, although the amount of

damage and the probability of occurrence of risk D is higher. With fuzzy classification (Figure

23b), such mistakes do not happen and risks of the matrix can be classified, managed and

controlled more exactly. To receive the membership degrees of all risks, where the amount of

damage is catastrophic and the probability of occurrence is very high (C1), it has to be queried: classify risks from risk matrix with probability of occurrence is very probably and amount of damage is catastropic

This section illustrated that the use of a fuzzy SWOT analysis is manifold. It enables

a more exactly and differentiated description of the classified elements in all matrices

to discern new market and marketing opportunities

to classify and survey different kind of threats or risks in the fuzzy risks matrix

the analysis of the company’s prospects for sales and profitability

to focus on the corporate activities and resources

to improve deficiencies and shortcomings and to defend or to extend strengths

to reveal the real core competences or competitive advantages, and disadvantages

the identification of Key Performance Indicators (KPI) or Key Success Factors (KSF)

the formulation or adoption of corporate, business or marketing strategies

a clear semantic and a reduction of redundant internal classes

to monitor the SWOT matrices or the risk matrix dynamically and fuzzily over time

the definition of a strength, weakness, opportunity or threat and a SWOT indicator and

the implementation of trigger mechanisms, if SWOT or risk indicators are degrading.

High risk to transfer or to reduce

Extreme risk to avoid or to transfer

Risk A

Amount of damage

0

μ very improbably μ very probably 1

01

Amount of damage

Pro

babi

lity

of o

ccur

renc

e

C1)C2) C4) C7) C11)

C3)C5) C8) C12) C16)

C6)C9) C13) C17) C20)

C10)C14) C18) C21) C23)

C15)C19) C22) C24) C25)

Low risk to accept or to retain

Moderate risk to reduce or to accept

Risk B

Very low Low Medium High Very highinsi

gnifi

cant

m

inor

m

oder

ate

maj

or c

atas

tropi

c

Examples of risks: Market risk Natural disasters Contractual risk Personell risk Legal risk Production risk

Threshold of risk

Source of examples: [Gladen 2006, p. 130]

Risk C Risk D

Pro

babi

lity

of o

ccur

renc

e

μ in

sign

ifica

nt

μ c

atas

tropi

c

C2)

Improbable risks with high amount

of damage

a) b)

C1)

Probable risks with high amount

of damage

C4)

Improbable risks with low amount

of damage

C3)

Probable risks with low amount

of damage


- 35 -

3.4 Fuzzy ABC Analysis 3.4.1 Definition

The ABC analysis is the most widely used method of customer, material or product segmen-

tation. In CRM practice, the ABC analysis is used between 29% and 88% of all companies,

depending on industry and study (see e.g. [Plinke 1997, Rudolf-Sipötz 2001, Köhler 2005,

Bruhn and Georgi 2006, Günter and Helm 2006, Reinecke and Tomczak 2006, Homburg and

Krohmer 2006]). ABC analysis is used to reduce complexity, to analyse and monitor the cus-

tomer structure, and to prioritise investments. It classifies customers into three different

segments, into A-, B- and C-customer. In Table 7, 20 customers are sorted by their turnover.

Table 7: Sharp ABC Analysis

# Cumulative share of customers (%) Class Class def.

(sharp) Number of Customers Customer Turnover

2006 (€)Cumulative

Turnover (%)

1 5% Marshall 21’760 32% 2 10% A [0,…,11.9] 2 Miller 19’040 60% 3 15% Brown 9’520 74% 4 20% Wright 4’080 80% 5 25% O’Connor 3’400 85% 6 30% Cooper 2’040 88% 7 35%

B [12,…,35.9] 5

Smith 1’224 89.8% 8 40% Ford 816 91% … … … … … 19 95% Graham 408 99.5% 20 100%

C [36,…,100] 13

Forrester 340 100%

"20:80-rule"

Total 68’000

The cumulative shares show, how many customers generate how much of the turnover. In

CRM literature (for instance [Homburg and Beutin 2006, p. 230]), often the 20:80-rule, or the

Pareto-rule is mentioned, which states that 20% of the customers generate 80% of the turn-

over. Usually, very few A-customers have a high value percentage. In Table 7, only two A-

customers, Marshall and Miller, generate 60% of the turnover. The B segment has a medium

value and a majority of the customers (C) have a low share in turnover.

The Lorenz or concentration curve in the example of Figure 24 indicates a concentrated distri-

bution of customer turnover. In the case of an equipartition, the curve would be a diagonal.


So far, the rather uncritical discussion about ABC analysis for customer segmentation in litera-

ture on marketing is always done in a sharp way. However, it is problematic to classify cus-

tomers sharply. First, the definition and fixing of the three classes (A: [0,…,11.9], B:

[12,…,35.9] and C: [36,…,100] in Table 7) is arbitrary. Second, sharp classification can be

unfair and lead to wrong decisions as the examples of the four customers in Figure 24 show:


- 36 -

Although Ford and Smith have similar turnovers, Ford is classified to class C and Smith to B.

Even though Brown’s turnover (9’520 €) is totally different from that of Smith (1’224 €), they

belong to same class B. However, Brown is rather an A-customer like Miller and should be

considered as a key account too. If Brown or Ford had little higher turnover, in some cases

may be only a few euros, they would slip into a higher class and were managed differently.


Figure 24: Fuzzy ABC Analysis With fuzzy classification, the transitions between A-, B- and C-customers' become fluent and

customers can be fuzzily classified by their real turnover (compare Figure 24 and Table 8).

Considered fuzzily, only Customer #1 is an A-, #5 a B-, and #10-20 are C-customers only. For

the rest a membership degree for both classes [A and B] or [B and C] are calculated.

Table 8: Fuzzy ABC Analysis

# Cumulative share of customers (%) Class Class definition

(fuzzy) Customer Turnover 2006 (in €)

Cumulative turnover (%)

1 5% A ([A: 100%]) Marshall 21’760 32% 2 10% ([A: 64%], [B: 36%]) Miller 19’040 60% 3 15%

(A, B) ([A: 32%], [B: 68%]) Brown 9’520 74%

4 20% ([A: 6%], [B: 94%]) Wright 4’080 80% 5 25% B ([B: 100%]) O’Connor 3’400 85% 6 30% ([B: 74%], [C: 26%]) Cooper 2’040 88% 7 35% ([B: 57%], [C: 43%]) Smith 1’224 89.8% 8 40% (B, C) ([B: 38%], [C: 62%]) Ford 816 91% 9 45% ([B: 10%], [C: 90%]) Spencer 748 92.1% … … … … … 19 95% Graham 408 99.5% 20 100%

C ([C: 100%])

Forrester 340 100% Total 68’000

μ C

0

1

With fuzzy classification, the transitions between

A-, B- and C-customers became fluent.

A-customers B-customers

C-customers


Cum

ulat

ive

shar

e

of c

usto

mer

s (%

)


0

1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

μ A

Lorenz curve (concentration curve)

Smith Ford

Miller

Brown

“20:80-Rule”

μ B

#1

#2

#3 #4

#5 #6 #7 #8 #9 #10

#11 #12 #13 #14 #15 #16 #17 #18 #19 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

A-customers B-customers C-customers Cumulative share of customers (%)

#20

Sharp ABC Analysis


- 37 -

Another restriction of the discussed example of ABC analysis is the isolated consideration of

only one single criterion, turnover. Customer turnover provides no information about the costs

and the profitability of a customer. Consequently, a differentiated fuzzy ABC analysis should

consider different customer performance indicators for profitability and investments (Figure

25a), for example customer contribution margins, net profit or the Return on Customer (ROC).

CRM should not consider financial metrics only, but also qualitative indicators about the cus-

tomer relationship, i.e. a fuzzy ABC analysis of customers’ satisfaction, loyalty or retention

(Figure 25b). In this case, very (dis)satisfied or (dis)loyal customers are identified.

In addition, even a highly unprofitable C-customer can be important for a company, since he

may has a very good recommendation, information or cooperation behaviour (Figure 25c), e.g.

a high number of recommendations and suggestions, or profound product knowledge.

Figure 25: Fuzzy ABC Analysis with Different Customer Performance Indicators

As these examples point out: a fuzzy ABC analysis can be adapted with any kind of criteria or

indicators. In addition, the fuzzy ABC analysis can be combined with other analysis methods,

for instance with the fuzzy portfolio analysis.

The combination of the fuzzy ABC analysis with the customer attractiveness/competitive posi-

tion portfolio (discussed in Chapter 2) defines a fuzzy classification space with six classes, C1

to C6 in Figure 26. ‘A-star-customers to maintain’ (C1) are customers with a high degree of

attractiveness, e.g. customers with high potential for future turnover or customer lifetime value,

and with a strong competitive position (e.g. customers with a high share of wallet).

A-customers B-customers C-customers

Customer performance measure of profitability & investments (I1-110)

Cum

ulat

ive

shar

e of

cus

tom

ers

Fuzzy ABC analysis of customer revenue,

profitability & investment

0

1μ A μ C

0

1μ B


Customer performance measure of relationship (I111-150)

Cum

ulat

ive

shar

e of

cus

tom

ers

Fuzzy ABC analysis of customer relationship

0

1μ A μ C

0

1μ B

B-customers C-customers

Performance measure of recommendation, information & cooperation (I151-173)

Cum

ulat

ive

shar

e of

cus

tom

ers

Fuzzy ABC analysis of customer recommendation, information & cooperation

0

1μ A μ C

0

1μ B

A-customers

I# Examples of customer relationship indicators

I124 Customer value I130 Customer involvement I131 Customer commitment I132 Customer attachment I133 Customer trust I126 Customer satisfaction I134 Customer loyalty I137 Customer retention I147 Duration of customer relationship I148 Intensity of customer relationship I149 Quality of relationship

I# Examples of customer recommendationinformation or cooperation indicators

I152 Number of recommendations I155 Potential reference recipients I156 Role as opinion leader I158 Consulting/helpdesk intensity I163 Number/quality of suggestions I164 Number of complaints I167 Number of returns I168 Product expertise or knowledge I169 Response rate I171 Cooperation behaviour I172 Expertise for cooperation

a) b) c)

I# Examples of customer performance indicators for revenue & profitability

I21 Demanded volume I39-41 Share of wallet or customer I50-53 Cross-/up-selling I55,56 Repurchases I69-74 Contribution margins I-IV I75-78 Customer gross/net profit I79 Customer equity I80,81 Customer Lifetime Value I83-85 Customer potential I102 Total customer costs I104 Return on customer (ROC)

For Detail on customer performance indicators (I 1 - I 173) see Appendix 4: 170+ Customer Performance Indicators (pp. 136ff)


- 38 -

Figure 26: Combination of the Fuzzy Portfolio and ABC Analysis

‘A-absorption-customers’ in C2 are ‘strong’ too, but their attractiveness is lower than in C1. In

‘B-development-customers’ (C3) has to be invested, but unpromising ‘B-absorption-customers’

(C4) are rather to skim, like those of C2. A ‘C-development-customer’ (C5) is still promising. In

contrast, ‘C-renunciation-customers’ (C6) are less attractive and should not be invested in.

Considered sharply, customer Ford in the example belongs entirely to C6 and would not be

invested in; fuzzily she belongs to four classes (C2, C4, C5, C6) and is part of different strate-

gies. Customer Smith and Brown also belong to four classes. Miller belongs to C1 and to C2,

i.e. he is not a 100%-A-star-customer anymore: his ‘competitive position’ still can be improved.

This combination of the fuzzy portfolio and ABC analysis is an interesting method for the for-

mulation and implementation of customer strategies. According to the corporate positioning in

the market, to the target segment and to the customer acquisition strategy, a company has

to acquire new, promising customers to maintain or increase sales, profits and market share.

To develop C- or B-customers, the company has to define and implement development and

penetration strategies to intensify customer relationships and to strengthen the company’s

competitive position at the customers. This can be realised e.g. by cross-/up-selling strategies

or by increasing purchase frequency and intensity. In addition, the company should augment

customer retention by different marketing actions discussed in Chapter 4. The intensity of the

strategy implementations has to vary according to the level of a customer’s attractiveness.

With the simultaneous consideration of quantitative and qualitative criteria, the fuzzy ABC

analysis segments customers differentiated, fairly and non-discriminatory. CRM can treat the

customers according to their value for the company. As a result, strategic and operational de-

cisions about customers or customer segments (e.g. key accounts), can be enhanced.

Finally, the fuzzy portfolio and ABC analysis enables an improved personalisation, individu-

alisation and differentiation of the products and services and better mass customisation.

μ strong (A)

C1)

A-star- customers

(to maintain)

C2)

A-absorption- customers (to skim)

Customer attractiveness (or potential)

Com

petit

ive

posi

tion

μ weak (C)

μ un

prom

isin

g

0 0

1

1

μ

pro

mis

ing

C5)

C-development- customers (to invest)

C3)

B-development customers (to invest)

C4)

B-absorption-customers (to skim)

C6)

C-renunciation-customers

(not to invest)

C1)

C3)

Customer attractiveness (or potential)

Com

petit

ive

Pos

ition

C4)

C2)

C5)

C6)

Acquisition strategies

Penetration strategies Disinvestment strategies

Retention strategies Moderate & aligned strategies Consequent

implementation Moderate implementation

Ford Smith Brown

μ medium (B)

Recovery strategies

Miller


- 39 -

3.5 Fuzzy Scoring Methods 3.5.1 Definition

In contrast to the ABC and portfolio analysis, which consider only one or two criteria, with

scoring methods several quantitative or qualitative variables can be taken into account.

The scoring approach assigns a predefined, weighted score to each value of a customer vari-

able. All scores of the different variables are added to the customer’s total score.

The most well-known example of a scoring method is the so called RFM method. RFM stands

for ‘Recency of last purchase’, ‘Frequency of last purchase’ and ‘Monetary value’ and is a

widely accepted and often used measurement tool in direct marketing and CRM (see for in-

stance [Link and Hildebrand 1993, Blattberg et al. 2001, Köhler 2005, Homburg and Sieben

2003, Neckel and Knobloch 2005, Günter and Helm 2006, Bauer et al. 2006, Krafft 2007]).

Empirical analyses show significant correlations between these three variables and the repur-

chase behaviour of customers, e.g. the response rate to mailings [Köhler 2005, Krafft 2007].

High frequencies and high monetary values of customer repurchases are more likely

the more recent a customer purchased (recency)

the more frequent a customer ordered in defined period (frequency) and

the higher customer turnovers were in the last years (monetary value).


The more recent and frequent a customer purchased products from the company in a period,

and the higher the monetary value is, the more the points assigned to a customer. Table 9

shows an example of the RFM method used in mail order business. The assignment of the

points to the classes obviously happens in a sharp manner. If a customer ordered for 99 €, for

instance, he is classified sharply in the Monetary class ‘51-100 €’ and receives plus 25 points.

Table 9: Example of the RFM Method with Sharp Classes

Customers Variables Points per variable Brown Miller

Initial score +25 +25 +25 Date of last pur-chase (Recency)

≤ 6 months: +40

7-9 months: +25

10-12 months:+15

13-18 months:+5

19-24 months:-5

earlier: -15

28 weeks:+25

27 weeks:+40

Number of orders (Frequency) Number of orders or purchases multiplied by 6 7·6 =

+42 7·6 = +42

Ø turnover of last 3 orders (Monetary)

≤ 25 €: +5

26-50 €: +15

51-100 €: +25

101-150 €: +35

151-200 €: +40

>200 €: +45

99 €: +25

102 €: +35

Number of returns

0-1: 0

2-3: -5

4-6: -10

7-10: -20

11-15: -30

>15: -40

0: 0

0: 0

Number of mailings since last purchase

Per main catalogue: -12

Per special catalogue: -6

Per mailing: -2

1 mailing:-2

1 mailing:-2

Total score 115 140

Source: adapted from [Link and Hildebrand 1993, p. 49, Neckel and Knobloch 2005, p. 211]


- 40 -

However, this frequently used RFM model with sharp classes is very problematic. Customers

Brown and Miller have the same values for four variables (see Table 9): both have an initial

score of +25, the same number of orders (+42), returns (0) and mailings (-2). Nevertheless,

there are two little differences: the date of Brown’s last purchase was 28 weeks ago (class: ‘7-9

months’), Miller’s last one was 27 weeks ago (class: ‘≤ 6 months’), and the average turnover of

Miller (102 €; class: ‘101-150 €’) was 3 € higher than this one of Brown (99 €; class: ’51-100 €’).

Since Miller ordered only one week earlier than Brown, and Miller only had a 3 € higher turn-

over, they are classified in two different classes and Miller (140 points) receives 25 points

more than Brown (115 points). Although Miller and Brown had the same purchase behaviour,

Miller had a 20% higher score than Brown and is may managed and treated differently by the

mail order company. In addition, the opposite can happen too: although two customers have

totally different purchase behaviour, they may have the same number of points.

These examples show how imprecise and inadequate methods with sharp classes can be.


To derive a fuzzy Recency Frequency Monetary method, the following example of the RFM

approach shall be considered. The attribute or linguistic variable ‘recency’ is described by the

terms ‘long ago’ and ‘very recent’, the attribute ‘frequency’ by the terms ‘rare’ and ‘frequent’

and the third attribute, ‘monetary value’, is divided into the terms ‘low value’ and ‘high value’.

The contexts of the three qualifying attributes can be defined as follows:

The attribute domain of ‘recency’ is defined by [0, 730] days and is divided into the equiva-

lence classes [0, 365] days for ‘very recent’ last repurchase and [366, 730] days for a repur-

chase which took places a ‘long (time) ago’.

The domain of ‘frequency’, measured by the number of repurchases, is [0, 100] and has

two equivalence classes: [0, 9] for ‘rare’ repurchases, and [10, 100] for ‘frequent’ ones.

The domain of the third variable ‘monetary value’ is defined by [0, 200] euros of customer

average turnover, divided into the equivalence classes [0, 99] euros for ‘low value’ and into

an average turnover of [100, 200] euros, which is considered as ‘high value’.

Table 10: RFM Method: Definition of Classes and Scores

RFM attributes, [equivalence classes] and (terms) Recency Frequency Monetary value Class

[Days last purchase] (Term) [# of purchases] (Term) [Ø turnover] (Term) Scores

C1 [0, 365] Very recent [10, 100] Frequent [0, 99] Low value 70 p C2 [0, 365] Very recent [0, 9] Rare [0, 99] Low value 40 p C3 [366, 730] Long ago [10, 100] Frequent [0, 99] Low value 30 p C4 [366, 730] Long ago [0, 9] Rare [0, 99] Low value 0 p C5 [0, 365] Very recent [10, 100] Frequent [100, 200] High value 100 p C6 [0, 365] Very recent [0, 9] Rare [100, 200] High value 60 p C7 [366, 730] Long ago [10, 100] Frequent [100, 200] High value 50 p C8 [366, 730] Long ago [0, 9] Rare [100, 200] High value 20 p


- 41 -

In Table 10, eight classes (C1 to C8) are defined by the RFM attributes and terms. For each

class, a number of points are defined: the more recent and frequent a customer purchases

products and the higher the monetary value is, the more points are assigned to the customer.

The four customers are now sharply RFM classified and receive following scores (Table 11):

Table 11: Sharp RFM Scoring of Customers

RFM attributes, [values of the customers] and (terms) Recency Frequency Monetary value Customer Class [Days last purch.] (Term) [# of purch.] (Term) [Ø turnover] (Term)

Score

Smith C3 378 Long ago 11 Frequent 92 Low value 30 p Ford C4 723 Long ago 7 Rare 12 Low value 0 p Brown C5 342 Very recent 13 Frequent 117 High value 100 p Miller C5 14 Very recent 38 Frequent 193 High value 100 p Here again, the same problem of sharp classification emerges: although Smith and Brown

have quite similar values, they are classified in different classes and to Smith are assigned 70

points less. In contrast, Miller is classified with 100 points in the same class as Brown, al-

though Miller’s performance is much better. By the definition of fuzzy sets and membership

functions, the RFM classification space becomes fuzzy (compare Figure 27b).

Figure 27: Sharp (a) and Fuzzy (b) RFM Method

With fuzzy classification, the scores of the customers can be calculated more exactly and fairly

(for the calculation see Table 12 and Appendix 1). Classified fuzzily, 43.3 points are assigned

to Smith, what is better than the 30 points in the sharp classification. Ford is also classified

better (sharp: 0; fuzzy: 12.9 points), whereas Brown (51.8 points) is classified much lower in

comparison to the sharp classification (100 points). Only Miller, who performs as good as pos-

sible in ‘recency’, ‘frequency’ and ‘monetary value’, still has the same 100 points.

010

1

μ rare μ frequent

μ ve

ry re

cent

μ

lang

ago

Ford

Smith Brown

μ low value

C3) 12.86 C4) + 0 = 12.9 points

C5) 100 points

μ high value

C1) 8.97 C2) + 3.86 C3) + 3.35 C4) + 0 C5) + 17.52 C6) + 8.06 C7) + 7.70 C8) + 2.35 = 51.8 points

Miller

b) Fuzzy RFM Method

C1) 10.43 C2) + 5.22 C3) + 5.20 C4) + 0 C5) + 9.71 C6) + 5.02 C7) + 5.73 C8) + 1.99 = 43.3 points

Frequency

Miller C5) 100 points Brown

C5) 100 pointsSmith

C3) 30 points Ford C4) 0 points

a) Sharp RFM Method

Recency

Mon

etar

y va

lue

high value

low value

ver

y re

cent

l

ong

ago

frequent rare

Recency

Frequency

Mon

etar

y va

lue

C4) 0 p C3) 30 p

C2) 40 p

C5) 100 p

C1) 70 p

C6) 60 p

C8) 20 p C7) 50 p Smith


- 42 -

Table 12: Fuzzy RFM Scoring of Customers

Mnorm(Oi│Ck) Fuzzy Calculated RFM Score Class Corresponding membership functions to each class Ford Smith Brown Miller Ford Smith Brown Miller

C1 μvery recent μfrequent μlow value 70 0 0.14902 0.12810 0 0 10.43 8.97 0 C2 μvery recent μrare μlow value 40 0 0.13051 0.09641 0 0 5.22 3.86 0 C3 μlong ago μfrequent μlow value 30 0.42857 0.17340 0.11167 0 12.86 5.20 3.35 0 C4 μlong ago μrare μlow value 0 0.57143 0.15235 0.08296 0 0 0 0 0 C5 μvery recent μfrequent μhigh value 100 0 0.09708 0.17521 1 0 9.71 17.52 100 C6 μvery recent μrare μhigh value 60 0 0.08360 0.13437 0 0 5.02 8.06 0 C7 μlong ago μfrequent μhigh value 50 0 0.11456 0.15395 0 0 5.73 7.70 0 C8 μlong ago μrare μhigh value 20 0 0.09948 0.11732 0 0 1.99 2.35 0

Total 1 1 1 1 12.86 43.3 51.81 100

The membership degrees to the different classes and the total score of the fuzzy RFM model

can be considered as an important indicator, how likely a customer is to purchase again by

the company and how loyal a customer is. According to the membership degrees to the differ-

ent classes, the customer manager could define incentives to animate customers to buy

again, to purchase more frequent by the company, and at a higher monetary value:

Customers who have recently bought the company’s products (C1, C2, C5, and C6) might

receive a thank-you letter or an e-mail for buying from the company and for the confidence

shown. In addition, these mailings may contain personalised product recommendations or

cross- and up-selling offers (like the e-mails from Amazon, for instance).

To customers who bought or buy very frequently (C1, C3, C5, C7), the company should

send mailings (e.g. catalogues) to inform about existing or new products and special offers.

For customers with high monetary values a one-time discount is offered. The discount in

percent (C5: 20%, C7: 10%, C6 and C1: 5%) also depends on the frequency and recency of

purchases. Figure 28a shows the combination of the RFM incentives.

Figure 28: Fuzzy RFM Incentives

μ ve

ry re

cent

μ

lang

ago

b) Fuzzy RFM Incentives a) Definition of RFM incentives

Frequency

Mon

etar

y va

lue

high value

low value

ver

y re

cent

l

ong

ago

frequent rare

Recency

C2)

C4) C3)

C6) C5)

C8) C7)

C6) Low discount (5%) 2 reminders 2 mailings per month

C5) High discount (20%) 2 reminders 8 mailings per month

C8) No discount (0%) 1 reminders 1 mailings per month

C7) Medium discount (10%) 1 reminders 4 mailings per month


C1) Low discount (5%) 2 reminders 8 mailings per month

C4) No discount (0%) 0 reminders 0 mailings


Miller C5) Brown

C5) Smith

C3) Ford C4) Ford

Smith

Miller

010

1

μ rare μ frequent

μ low value

μ high value

Recency

Frequency

Mon

etar

y va

lue

Brown Discount: 6.4% 2 reminders 4 mailings/month

Smith Discount: 4.3% 1 reminders 4 mailings/month

Miller Discount: 20% 2 reminders 8 mailings/month

Ford Discount: 0% 0 reminders 1 mailings/month

C1)


- 43 -

Classified sharply, Smith receives no discount, although he has similar values like Brown, who

receives a high discount (20%). As a result, Smith may feel discriminated or betrayed by the

company and he does his shopping elsewhere. Miller might feel the same way too, because

he has the same discount as Brown, although his performance is much better than Brown’s.

With fuzzy classification (Figure 28b), fair incentives can be offered and personal discounts

calculated. Smith receives a discount of 4.3% (0.097·20 + 0.115·10 + 0.084·5 + 0.149·5) and

Brown a discount of 6.4% (0.175·20 + 0.154·10 + 0.134·5 + 0.128·5). Miller still has the maxi-

mum of 20%. With personalised discounts the company offers the same incentives for all customers to repurchase by the company. In addition, Smith and Brown receive an adequate

number of reminders according to their purchase behaviour and are informed about the com-

pany and its products. The service of a reminder or mailing should be based on permission

marketing, so that customers are not annoyed about undesired information or advertising.

Fuzzy scoring models in combination with a fuzzy hierarchical classification are not limited to

three attributes, as discussed in this example of RFM analysis. By the assignment of weighted

scores to any number of qualitative or quantitative criteria, scoring models can be applied

flexibly and independently to different kinds of analyses in CRM.

Both approaches, sharp and fuzzy scoring methods, are confronted with the same problem of

weighting or assigning the "right" number of points to each class. Since the weighting of the

classes’ score (e.g. C1: 70 points) is often done subjectively and intuitively, RFM methods are

not as objective as they pretend to be. However, scoring and RFM methods are more valid,

significant and objective, if certain valuation rules are considered (see [Plinke 1997, p. 140]).

In addition, the weighting and scoring of the classes can be improved using empirical data

and regression analyses (e.g. logistic regression).

However, a fuzzy RFM analysis can be undertaken without knowing the optimal weights and

scores: if the manager wants to know all clients who are most likely to repurchase, he queries: classify customers from RFM with recency is very recent and frequency is frequent and monetary value is high value

This fuzzy classification query would return all absolute membership degrees of the customers

to the class C5 (Ford: 0%, Smith 25.3%, Brown 45.7% and Miller 100%).

With the fuzzy scoring approach, a company can identify, classify, analyse, score, profile,

evaluate and segment customers and customer groups according to their value for the com-

pany (compare Section 5.1: Fuzzy Customer Segmentation with Important Indicators).

In addition, fuzzy scoring and profiling of customers facilitates the selective communication to

target customers or fuzzy segments, the adoption and customisation of the marketing mix and

an effective management of customer relations. As a result, the allocation of limited res-

sources, like a marketing or CRM budget, can be optimised.

- 44 -

Chapter 4

Analytical Customer Relationship Management

Chapter 4: Analytical Customer Relationship Management

- 45 -

4.1 Customer Relationship Management (CRM) 4.1.1 Overview

In Chapter 2, the fuzzy classification approach and fCQL (fuzzy Classification Query Language)

were described and applied to different management tools in Chapter 3.

This Chapter will now discuss the application of fuzzy classification and the fuzzy Classification

Management Tools (fCMT) to the field of Customer Relationship Management (CRM).

Fuzzy classification could be categorised as a multidimensional data analysis method. Conse-

quently, the fuzzy classification approach with fCQL are especially suited for the different tasks

and processes of analytical Customer Relationship Management (aCRM).

However, it is a principal task of aCRM to analyse, classify, evaluate and segment customers

and their performance according to their value for the company in order to manage and maxi-

mise customers like any other asset of the company [Blattberg et al. 2001].

That means: analytical customer relationship management needs a comprehensive and holistic

Customer Performance Measurement (CPM; content of Section 4.2). To measure customer

performance comprehensively, CPM has to define, collect, record and manage an adequate

customer data and Customer Performance Indicators (CPI; discussed in Section 4.3).

The fuzzy classification and evaluation of customer performance indicators is an important con-

dition to fuzzily segment customers (content of Chapter 5) and to define and implement ap-

propriate strategies for a customer or for a customer segment.

These processes and the structure of Chapter 4, Analytical Customer Relationship Manage-

ment, and Chapter 5, Fuzzy Customer Segmentation, are shown in Figure 29.

Figure 29: Structure of Chapter 4 and 5

Fuzz

y cl

assi

ficat

ion

(Cha

pter

2)

Man

agem

ent t

ools

(fC

MT)

(Cha

pter

3)

Fuzz

y m

arke

t seg

men

tatio

n (S

ectio

n 5.

2) Customer Relationship Management (CRM) (Section 4.1)

Customer Performance Measurement (CPM) (Section 4.2)

Operational CRM

Strategic CRM

Analytical CRM (Chapter 4)

Fuzzy customer performance analysis, classification and evaluation

Fuzzy customer segmentation with important indicators (Section 5.1)

Customer Performance Indicators (CPI) (Section 4.3)

Action planning and implementation of customer strategies

appl

ied

to

appl

ied

to


- 46 -

4.1.2 The Development to the Customer Oriented Company

After the change from seller’s to buyer’s markets about 40 years ago, the quality orientation,

Total Quality Management (TCM), became increasing important in companies. Hence, until

the end of the 1980s, firms focused mainly on the improvement of manufacturing processes, on

the product quality and the integration of in-house product data (see Figure 30).

Thereafter, companies started to align the product quality to the customers needs and, within

the scope of Business Process Re-Engineering (BPR), to redesign business processes. Due

to information systems, such as Sales Force Automation (SFA) or Computer Aided Selling

(CAS), business processes were customised and production or sales data integrated.

Since the 1990s, many companies have to align the complete value chain and the organisation

to customers, especially in high competitive markets. With the rise of Customer Relationship

Management (CRM), many companies became more customer-oriented. Thereby, the sale of a

product or service is not considered as a business transaction only, but as the beginning of a

customer relationship. In addition, a consistent view of customers in enterprises often is only

possible by integrating all customer relevant data in a CRM information system.

Figure 30: The Development to the Customer-Oriented Company

Within the field of CRM, several trends can be observed: CRM became more and more system-

atic, individual, differentiated and, due to innovations in IT, highly technical in the last years. The

profit orientation and the controlling of CRM and CRM processes also increased.

The main ideas of customer (relationship) management are not new, but as old as people doing

business. Concepts like customer satisfaction and retention, which have been discussed since

the 1970s, are still the basic for long-term and profitable relationships. However, the importance

and potential of CRM is highly increasing due to technological innovations and software in IT.

CRM Trends: increasing IT-applications systematisation individualisation differentiation profit orientation

Focus: customer retention

Focus: customer satisfaction Customer orientation

Customer Relationship Management (CRM)

Process orientation

Business Process Re-Engineering (e.g. SFA/CAS)

1980 1990 2000 Source: adapted from [Sieben 2001, p. 298, Rapp 2005 p. 42]

Integration of all customer- related data

Integration of the production and sales data

Integration of the in-house product data

Quality orientation

Total Quality Management (TQM)


- 47 -

4.1.3 CRM and Customer Management

CRM is only one, but an important approach in the domain of customer management and

marketing. Figure 31 illustrates the development of the different fields in customer management.

Most of them are promising for business applications for fuzzy classification.

According to [Belz and Bieger 2004, 2006], the weight and diffusions of the different approaches

can vary strongly over different industries.

Figure 31: Applications of Fuzzy Classification in the Domain of Customer Management

The primary focus of this thesis is the application of fuzzy classification in the domain of Cus-

tomer Relationship Management (CRM), which includes customer segmentation, acquisitions

and retention (compare Figure 31).

Especially in saturated markets, which is the case in many industries, customer retention is in-

creasingly important, but also difficult.

Mass customisation and one-to-one marketing are particularly interesting for fuzzy classification

as it facilitates to identify and segment customers according to their individual behaviour to-

wards the company.

1980 1990 2000 1970 2010

Mass marketing

Market segmentation

Lifestyle segments

Customer retention

Customer relationship management

Direct marketing

Key account management

Customer acquisition

One-to-one marketing

Global account management

Small customer management

Partnership systems

Weight of different approaches in customer management

t

Primary focus of the paper Secondary focus Not specified Source: adapted from [Belz and Bieger 2004, p. 56]

Mass customisation

Promising fields for fuzzy classification

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc


- 48 -

Figure 32: Fuzzy Classification and Individual Marketing

Fuzzy classification allows treating customers or customer segments more individually. There-

fore, fuzzy classification is not only promising for customised marketing (for instance for the

fuzzy classification of products, their design or configuration) and relationship marketing (e.g.

to classify less loyal customers in order to launch loyalty programs), but also for individual

marketing like mass customisation, customer profiling or for database marketing (in Figure 32).

The idea of one-to-one marketing, which originally trace back to [Peppers and Rogers 1993,

1997], is to learn from the customer ("learning relationship") and to improve the relationship and

knowledge about the customer and his needs by cooperation (for instance through customers’

feedback or observations) in order to treat customers individually. One-to-one (or individual)

marketing and mass marketing differ in many topics. Table 13 contrasts these two concepts and

shows the possible applications for fuzzy classification in one-to-one marketing.

Table 13: Mass Marketing vs. One-to-One Marketing and Applications for Fuzzy Classification

Mass Marketing One-to-One Marketing Applications for Fuzzy Classification Average customer Individual customer Performance measurement of individual customers All customers Profitable customers No segmentation Customer segmentation

Fuzzy customer analysis and segmentation (microsegmentation), key account management

Customer anonymity Customer profile Fuzzy classified customer profile Standard product Customised marketing offeringMass production Customised production

Fuzzy classification based customisation, individualis- tion, configuration or personalisation of products/services

Mass distribution Individualised distribution - Mass advertising Individualised message One-way message Two-way message

Individualised campaigns and messages based on fuzzy segmentation of customers or (target) segments

Mass promotion Individualised incentives Fuzzy calculation of personal prices, offers or discounts Economics of scale Economics of scope - Share of market Share of customer Optimised share of customer/wallet by cross-/up-selling Customer attraction Customer retention Retention programs for based on fuzzy retention portfolios Short-term focus Long-term focus -

Source: adapted from [Muther 1998, p. 54], [Payne 2006, p. 8]

3) Relationship marketing

(Focus: customer)

4) Mass/transactional marketing

(Focus: sale)

2) Customized marketing

(Focus: product)

1) Individual marketing

(Focus: product & customer)

Learning relationship Database marketing

Self-customisation

Mass customisation

Product design

Product configuration …

Standard products

Transaction Repurchases

…

…

Partnership Loyalty programs

Customer integration

Inte

nsity

of c

usto

mer

rela

tions

hip

In

divi

dual

isat

ion

of th

e pr

oduc

t/ser

vice

Retention programs

One-to-one marketing

Possible marketing concepts for fuzzy classification

Mikromarketing

Satisfaction controlling

Personalisation

Customer profiling fc

fc

fcfc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

Source: adapted from the scheme of [Kotler and Keller 2005, p. 155]


- 49 -

4.1.4 Definition of CRM

The term Customer Relationship Management (CRM) is defined differently. Put simply, CRM

is “information-enabled relationship marketing” [Payne 2006, p. 11]. A rather technical definition

considers CRM as “the automation of horizontally integrated business processes involving front

office customer points (marketing, sales, service and support) via multiple, interconnected deliv-

ery channels [Peelen 2005, p. 3].” Other definitions describe CRM as a process that addresses

all aspects of identifying customers, creating customer knowledge, building customer relation-

ships, and shaping their perceptions of the organisation and its products. The Gartner Group

describes CRM as „an IT enabled business strategy, the outcomes of which optimise profitabil-

ity, revenue and customer satisfaction by organising around customer segments, fostering cus-

tomer-satisfying behaviours and implementing customer-centric processes” [Payne 2006, p. 5].

Figure 33: The Use of Fuzzy Classification in Typical Tasks of CRM

Service

Marketing

Sales

Finance

Sales

Service

Information

Offer

Purchase

After sales

Phase model Subject area Use of fuzzy classification (fc) Processes (tasks)

Market research

Demand analysis

Public relations

Advertising

Sales promotion

Campaigns

Information

Individual campaign

Customising

Tender preparation

Price calculation

Financing

Contract conclusion

Order processing

Handling of payment

Delivery

Startup

Installation

Maintenance

Complaints

Repairs

Customer service

Customer care

Customer retention Source: adapted from [Schumacher and Meyer 2004, p. 39]

fc can be used for the identification, classification, analysis and development of promising markets or

market segments (Chapter 5.2 Market Segmentation).

fc can help to classify and analyse needs and demands of customers as well as to develop and

provide corresponding products and services.

fc enables to realise individual campaigns e.g. for important, valuable customers (key account man-

agement) and for new or imperilled customers.

Through adequate fuzzy customer segmentation, customers (or segments) can be informed specifically (depending on their needs or their purchase history).

Database marketing and fc support the configuration, customisation, individualisation or personalisation

of products, services and communication.

Thanks to fc, prices, estimates, accounts, paying conditions (e.g. warranties) and contracts can be calculated and offered more flexibly and personally

in order to set incentives to improve buying attitudes, retain customers and to drive customer equity & profit.

fc facilitates to bind customers with individual loyalty programs or incentives, like fuzzy personal accounts.

By fuzzy classifying customers or customer segments, customer care can be realised more precisely and

focused (e.g. on key, periled, unsatisfied customers).

With fc, services and customer care can be aligned more personally, customer- and segment-oriented (e.g. additional services for strategic key customers).

With fc important customer information, indicators or values of finance and accounting (e.g. customer

turnover, contribution margins, profits, equity, customer lifetime value) can be better analysed and evaluated

in order to manage customer relations effectively.

fc allows advertising and promoting more effectively and efficiently by addressing the target customers or segments with adequate communication instruments.

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc

fc


- 50 -

Figure 33 shows typical tasks and processes in CRM, mostly promising for fuzzy classification.

In CRM, a distinction is often made between strategic, analytical and operational CRM (see for

instance [Meier 2003b, Hippner and Wilde 2004a, 2006, Peelen 2006]). Therefore, CRM appli-

cation architectures (in Figure 34) often contain a strategic, analytical and an operational layer.

Figure 34: CRM Application Architecture

At the strategic layer at the bottom, strategies and operational objectives for customer acquisi-

tion, retention and recovery are defined as well as add-on selling strategies.

The operational layer at the top, with the components Customer Relationship Communications

(CRC) and Customer Relationship Operations (CRO), is in touch with the customers. Custom-

ers communicate und interact with the company through different channels or media.

The analytical layer in the middle, analytical Customer Relationship Management (aCRM),

includes an Information (IS) or a Management Information System (MIS), a Relational Database

Management System (RDBMS), a customer Data Warehouse (DWH), CRM software, data min-

ing tools or other CRM service systems, which contain customer data. The query process with

fCQL deals with the RDBMS and DWH within the layer of aCRM, as shown in Figure 34.

Customers Miller Smith

Source: adapted from [Hippner et al. 2001b, p. 201, Meier 2003b, p.10, Neckel and Knobloch 2005 p. 45]

Customer Relationship Communications (CRC)

Customer Relationship Analytics (CRA)

CRM service systems

Relational Database Mana- gement System

Computer Integrated Manufactering (CIM)

Personal contact

Phone

WWW

E-Mail

Mass media Mobile technologies

Data mining & OLAP

CRM software

External data

Enterprise Ressour-ce Planning (ERP)

Supply Chain Management (SCM)

Mail

Closed loop architecture

Customer contact points

Process-based strategic planning Fuzzy customer segmentation and targeting Customer acquisition strategies and programs Customer loyalty/retention or recovery strategies and programs Add-on selling (cross-/down-/up-selling) strategies

Stra

tegi

c an

d op

erat

iona

l obj

ectiv

es

Cus

tom

er P

erfo

rman

ce M

easu

rem

ent (

CP

M)

Stra

tegi

c C

RM

A

naly

tical

CR

M (a

CR

M)

Ope

ratio

nal C

RM

Fron

t offi

ce

Bac

k of

fice

DWH

Fuzzy queries & gathering of data

Customer Relationship Operations (CRO)

Marketing

Sales

After sales

fc

fc

fc

fc

Ford

Brown

Management Infor-mation System (MIS)

Service

fCQLtoolkit


- 51 -

Figure 35: Mobile Analytical Customer Relationship Management

To undertake fuzzy queries with fCQL, the desktop user (e.g. a sales or customer manager) can

access the RDBMS or DWH over a conventional, stationary network (intranet; see Figure 35). In

the case of mobile analytical CRM, a field manager has wireless connection to the customer

data over mobile networks and the internet, using a laptop, PDA or a smart phone. With mobile

technologies, a customer or sales manager has flexible access to relevant and actual customer

information (anytime – anywhere). In addition, electronic and mobile CRM improve operational

activities, for instance to prioritise customer care or services, and to improve time management.

4.1.5 Objectives and Key Points of CRM

CRM is not an end in itself. Its main objectives are to increase customer profits and customer

equity, which is defined here as the monetary value of a company’s current customers.

In order to augment the profitability of customer relationships, a number of requirements have to

be fulfilled: first of all, the company has to focus and align its processes towards its customers,

in order to identify, recognise and analyse the needs and wants of the target customers, or of a

segment, regarding the company and its products or services. Second, the products or services

offered should meet the customers’ expectations and create a visible value for them.

Customer value is defined here as the value creation from a customer perspective, that

means the entire product, services, personnel and image values that a buyer receives from a

marketing offer (compare [Kotler et al. 2005, p. 464] and Subsection 5.1.6: Customer Value).

Consequently, the term ‘customer value from the customer perspective’ is not to be confused

with the term ‘customer value from the company perspective’ or Customer Lifetime Value (CLV).

However, customer value contributes significantly to customer satisfaction, the fulfilment of the

customer’s needs or expectations. Increasing satisfaction and commitment raises customer

loyalty and customer retention [Reichheld and Sasser 1990, Boulding et al. 1993, Anderson

and Sullivan 1993, Rapp 1995, Oliver 1996, Bolton 1998, Mital and Kamakura 2001, Homburg

2006, Krafft 2007, Bruhn 2007, Bruhn and Homburg 2005, 2006, Homburg and Krohmer 2006].

Mobile CRM

Customer data (customer information)

classify from with

Internet

RDBMS

DWH

CRM

eCRM

Cus

tom

ers

Intranet Stationarynetworks

fCQL & SQL queries

Ser

ver

WLAN LAN EDGE

HSDPA UMTS (E)GPRS

Laptop Bluetooth

Tablet PC PDA

Smart phone Cell phone

Desktop

Mob

ile

netw

orks

Stationary user; sales or

customer manager

Mob

ile u

ser;

sale

s or

cu

stom

er m

anag

er

Analytical CRM

Operational CRM Mobile Analytical CRM


- 52 -

Empirical research show significant positive correlations of satisfaction (or loyalty) and

operating margins like sales, turnover and cash flows [Bolton 1998, Rust et al. 1995],

Return on Investment (ROI, e.g. [Anderson et al. 1997], for a review see [Zeithaml 2000]),

Return on Customer Investment (ROCI; for instance [Hayman and Schultz 1999]),

Return on Relationship (ROR; see for example [Gummesson 1999]),

accounting returns [Ittner and Larcker 1998]

firm value and a firm’s raw market value [Ittner and Larcker 1996] and

shareholder value and stock prices [Anderson et al. 2004, Fornell et al. 2006].

Increasing customer satisfaction and thereby higher retention secures future revenues [Fornell

1992, Rust et al. 1995, 2005] and reduces costs of future customer transactions, such as

ones associated with sales, transport, service and payment (see for instance [Reichheld 1993,

1996, Reichheld and Sasser 1996, Srivasatava et al. 1998]). Consequently, sales, turnover

and net cash flows are higher. At the same time, greater customer retention indicates a more

stable customer base that provides a relatively predictable source of futures revenue due to

repurchases and add-on sellings In addition, recommendations of satisfied customers lead

to lower acquisition costs and to additional revenues from new customers. As a result, the

long-term profits of the firm increase, their volatility tends to be lower, and the risk associated

with the anticipated cash flows is reduced. Therefore, market value and shareholder value

increase (see [Anderson et al. 2004, p. 173]). All the discussed empirical findings of research

on CRM can be summarised in a CRM success chain (compare Figure 36).

Figure 36: CRM Success Chain

Customer attitude

Behavioural intentions

Customer behaviour

Customer results

Customer investments

Marketing management Accounting & finance

Customer needs

Customer expectations

Perceived value & price/performance

Image Quality of relationship

Recommendationsword of mouth

Commitment & attachment

Enthusiasm factors

Switchingcosts

Cu. Profit, Contr. margins

Market share

Turnover, sales, cash flow

Customer costs (sales, marketing,…)

Share-holder

Market value

Customer equity,CLV

Customer value

Customer orientation

Customer retention

Customer loyalty

Customer satisfaction

Product and service management

Customer Performance Measurement (CPM) & Customer Relationship Management (CRM)

Firm & shareholder value management

Performance measurement and management

Perceived product or service quality

Repurchases

Share of wallet

Cross-/up-selling

Network effects

Price sensitivity


- 53 -

Each indicator of the success chain can be assigned to a psychological construct of a simple

model: products and services ( ; customer investments) influence customer attitudes ( ),

such as the perceived customer value and the perceived product, service or relationship quality,

which effect customer satisfaction. Customer attitudes towards the company and its products

and services lead to behavioural intentions ( ), for instance, the intentions to repurchase the

same or other products from the company. Customer behaviour ( ) is whether they really

repurchase (or not), cross-buy or recommend products to friends, for instance. Their behaviour

leads to customer results ( ), e.g. customer turnover, contribution margins and profits.

It is a strategic task for firms to analyse, control and manage the different customer indicators

proposed in the CRM success chain. This is a challenge for both marketing management, on

the left side of Figure 36, and for managerial accounting or finance on the right.

Such performance measurement and management should not only consider organisational,

production- or process-related and financial indicators, measures, numbers, metrics or figures,

but, in particular, market- and customer-related performance indicators.

The "small CRM success chain" in Figure 36, or similar success chains, discussed for instance

in [Bruhn 2007], contain important constructs of marketing (for instance customer satisfaction,

loyalty, retention). However, this small CRM success chain does not provide enough customer

information and customer performance indicators for a customer performance measurement

system, since the aggregation level of information is too high.

Consequently, more indicators have to be defined and taken into account.

That means that the "small CRM success chain" has to be enlarged to a "big CRM success

chain" with a comprehensive number of different indicators.

“Only what get’s measured is managed and done.”

According to this management slogan, CRM and marketing cannot manage its clients, if cus-

tomers cannot be described, measured and evaluated. Typically, these processes are based on

customer data and information. Consequently, CRM requires a number of relevant customer

criteria and customer performance indicators, as discussed in the following section.


- 54 -

4.2 Customer Performance Measurement 4.2.1 Definitions

Entering rather new territory of research, this section develops a new concept of customer per-

formance measurement and management by considering a comprehensive number of different,

but relevant customer performance indicators. First of all, since there are no common definitions of several basic expressions for this purpose,

the different terms have to be derived from related expressions and are defined as follows.

Performance is, among others, the observable or measurable behaviour of a person [Oxford

1989]. Derived there from, customer performance can be defined:

Customer Performance (CP) is a measurable monetary or non-monetary result of a customer

relationship in a defined period.

To operationalise ‘performance’, performance measurement is used. However, performance

measurement is a topic which is often discussed but rarely defined [Neely et al. 1995].

Performance Measurement (PM), for the purpose of this work, is understood as the measure-

ment, analysis and communication of performance [Wettstein 2002, p. 19]. Applied on customer

performance, customer performance measurement can be defined as follows:

Customer Performance Measurement (CPM) is the acquisition, analysis and the evaluation of

performance-related customer information.

However, CPM has many facets and can be characterised by following dimensions of cus-

tomer performance measurement: the unit, format, planning, interval, time, alignment, connec-

tion with incentives, CRM layer and the aggregation level. The values of these dimensions can

be very different, as shown in Figure 37.

Figure 37: Dimensions of Customer Performance Measurement

2) Format

6) Connection with incentives 7) CRM layer

variation

improvement

1) Unit

operational

strategic

5) Alignment

3) Planning interval

Customer Performance Measurement (CPM)

qualitative

quantitative

non-monetary

monetary

ex post ex ante high low 8) Aggre-

gation level

internal

4) Time

external

short-term

long-term

Source: adapted from [Müller-Stewens 1998, p. 37, Gleich 2001, p. 11, Reinecke 2004, p. 48]

1 month

5 years

lagging-indicator leading-indicator

low – high

1 – 100

customer loyalty

customer turnover

CRM, employee

customer strategies customer

sale ∆X↓

X↑

one customer all customers, customer segment

discrete member-ship functions continuous functions


- 55 -

Performance management is “a philosophy which is supported by performance measurement”

[Lebas 1995, p. 44]. That means that performance measurement is a part of performance man-

agement. However, performance management is understood as a management function

(analysis, planning, implementation and control) and encompasses the methodologies, metrics,

processes, software tools and systems that manage the performance [Cokins 2004].

Consequently, customer performance management is defined here as a CRM function and

encompasses all customer performance indicators, instruments, processes, software tools and

systems of CRM to analyse and control customer performance.

Finally, a Performance Measurement System (PMS) is defined as “a set of metrics used to

quantify both, the effectiveness and the efficiency of actions” [Neely et al. 1995, p. 5]. However,

a Customer Performance Measurement System (CPMS), as an instrument of analytical

CRM, is defined here as an information or CRM system used to analyse, evaluate, control and

communicate customer performance and customer strategies.

4.2.2 Processes of Customer Performance Measurement

Following the discussed CRM success chain, each company should select and define an ade-

quate number of important customer performance indicators ( in Figure 38; the term customer

performance indicator is defined and discussed in the next subsection).

Figure 38: Processes of Customer Performance Measurement

However, the chosen indicators have to meet several requirements ( ) of performance meas-

urement theory and practice (see for instance [Bruhn 2003a, p. 89, Reinecke 2004, p. 329,

Vollmuth 2006, p. 23, Probst 2006, p. 18]).

Customer Performance Indicators (CPIs) in particular have to be

customer-specific (causality and traceability of CPIs to a customer)

relevant (high importance of CPIs for customer relationship management and marketing)

well-defined (precise definition and description of CPIs)

available (availability and accessibility of CPIs in an IS, MIS or within a company)

measurable (measurability of CPIs with continuous or discrete membership functions)

objective (intersubjective traceability of CPIs)

Collection of customer data

Evaluation & segmentation

Fuzzy clas-sification

Analysis of results

Selection of customer performance indicators

Requirements for indicators

Action planning


- 56 -

comprehensible (plausibility and understandability of CPIs)

up-to-date (up-to-dateness of CPIs)

user-friendly (user-friendliness of CPIs)

accepted (acceptance of CPIs by employees and managers of CRM or by management) comparable (comparability of CPIs over time or with competitors)

economical (proportion of the costs of data collection to benefit of CPIs)

decision-/target-oriented (importance of CPIs for action planning and decision making)

reliable (high reliability of CPIs and low random error) and

sensitive (CPIs should be suited for an early warning system or a trigger mechanism).

However, after the selection of customer performance indicators, employees or managers of

CRM, marketing and IT have to collect, record, store and administrate customer data ( in

Figure 38) in a Customer Performance Measurement System (CPMS) or in an information sys-

tem. Since information management mostly has to classify customer data, for instance as ‘low’,

‘medium’ or ‘high’ performance of indicator X, one- or multidimensional fuzzy classification ( )

is a crucial step in the processes of customer performance measurement.

This thesis suggests to classify all customer performance indicators fuzzily in order to avoid

misclassifications and to improve the quality of customer evaluations and CRM.

Fuzzy classification of customer data is a precondition to analyse ( ), evaluate and to fuzzily

segment customers ( ). Based on different fuzzy classified and company-relevant customer

performance indicators, customers can be analysed, segmented by CRM or marketing.

As a result, customer specific CRM or marketing strategies and actions can be planned, com-

municated and realised ( ) in order to improve customer performance on an individual and on a

corporate level.


- 57 -

4.3 Customer Performance Indicators 4.3.1 Definitions

To simplify and aggregate customer data, different indicators and measures are used for per-

formance measurement. Consequently, customer performance measurement has to rely on an

adequate number of customer performance indicators.

A Customer Performance Indicator (CPI) is defined here as a customer-related monetary or

non-monetary criterion (a measure, metric, index, figure or ratio) about customer performance.

In this thesis, Customer Performance Indicators (CPIs) are categorised under six categories:

1) Customer Performance Indicators for Revenue and Profitability (CPIP)

2) Customer Investment Indicators (CII)

3) Customer Relationship Indicators (CRI)

4) Customer Recommendation Indicators (CReI)

5) Customer Information Indicators (CInfI) and

6) Customer Cooperation Indicators (CCI).

Appendix 4 (pp. 136ff) shows a collection of 170+ Customer Performance Indicators (CPIs),

with the definition, operationalisation, measuring units and the purpose of each indicator.

In addition, each indicator can be either assigned to the construct ‘customer investment’, ‘cus-

tomer attitude’, ‘customer behavioural intention’ ‘customer behaviour’ or ‘customer result’.

Most indicators can be measured at an individual level (*) to analyse the performance of a sin-

gle customer, or as an aggregate indicator (∑*) to evaluate the performance of all customers or

of a segment. Depending on the company, the size of the enterprise, market or on the industry,

some indicators are more important, other less. However, the important indicators are called:

Key Customer Performance Indicators (KCPI; symbol: ) are defined here as important

monetary or non-monetary customer performance indicators reflecting critical success factors

for customer relationships. In contrast, leverages (symbol: ) are influenceable or controllable

variables to manage successfully CRM processes in order to improve customer performance.

Customer (key) performance indicators, have different functions. They are used for the

comparison of the target and the actual customer performance

operationalisation of objectives and the ‘management by objectives’ of marketing and CRM

prioritisation and realisation of objectives and decisions referring to the customers and CRM

justification of decisions or actions (indicators, data and facts instead of acts on instinct)

communication and management of customer-related tasks and processes

controlling and monitoring of customer and market performance and for action planning.


- 58 -

4.3.2 Categories of Customer Performance Indicators

As mentioned, the CPIs are categorised under six different categories:

1. Customer Performance Indicators for Revenue and Profitability (CPIP) contain criteria,

which are dependent on the customer’s past, actual or future performance regarding his

purchases, e.g. purchased products (I 20 in Appendix 4), number, intensity, frequency of

orders (I 17, I 26, I 28), volume (I 21) or value (I 23, I 29) of purchases and repurchases (I 56),

turnover or sales (I 31), share of wallet (I 39), cross- and up-selling (I 50 - I 53),

payment history (I 66), e.g. punctuality of payment (I 63) and creditworthiness (I 68), and

profitability (I 78), e.g. customer contribution margins I to IV (I 69 - I 73), gross or net profit

(I 75, I 76) and growth of profit (I 77),

customer equity (I 79) and Customer Lifetime Value (CLV; I 80).

As shown in Figure 39, CPIPs, for instance customer profit, often are operational, monetary,

quantitative, lagging and internal indicators to improve. However, each CPIP has different

characteristics, depending on the task and context of customer performance measurement.

Figure 39: Measurement Dimensions of the CPIP ‘Customer Profit’ 2. Customer Investment Indicators (CII) consider all customer costs or investments incurred

in a customer relationship in a defined period. Some examples:

customer acquisition, retention and recovery costs (I 87-I 92), customer efficiency (I 108)

transaction (I 94), sales (I 95), service (I 97), communication (I 99), contact costs (I 100)

marketing costs per customer (I 101) or total customer costs (I 102)

"return-on-"ratios like Return on Sales (ROS; I 103), Return on Customer (ROC; I 104),

Return on Relationship (ROR; I 105), Return on Customer Satisfaction (I 106), Return on

Retention (I 107), Return on Marketing (ROM; I 109) or Return on Investment (ROI; I 110).

3. Customer Relationship Indicators (CRI) are mainly qualitative, non-monetary, long-term,

external and strategic indicators about a customer relationship (see Figure 40), such as

2) Format


variation

improvement

1) Unit

operational

strategic

5) Alignment


Example of CPIP: customer profit (I 76)

qualitative

quantitative

non-monetary

monetary


gation level

internal

4) Time

external

short-term

long-term


- 59 -

customer perceived product (I 118, I 119), service (I 120) or relationship (I 149) quality, per-

ceived price-performance ratio (I 121, I 122) and customer value (I 123, I 124)

customer satisfaction in general (I 125 - I 127), with a product (I 128) or service (I 129) and

customer commitment (I 131), attachment (I 132), loyalty (I 134) or retention (I 137 - I 142).

Figure 40: Measurement Dimensions of the CRI ‘Customer Loyalty’

4. Customer Recommendation Indicators (CReI) assess, for instance, the number (I 152)

and intensity (I 153) of customer recommendations (Figure 41) or positive word of mouth.

Figure 41: Measurement Dimensions of the CReI ‘Number of Customer Recommendations’

5. Customer Information Indicators (CInfI) evaluate e.g. customer contacts (I 159), responses

(I 169), number/quality of suggestions (I 163), complaints (I 164) or his product expertise (I 168).

6. Customer Cooperation Indicators (CCI) inform about a customer’s intention to cooperate

(I 171) or his expertise for cooperation (I 172) or other indicators for cooperation.

Figure 42 shows the coherence and relations of the 170+ Customer Performance Indicators

(CPIs) in a "big CRM success chain". However, the cause-and-effects of the big success chain

are ideal, theoretical causalities to illustrate the context and purpose of the different indicators.

2) Format


variation

improvement

1) Unit

operational

strategic

5) Alignment


Example of CRI: customer loyalty (I 134)

qualitative

quantitative

non-monetary

monetary


gation level

internal

4) Time

external

short-term

long-term

2) Format


variation

improvement

1) Unit

operational

strategic

5) Alignment


Example of CReI: number of recommendations (I 152)

qualitative

quantitative

non-monetary

monetary


gation level

internal

4) Time

external

short-term

long-term


- 60 -

Figure 42: CRM Success Chain with 170+ Customer Performance Indicators

Figure 42: CRM Success Chain with 170+ Customer Performance Indicators (For Definitions and Details on Indicators see Appendix 4, pp. 136ff)

Pur

chas

e D

ata

Customer needs

(Fulfilment of) customer expectations (I 125)

Perceived value or utility (I 123)

Corporate Image (I 115)

Commitment (I 131) and attachment (I 132)

Enthusiasmfactors

Market share(I 43,44)

Customer turnover/sale (I 31-35)

Total customer costs (I 102)

Shareholder value

Market value

Customer equity (I 79) and Customer Lifetime Value (I 80)

Customer retention (I 137-139; 142-144,147)

Customer satisfaction (I 126-129)

Customer value (I 124)

Customer loyalty (I 134)

Cash flows (I 30)

Brand Image (I 116)

Perceived relation-ship quality (I 149)

Recommendations, word of mouth (I 151f)

Awareness(I 111,114)

Ad aware-ness (I 112) Payment history

(I 63-68)

Market pene-tration (I 48,49)

Number of new customers (I 4)

Number of cust-omers (I 7-9)

# of migrated customers (I 10)

Return on sales (I 103)

Marketing costs (I 101)

Retention costs/efficiency

(I 89,90)

Recovery costs/efficiency

(I 91,92)

Contribution margins (I 69-74)

Customer pene-tration (I 38)

Contact costs (I 100)

Transaction costs (I 94)

Service costs (I 97)

Distribution & logistic (I 95)

Return on marketing (I 109)

Return on investment (I 110)

Return on rela-tionship (I 105)

Customer Performance Indicators for Revenue and Profitability (I 1-86)

Customer Investment Indicators (I 87-110)

Customer Relationship Indicators (I 111-150)

Customer Information Indicators (I 157-170)

Customer Cooperation Indicators (I 171-173)

Product knowledge/ expertise (I 113,168)

Intention to cooperate (I 171)

Expertise for cooperation (I 172)

Possible cooperation topics (I 173)

Intention to dialog, response rate (I157,169)

Consulting/helpdesk intensity (I 158)

Customer initiated contacts (I 159,162)

Number/quality of suggestions (I 163)

Number/quality of complaints (I 164)

Complaint satisfaction (I 165)

Demand of technical service (I 166)

Number of returns (I 167)

Customer profit (I 75-78)

Customer efficiency (I 108)

Return on customer (I 104)

(After) sales costs (I 95,98)

Repurchase intentions (I 55)

# of orders (I 17)

Cross- & up- selling (I 50-54)

Purchased products (I 20)

Purchased volume (I 21)

Purchase intensity (I 26)

Price-performance-ratio (I 122)

Customer orientation (I 117)

Value drivers

Intention/intention to switch (I 140,141)

Switching costs (I 145; e.g. functional/contractual I 146)

Trust (I 133)

# of recovered customers (I 11)

Order value (I 23)

Monetary value (I 29)

Recency(I 27)

Frequency(I 25,28)

Growth (sales I 35, profit, m.share I 45)

Share of wallet (I 39-41)

Involvement (I 130)

Communicat- ion costs (I 98)

Firm value

Intensity of re-lationship (I 148)

Repurchases(I 56,57)

Heavy usage index (I 22)

Perceived product quality (I 118,119)

Perceived service quality (I 120)

Key Customer Performance Indicator (KCPI) Leverage

Price sensitivity (I 60,61)

Acquisition costs/efficiency

(I 87,88)

Customer acquisition (I 6)


- 61 -

4.3.3 Customer Performance Indicators in Business Practice

Business practice focuses mainly on financial performance indicators, ratios, numbers and

figures like sales (1. in Figure 43), cash flows, contributions margins (3.), profits (4.) or market

share (2.; see [Ambler 2000, Rudolf-Sipötz 2001, Reinecke 2004, Barwise and Farley 2004,

Farris et al. 2006, Krafft 2007]). Consequently, most companies evaluate financial, easily

measurable customer performance indicators, such as turnover (27./34.) or contribution

margins (26./35.). Qualitative criteria like customer satisfaction (5./33.), loyalty or retention

(14.) or the perceived service and product quality (17./18.) are also often measured indicators

in daily business. In addition, CRM practice collects customer data like the number of new

customers (23.) or complaints (32.) or the customers’ payment history (25./36.).

Figure 43: Empirical Results of Customer Performance Measurement in Companies

Marketing & sales performance indicators1. Sales (turnover and/or volume; I 21,31-35) 2. Market share (I 43) 3. Gross margins or gross contributions (I 69-74) 4. Net profit (I 76) 5. Customer satisfaction (I 125-129) 6. Return on sales (I 103) 7. Sales growth relative to market growth (I 45) 8. Relative market share (I 44) 9. Sales per employee 10. Relative price (compared with main competitor)11. % of new customers relative to portfolio (I 5) 12. Proportion of sales generated by new products13. Distribution level 14. Customer loyalty or retention (I 134-147) 15. Proportion of own customers to potential total 16. Awareness (I 111-113) 17. Perceived service quality (I 120) 18. Perceived product quality (I 118,119) 19. Brand equity (brand strength) 20. Customer equity or lifetime value (I 79,80) 21. Capital turnover 22. Commitment (I 131; purchase intention, I 55) Indicators for customer acquisition 23. Number of new customers (I 4) 24. Potential of new customers (I 84) 25. Payment history of new customers (I 67) 26. Contribution margin of new customers (I 74) 27. Share of new customer’s sales to total (I 31) 28. Poached customers from competitors (I 3) 29. Number of prospective customers (I 1) 30. Sales or turnover of first-time buyers (I 32) 31. Acquisition costs (I 87) Indicators for customer retention 32. Number of complaints (I 164) 33. Customer satisfaction (I 125-129) 34. Customer turnover or sales (I 31,33-35) 35. Contribution margins (I 69-73) 36. Payment history (I 63-66,68) 37. Share of wallet (I 39,41) 38. Frequency (I 28) 39. Duration of relationship (I 147) 40. Churn rate (I 144) 41. Product portfolio of customers (I 58,59) 42. Retention costs (I 89)

96%65%

76%

83%50%

63%

53%53%52%50%

43%38%

20%25%

36%41%

40%

20%17%

32%

91%78%

81%

91.5% 68%

57% 73%

66%64%65%

78%

55%

[Reinecke 2004] [Rudolf-Sipötz 2001]

[Ambler 2000]

[Barwise and Farley 2004]

Empirical studies

[Reinecke 2004]: n = 419/276

Percentage of usage

[Rudolf-Sipötz 2001]: n = 155

Percentage of usage

[Ambler 2000]: n = 200

Percentage of usage

[Barwise and Farley 2004]: n = 697

Percentage of usage

79%

63%

59%

66%

63%

81%

85%

77%

81%

40%

64%

70%

Source: [Ambler 2000, p. 8], [Rudolf-Sipötz 2001, p. 74], [Reinecke 2004, p. 153, 274, 294], [Barwise and Farley 2004, p. 259]

77%

15%

46%

43%54%

64%

36%

39%20%

37%

18%

55%

62%

28%

35%12%

36%

22%

54%

44%

24%

27%

36% 52%

- 62 -

Chapter 5

Fuzzy Customer Segmentation

Chapter 5: Fuzzy Customer Segmentation

- 63 -

5.1 Fuzzy Customer Segmentation with Important Indicators 5.1.1 Definitions

Each company has different kinds of customers. Customers have different needs, different

expectations about products or services, different buying or using behaviour of purchased

products and different information or communication behaviour. In addition, from a company

point of view, customers perform differently. In the words of [Peppers and Rogers 1997, p. 31]:

“Customers have different needs from a firm, and they represent different valuations to a firm.”

Consequently, it makes sense to divide customers into groups with similar characteristics or

performance. This process of dividing different customers into homogeneous groups, is called

customer segmentation. Based on customer segmentation, the company can adapt the

marketing mix to the different customer segments, e.g. by mass customised products or by

segment specific communication. Customer-oriented definitions of customer segmentation

are often used in literature. [Bruhn 2003a, p. 88] states: “Customer segmentation refers to a

classification of all potential and current customers, based on their market reaction, into inher-

ently homogeneous but externally heterogeneous sub-groups (i.e. customer segments)”.

However, a company could follow another approach by asking: who are our most interesting,

valuable, profitable or promising customers? This company-oriented interpretation of cus-

tomer segmentation has another context and other objectives: the identification and separation

of the most valuable customers for the company. Customer criteria, which can be influenced

by the company, are called endogenous criteria. The following endogenous groups of seg-

mentation criteria can be applied for customer segmentation [Bruhn 2003a, p. 90]:

Economic endogenous criteria, for example customer sales or turnover (I 31), contribution

margins (I 69-I 74), profits (I 75-I 78), customer equity (I 79) or customer lifetime value (I 80,I 81)

Behavioural endogenous criteria, like customer retention (I 137), recommendations (I 152),

add-on-selling (I 50-I 53), repurchases (I 56), punctuality of payment or creditworthiness (I 68)

Psychological endogenous criteria, for instance the perceived service, product, or relation-

ship quality (I 118-I 120; I 149), customer value (I 124), satisfaction (I 126) or loyalty (I 134).

Based on these endogenous criteria, for example ‘valuable’ and ‘not valuable’, ‘promising’ and

‘non-promising’, or ‘satisfied’ and ‘dissatisfied’ customers can be separated from each other.

Since a customer should not be labelled sharply only ‘valuable’ or ‘not valuable’, customer

segmentation and evaluation should be done fuzzily.

Fuzzy customer segmentation is defined here as the fuzzy classification of the company’s

current customers into similar, fuzzy segments, using different customer performance indica-

tors (i.e. economic, behavioural and/or psychological endogenous segmentation criteria).


- 64 -

5.1.2 Fuzzy Clustering

As defined in Section 2.1, any class or a combination of classes of a one-, two- or multidimen-

sional fuzzy classification can be defined as a fuzzy segment.

However, it was assumed that the classes where the data is classified in are known and fix. In

reality, data usually do not fit in given classes. Since the classes should be adapted to the

data and not the other way round, the classes have to be identified first. This identification can

be done by cluster analysis. Cluster analysis is a multivariate data analysis method to reduce

complexity of a dataset by grouping elements or objectives to groups, i.e. to clusters.

In contrast to classification, cluster analysis or clustering does not rely on predefined classes.

[Han and Kamber 2006, p. 383] define: “The process of grouping a set of physical or abstract

objects into classes of similar objects is called clustering”. To derive fuzzy classes from the

data, non-disjunctive, fuzzy cluster algorithms can be used, as shown in Figure 44.

Figure 44: Fuzzy Methods of Cluster Analysis

Considering non-disjunctive methods of cluster analysis based on fuzzy logic, different algo-

rithms are discussed in data mining literature. [Höppner et al. 1999], for instance, examine the

fuzzy-c-means-, Gustafson-Kessel-, Gate-Geva-algorithm and fuzzy-shell-clustering-methods.

The most famous fuzzy algorithm, the fuzzy-C-Means-algorithm (FCM) is an advancement of

the hard c-means. In contrast to the hard c-means, where objects are assigned clearly to clus-

ters, the FCM defines membership degrees between 0 and 1 for each object to the clusters.

The FCM assumes that all clusters have approximately the same shape and size.

The Gustafson-Kessel-algorithm, in contrast, takes the structures of the data into account.

The Gate-Geva-algorithm is an extension of the Gustafson-Kessel-algorithm and considers

additionally the size and density of the clusters. The less important fuzzy-shell-clustering-

methods describe mathematically the shape and distance of data to the geometric structure.

Finally, the Fuzzy-Maximum-Likelihood-Estimation-algorithm (FMLE-algorithm) is based

on a probabilistic evaluation (see for instance [Timm 2002]).

Agglomorative Divisive

Disjunctive (sharp) methods

Partitional methods

Non-disjunctive methods

Overlapping methods

Methods of cluster analysis

Fuzzy methods

FMLE-methodGustafson-KesselFuzzy-c-means

Hierarchical methods

Mixture models

Gath-Gewa


- 65 -

Figure 45: Sharp (a) and Fuzzy (b) Customer Segments

In Figure 45a, for example, five sharp segment are defined (Ss1 to Ss5) by a sharp cluster al-

gorithm. Ford and Smith both belong entirely to segment 2 (Ss2), Brown and Miller to segment

1 (Ss1). Such a sharp segmentation can be problematic, as the examples of Smith and Brown

show: although they have nearly the same values of the indicator 1 and 2, they are classified

in two different segments. If segment 2 (Ss2) would be defined a little bit larger (dashed circle

in Figure 45a), Brown would belong to the same segment 2 as Smith. If segment 1 (Ss1) would

be enlarged (dotted circle), both Smith and Brown belong to segment 1.

Using a fuzzy clustering algorithm, for instance the fuzzy-C-Means-, Gustafson-Kessel, Gate-

Geva- or the Fuzzy-Maximum-Likelihood-Estimation-algorithm, the borders between the dif-

ferent segments become fluent (Figure 45b). That means that the sharp borders between the

segments disappear. Fuzzy classified elements can belong to several segments at the same

time: Ford belongs to three segments (48% to Sf2, 28% to Sf4 and 24% Sf5); Smith and Brown

belong both nearly half to segment 1 and 2. With fuzzy customer segmentation and the defini-

tion of fuzzy segments, customers’ positions can be shown more exactly. The probability of

misclassifications is reduced and customers segments can be managed more flexible.

Since the logic of fuzzy segmentation does not depend on the exact form of the segments or

the segmentation, and to simplify matters, a normal fuzzy classification with four classes (as

defined in Section 2.1) will be used for fuzzy customer segmentation in the following sections.

5.1.3 Methods of Customer Segmentation In literature on marketing, different approaches and methods of customer segmentation

and evaluation are discussed (for a review see [Meffert 2000, Rudolf-Sipötz 2001, Reichold

2006, Günter and Helm 2006, Homburg and Krohmer 2006, Bauer et al. 2006, Krafft 2007]).

Indicator 2

In

dica

tor 1

μ high indicator 1 μ low indicator 1

μ lo

w in

dica

tor 2

μ h

igh

indi

cato

r 2

00

1

1

b) Fuzzy Customer Segments (Sfi)

Indi

cato

r 1

Brown 53% Sf1 47% Sf2

Indicator 2

Ss2

Ss3

Ss4

Miller 100% Sf1

Smith 48% Ss1 52% Sf2

Ford 48% Sf2 28% Sf4 24% Sf5

a) Sharp Customer Segments (Ssi)

Sf2

Sf1 Sf3

Sf4 Sf5Ford 100% Ss2

Ss5

Smith 100% Ss2

Miller 100% Ss1Ss1

Brown 100% Ss1


- 66 -

Figure 46: Methods of Customer Segmentation

In Figure 46, a distinction is made between one-dimensional customer evaluation or segmen-

tation methods (for instance the ABC analysis or the present value method of finance), and

multidimensional methods (for example scoring methods and portfolio analyses discussed in

Chapter 3, or other mathematical models).

One-dimensional methods consider, on the one hand, monetary measures, such as customer

turnover, contribution margins, profit or Customer Lifetime Value (CLV), and, on the other

hand, non-monetary indicators like customer value, satisfaction, loyalty or retention.

Fuzzy classification can be categorised as a multidimensional approach of customer evalua-

tion and segmentation, which can be applied to other methods, as fuzzy portfolio analysis,

fuzzy scoring model or as fuzzy ABC analysis (discussed in Chapter 3). In addition, both

monetary and non-monetary customer performance indicators can be classified fuzzily.

In order to evaluate customers comprehensively and to manage them successfully, different

customer data have to be collected and integrated in a corporate information system. Cus-

tomer data often are displayed in an information dashboard, accessible for the responsible

customer managers and employees of CRM, marketing or of the executive board.

The information dashboard of relevant customer data in Figure 47 shows a categorisation

of following five categories of customer data:

1) Profile data contain conventional customer data like address data, i.e. the customer’s

identification number, name, mail and e-mail address, phone numbers, or demographic

data as customer’s age, gender, civil and family status, education, profession, etc.

In addition, profile data may include psychographic data as a customer’s hobbies, inter-

ests, lifestyle and his buying or payment behaviour.

2) Purchase data inform about a customer’s purchase history, i.e. all purchase dates, his

number, volume or value of purchased products or services, his purchase recency and

frequency or the duration of the customer relationship.

Methods of customer evaluation and segmentation

One-dimensional Multidimensional

(Fuzzy) portfolio analysis Customer value

Satisfaction Loyalty (ladder) Duration of CR


Application

static dynamic

(Fuzzy) scoring methods

fcfcfc

(Fuz

zy) A

BC

ana

lysi

s Customer performance indicators

fc

Non-monetary

Profits

Turnover

Equity, CLV

Contribution margins

Monetary

Retention, etc.


- 67 -

Figure 47: Information Dashboard of Relevant Customer Data

3) Customer Performance Indicators (CPIs) hold a number of relevant performance indica-

tors for the customer relationship, profitability and investment as discussed in the last Sec-

tion 4.3. Following 12 Subsections 5.1.5 to 5.1.16 discuss some of these indicators shown

in the middle of the ‘information dashboard of relevant customer data’ in Figure 47.

4) Service data contain information about the services a customer demanded, e.g. the num-

bers of general requests, support, the demand of technical services or the number and the

quality of complaints or returns.

5) Contact data (action and reaction data) include all information about the number, dates,

types, channels, intensity, frequency or costs of communication activities or of contacts

with a customer. In Appendix 4, most service and contact data are categorised under cus-

tomer information indicators. Some of the profile, purchase, service or contact data is generated directly at the points of

customer contact, for instance in the front office (customer relationship communication and

operations in the architecture of Figure 34).

However, it is the challenge and principal task of analytical CRM to collect, integrate, store,

analyse, administrate and to manage all existing customer data.

Most of the customer data of the dashboard shown in Figure 47 can be used for the fuzzy cus-

tomer segmentation process and for the definition of fuzzy customers segments:

The customer manager may want to segment the customers according to their profile data,

to their demographic or psychographic data (e.g. all young customers with similar lifestyles).

3) Customer Performance Indicators (CPIs)

Customer relationship indicators


Subsection 5.1.5 Perceived product/ service quality

Customer value Subsection 5.1.6


Subsection 5.1.7 Customer attachment/commitment

Customer loyalty Subsection 5.1.8

Customer retention Subsection 5.1.9

Customer Lifetime Cycle

Performance indicators for profitability

RFM method Repurchases

Subsection 5.1.10 Add-on-selling

Subsection 5.1.11 Share of Wallet

Chapter 5.1.12 Price sensitivity Sales or turnover

Subsection 5.1.13 Contribution

Subsection 5.1.14 Customer profits

Subsection 5.1.15 Customer equity or Customer Life- time Value (CLV)

Subsection 5.1.16

Customer investment indicators

Acquisition costs Retention costs Recovery costs Administration costs Transaction costs Communication costs Service costs Contact costs (After) sales costs Total customer costs Marketing costs Return on’s: - sales (ROS) - customer satisfaction - customer (ROC) - relationship (ROR) - Marketing (ROM) - Investment (ROI)

2) Purchase data Dates of purchase Purchased products Purchased volume Heavy usage index Frequency Recency Monetary Ø order value Duration of RS

4) Service data General customer requests

Demands of technical services

Number and quality of complaints

Number of returns

5) Contact data Date of contact Number, types, channel, inten-sity, costs of communication

Frequency of actions

Customer adviser

1) Profile data

Adress data

#, Name, address, e-mail, phone, etc.

Profile data Demographic data (age, civil & family status, education)

Psychographic data (interests, lifestyle)

Buying and pay-ment behavior

Inverse: promising performance indicators for fuzzy customer segmentation

Source: adapted from [Homburg and Sieben 2003, p. 427]

0 0.1 0.2

0.3 0.4 0.5

0.6 0.7 0.8

1

0.468 0.9 0 0.1 0.2

0.3 0.4 0.5

0.6 0.7 0.8

1

0.739 0.9

fc

fc

0 0.1 0.2

0.3 0.4 0.5

0.6 0.7 0.8

1

0.964

margins


- 68 -

Regarding the purchase data, all customers, for instance, with similar purchase frequencies

or recency can be chosen and addressed for a marketing campaign.

Another campaign may be launched in the segment of dissatisfied customers to know the

reasons for dissatisfaction and to improve satisfaction.

A specific retention program may be realised for the segment ‘key accounts’; for this, all

customers with high turnover or profits are selected.

Depending on the task, situation or strategy of CRM, different customer data, criteria or per-

formance indicators can be used for ‘fuzzy customer classification’, for ‘fuzzy customer

segmentation’ and for ‘customer evaluation, scoring and profiling’ (see Figure 48).

Figure 48: Context of Fuzzy Customer Segmentation

Fuzzy classified customer data, CPIs and fuzzy segments allow to evaluate, qualify, score or

profile customers more exactly and fairly. This enables, for instance, an improved Key Ac-

count Management (KAM), i.e. the management of a company’s most important customers

and the formulation of customer or segment specific loyalty or retention programs.

Campaign management is more effective and efficient by adapting communication, informa-

tion or offers to the different needs and expectations of different fuzzy customer segments.

In addition, an early warning system can warn the customer manager, if the performance of

a customer, of a fuzzy customer segment or of the whole customer portfolio is rapidly decreas-

ing in order to launch counter measures of strategic or operational CRM and marketing.

Stra

tegi

c C

RM

A

naly

tical

CR

M

Fuzzy customer segments, CPIs

Evaluations & profiles

Fuzzy clusters and segments

Fuzzy classified

data, CPIs

Fuzzy customer segments

Profile, purchase, service, contact data; CPIs

Customer Relationship Communication (CRC) and Operations (CRO)

Early warning system (trigger mechanism) Campaign

management

Information demand

Marketing and sales

Retention programs

Early warning system(trigger mechanism)

Fuzzy customer classification

CRM strategy development

Customer evaluation, scoring and profiling

Key account management

Evaluations, scores & profiles

Fuzzy classified data, CPIs & fuzzy customer segments

CPIs and KPIs

Counter measures

Requirements

Counter measures Ope

ratio

nal C

RM

Customer data; Customer Performance Indicators (CPIs)

Customer strategies

Fuzzy customer segmentation



- 69 -

5.1.4 Selected Indicators for Fuzzy Customer Segmentation

The way customers can be segmented fuzzily, will now be discussed with some examples of

customer performance indicators of the ‘information dashboard of relevant customer data’

(Figure 47) and of the ‘small CRM success chain’. These indicators are chosen because they

can be considered as important Key Customer Performance Indicators (KCPIs) or leverages.

The following indicators in Figure 49 are discussed in the Subsections 5.1.5 to 5.1.16.

Figure 49: Selected Indicators of the CRM Success Chain for Fuzzy Customer Segmentation

By combining different customer performance indicators of the small (Figure 49) or big CRM

success chain (Figure 42; Appendix 4), fuzzy customer portfolios analysis allow managers

to identify and manage fuzzy customer segments. To drive the CRM success chain, a com-

pany has to maintain the good performance of all customers with high membership degrees to

the promising classes CX-1 in Figure 50. In addition, strategic CRM has to define adequate

strategies to improve the performance of customers classified in the less promising classes.

Figure 50: Driving the CRM Success Chain by Optimising Fuzzy Classified Portfolios

Repurchases ( 5.1.10)

Share of wallet ( 5.1.12)

Add-on selling ( 5.1.11)

Network effects

Price sensitivity

Profits ( 5.1.15)

Market share

Customer turnover, sales, cash flow

( 5.1.13)

Customer costs

Share-holder

Market value

Customer equity,CLV

( 5.1.16)


( 5.1.7)

Customer value

( 5.1.6)


( 5.1.5)

Customer retention ( 5.1.9)

Customer loyalty

( 5.1.8)

Customer needs

Customer expectation

Image Quality of relationship

Recom-mendations

Commitment& attachment

Enthusiasmfactors

Switchingbarriers

Key Customer Performance Indicator (KCPI) Leverage

Perceived quality

Perceived value/utility Contr. margins

( 5.1.14)ROR, ROCI

C1-2)

C1-1)

C1-4)

C1-3)

Customer value

Cus

tom

er o

rient

atio

n

C6-2)

C6-1)

C6-4)

C6-3)

Market share

Cus

tom

er e

quity

C2-2)

C2-1)

C2-4)

C2-3)


Cus

tom

er v

alue

C5-2)

C5-1)

C5-4)

C5-3)

Customer equity or profit

Cus

tom

er re

tent

ion

C3-2)

C3-1)

C3-4)

C3-3)

Customer loyalty or retention

Cus

tom

er s

atis

fact

ion

C4-2)

C4-1)

C4-4)

C4-3)

Repurchases or add-on selling

Cus

tom

er re

tent

ion


- 70 -

5.1.5 Customer Orientation

To succeed or simply to survive in today’s marketplace, a company has to be customer ori-

ented or customer-centred [Kotler and Keller 2005]. Such a customer-driven company fo-

cuses on customer needs and wishes in designing its marketing strategies and on delivering

superior value to customers. According to Kotler, only high customer orientation allows a cus-

tomer-value-creating marketing, the second step of the success chain.

Customer orientation (I 117) can be defined as the focus on meeting the needs and wants of

a company’s customers. Customer orientation is a comprehensive, continuous analysis and

evaluation of individual customer expectations as well as their internal and external transfor-

mation in output. In addition, it includes all interactions in the context of the relationship mar-

keting concept with the objective to establish stable and profitable customer relationships

[Bruhn 2003b, p. 15]. Customer orientation can be also considered as the proximity to cus-

tomers [Homburg 2000].

However, customer orientation is not for free, but causes considerable costs and investments,

such as administration (I 93), transaction (I 94), service (I 97), contact (I 100) or communication

costs (I 99). Since maximal orientation is mostly not affordable, a company should determine

an optimal degree of customer orientation by analysing its costs and benefit in order to im-

prove the efficiency of customer orientation (see Figure 51a). On an individual level, different

customers may need different degrees of orientation. The company has to focus on customers

with a high information potential, i. e. with an intensive response rate (I 169), a high intention to

dialog (I 157) and/or good cooperation behaviour (I 171 - I 173; see Figure 51b).

Figure 51: Fuzzy Cost-Benefit Analysis (a) and Portfolio of Customer Orientation (b)


Customer value


Customer loyalty

Customer retention

Customer equity

Market value

Share-holder

μ high costs

Benefit of customer orientation

μ low costs

μ lo

w b

enef

it

0 0

1

1

μ

high

ben

efit

Cos

ts o

f cus

tom

er o

rient

atio

n

μ high orientation

C2)

Augment customer

orientation

C1)

Retain high customer

orientation

C4)

Retain low customer

orientation

C3)

Reduce customer

orientation

Response rate (I 169; intention to dialog, I 157)

μ low orientation

μ lo

w re

spon

se ra

te

00

1

1

μ hi

gh re

spon

se ra

te

Cus

tom

er o

rient

atio

n (I

117)

b) Fuzzy Customer Orientation Portfolio a) Fuzzy Cost-Benefit Analysis

C2)

Efficient orientation (low costs,

high benfits)

C1)

Costly orientation (high costs, high benefit)

C3)

Inefficient orientation (high costs, low benefit)

C4)

Insufficient orientation (low costs, low benefit)


- 71 -

5.1.6 Customer Value

Customers choose these products or services, which have the highest value for them. That

means “customers are value-maximisers, within the bound of search costs and limited knowl-

edge, mobility and income. They form expectations of value and act upon them. Then they

compare the actual value they receive in consuming the product to the value expected, and

this affects their satisfaction and repurchase behaviour” [Kotler et al. 2005, p. 463].

The customer delivered value is defined as the difference between total customer value and

total customer cost of a marketing offer, i.e. the ‘profit’ to the customer. Total customer value

is thereby the total of the entire product, services, personnel and image values that a buyer

receives from a marketing offer and total customer cost is the total of all the monetary, time,

energy and psychic costs associated with a marketing offer [Kotler et al. 2005, p. 464].

According to Kotler’s explanations, customer value (I 124) is defined here as value creation

from a customer perspective. This customer point of view of customer value is common in

CRM literature (see [Rust et al. 2000, Eggert 2006, Helm and Günter 2006, Bauer et al. 2006,

Belz and Bieger 2006]). In addition, a distinction is made between the basic value (e.g. tech-

nical-functional value) and the added-value (socio-psychological value). It is a strategic task

to identify, create, maintain and control important customer value drivers and to manage

them by portfolios analysis. Since customer value contributes to customer satisfaction, drivers

of customer value and satisfaction often go hand in hand. A selection of customer value and

satisfaction drivers is listed in Table 14. More value factors are shown in Appendix 3.

Table 14: Drivers of Customer Value and Satisfaction

Customer Value Drivers Price, monetary sacrifices Product innovation and creativity Company’s reliability Quality (of products or services) Individualisation or personalisation Company’s (core) competencies Technical values Accuracy, flexibility, efficiency Company’s expertise / knowledge Functional values Coordination and organisation Company’s experience Psychological or emotional values Reputation, image, status, prestige Company’s network Social values (added-value) Problem identification Goals, goal compatibility Relationship values Empathy and trust Bonds (structural, social, economic)

Customer Satisfaction Drivers Service specific criteria of customer satisfaction Product specific criteria of customer satisfaction Reaction to customer problems Ability to meet specifications Reactivity and flexibility Failure, error and rejection rate Availability of contact persons Availability of products Convenience and accuracy of documentation Constant quality and reproducibility Reliability Sales assistance Frequency of delivery Product literature Delivery within the agreed time Technical assistance Terms of payment and financing Maintainability and longevity Handling of complaints Completeness of delivery Service quality and innovation Product training Warranties Product development

: Nominal fuzzy classification possible : Discrete fuzzy classification possible

Source: adapted from [Leino 2004, pp. 45f, Belz and Bieger 2004, p. 101, Waibel and Käppeli 2006, p. 172]


Customer value


Customer loyalty

Customer retention

Customer equity

Market value

Share-holder


- 72 -

5.1.7 Customer Satisfaction

Customer satisfaction is considered as a Key Performance Indicator (KPI) of corporate suc-

cess. From the company’s point of view, customer satisfaction can be considered as the

company’s ability to fulfil the economical, emotional and psychological needs of its customers.

From the customer’s point of view, customer satisfaction as an attitude can be defined as

the fulfilment of customers’ requirements or needs. Customer (dis)satisfaction with a product (I

128), service (I 129) or the company itself (I 126), depends on the customer’s comparison of ex-

pectations (I 125) and perception of experience. Dissatisfied customers often migrate to

competitors and to their offers. This does not only result in lower sales, but often leads to

negative word of mouth. Empirical studies show that only 5% of the dissatisfied customers

complain, but they share their experiences in average with ten other persons and 90% do not

buy the company’s products anymore [Waibel and Käppeli 2006, p. 171].

Several methods are used to track satisfaction: companies can install complaint or suggestion

systems (e.g. customer hotlines, suggestion boxes, complaint forms) or make periodic satis-

faction surveys. Thereby it is important to ask customers about their intention to recommend

a product or service (I 151; “would you recommend this product or service to friends?”).

With fuzzy classification, the assignment of a customer to one class or several classes is more

precise. This enables, for instance, the analysis of the customer’s overall (dis)satisfaction in

relation to the satisfaction with a certain product X (see Figure 52a). Regarding the overall

satisfaction and the customer’s willingness or intention to switch, four different basic strategies

are defined in Figure 52b. With fuzzy classification, customers can be assigned differentiated

to the basic strategies and can be managed according to their satisfaction level.

Figure 52: Examples of Fuzzy Classified Customer Satisfaction Portfolios


Customer value


Customer loyalty

Customer retention

Customer equity

Market value

Share-holder

μ satisfaction with X

C2)

Dissatisfaction with product

X only

C1)

Overall satisfaction

C4)

Overall dissatisfaction

C3)

Satisfaction with product

X only

Customer satisfaction (I 126)

μ dissatisfaction with X

μ di

ssat

isfa

ctio

n

0 0

1

1

μ sa

tisfa

ctio

n

μ willing to switch

C2)

Retain satisfaction

C1)

Bind customers

C4)

Reduce dissatisfaction

C3)

Improve satisfaction

Customer satisfaction (I 126)

Willi

ngne

ss/in

tent

ion

to

sw

itch

(I 14

0,I 1

41)

μ unwilling to switch

μ di

ssat

isfa

ctio

n

00

1

1

μ sa

tisfa

ctio

n

Sat

isfa

ctio

n w

ith p

rodu

ct X

(I 1

28) a) b)


- 73 -

5.1.8 Customer Loyalty

It is well documented in literature on marketing that higher customer satisfaction leads usually

to higher loyalty. Customer loyalty (I 134) is defined here as the behaviour customers exhibit,

when they make frequent repurchases of products of the company or plan to do so, and if they

intend to recommend the company to others. Different methods and approaches are dis-

cussed in literature, how to measure loyalty. Directly measurable indicators for loyalty are:

number of repurchases (I 56), repurchase intentions (I 55) and cross buying (I 50 - I 54)

intention to recommend (I 151) and number of recommendations (I 152)

duration and intensity of the customer relationship (I 147, I 148)

share of wallet (i.e. the percentage of a customer’s requirements of a product or service

category that are fulfilled by a particular product or service of the company; I 38 - I 41)

RFM method discussed in Section 3.5: “Recency of last purchase” (I 27), “Frequency of

purchase” (I 28) and “Monetary value” (I 29) or the

loyalty ladder, which classifies customer loyalty into different steps ( - in Figure 53)

Figure 53: Loyalty Ladder


Customer value


Customer loyalty

Customer retention

Customer equity

Market value

Share-holder

Smith

Source: adapted from [Kreutzer 1992, p. 335, Neckel and Knobloch 2005, p. 28, Krafft 2002, p. 66]

Repeat buyer (I 8)

Sequence buyer (I 8)

Regular buyer (I 9)

First-time buyer (I 6)

Prospective customers (I 1)

Product interesent

Advertising contacted

Intesive user

Usage notice

Light user

Usage potential

Afte

r-buy

ing

phas

e Pr

e-bu

ying

pha

se

0 1

μ us

age

pote

ntia

l

Potential buyer (I 1)

Customers (I 7)

Contacted

User

Potential user

Ford

Miller

Brown

Potential to contact

μ re

gula

r buy

er


- 74 -

According to the loyalty ladder, regular customers or regular buyers (I 9) are the most loyal

customers. Most companies in business practice analyse their regular customers (see [Rudolf-

Sipötz 2001, pp. 67ff]): the main criteria for the definition of a regular customer are customer

turnover (I 31), contribution margins (I 69 - I 73), customer penetration (I 38), share of wallet (I

39), number and rhythm of purchases (I 20; I 25; I 56) and the duration of the relationship (I 147).

To augment loyalty is essential to enrich customer relationships, e.g. by a high degree of

added-value, trust, exchange of information or cooperation ([Homburg 2001]; see Figure 54a).

Another widely used way to bind customers and improve future financial performance at the

firm level is to augment switching costs (Figure 54b). Switching costs place a technical, func-

tional, contractual, financial, psychological or emotional burden on the customer, who intends

to switch away from the offered products or services, or from the company itself, to a competi-

tor. Switching costs include all costs of determining the relationship to the company and set-

ting up a new one until it provides the same benefit (see [Büschken 2004, Dikolli et al. 2006]).

Figure 54: Examples of Fuzzy Classified Customer Loyalty Portfolios Source: adapted from [a) Homburg 2001, p. 50; b) Büschken 2004, p. 15; c) Bauer et al. 2006, p. 132; d) Werro et al. 2005a, p. 10]

μ high loyalty

C2)

Improve loyalty

C1)

Commit customer

C4)

Don’t invest

C3)

Augment turnover

Turnover or sales (I 31)

Cus

tom

er lo

yalty

(I 1

34)

μ low loyalty

μ lo

w tu

rnov

er

00

1

1

C2)

Pseudo loyalty

C1)

True loyalty

C4)

No loyalty

C3)

Latent loyalty

Repurchases (I 56)

A

ttach

men

t (I 1

32)

μ high attachmentμ low attachment

μ fe

w re

purc

hase

s μ m

any

repu

rcha

ses

0 0

1

1

μ hi

gh tu

rnov

er

c) Fuzzy Classified Loyalty Portfolio d) Fuzzy Classified Loyalty/Turnover Portfolio

Ford Forrester

Smith

Miller

Brown

O’Connor Spencer

Ford

Smith

Miller O’Connor

Brown

μ satisfaction

Customer loyalty (I 134)

Cus

tom

er s

atis

fact

ion

(I 12

6)

μ dissatisfaction

μ lo

w lo

yalty

0 0

1

1

μ hi

gh lo

yalty

a) Higher Loyalty through Enriched Relationships

Customer loyalty or retention (I 137)

C

usto

mer

sat

isfa

ctio

n (I

126)

μ satisfactionμ dissatisfaction

μ lo

w re

tent

ion

μ hi

gh re

tent

ion

00

1

1

b) Higher Retention through Lock-in Effects

Ford

Forrester Smith

Miller Brown

O’Connor

Ford

Forrester

Smith

Miller

Brown

O’Connor

Low switching costs

High switching costs

Naked relationship

Enriched


- 75 -

In Figure 54c, fuzzy classification facilitates to identify and segment the true loyal customers

(Miller), to distinguish pseudo loyal clients (O’Connor) from more promising ones (Spencer),

latent loyal customers (Forrester) from quite loyal ones (Brown) or separate very disloyal

(Ford) from more loyal customers (Smith). With the fuzzy classified portfolio in Figure 54d, it

can be distinguished between customers to invest (Smith) or not (Ford), if loyalty (O’Connor)

or turnover (Brown) has to be improved or which customers have to be committed (Miller).

Dynamically, fuzzy classification provides valuable information by analysing, evaluating and

controlling individual effects of retention and loyalty programs or of switching barriers.

In addition, fuzzy classification enables the offering of individual and personal loyalty rewards,

like personal discounts (see Werro et al. 2005b), bonus points, special offers or presents.

Customer clubs, frequent flyer programs, communities and account or store cards are other

examples of incentives for customers to stay loyal and repurchase by the company.

5.1.9 Customer Retention

Customer retention (I 137), in a marketing sense, means holding on to customers [Peelen

2006, p. 239]. In contrast to loyalty, customer retention can be defined from a customer- and

from a company point of view. Retention from the customer’s point of view exits, if a customer

has certain reasons to repurchase from the company. However, customer retention is usually

defined from the company’s point of view [Homburg and Bruhn 2006, Reinecke and Dittrich

2006], and contains all actions, which lead to customer repurchases or cross-buying and avoid

that customers migrate to competitors. It can be distinguished between real and intentional

customer retention with different determinants shown in Figure 55.

Figure 55: Determinants of Customer Retention

As customer retention is crucial to augment customer equity and profit, CRM and marketing

require an adequate number of relevant retention indicators to manage and control different

levels of customer retention: customer attitudes ( in Figure 56), intentional customer reten-

tion ( ), real customer retention ( ) and the retention results ( ).


Customer value


Customer loyalty

Customerretention

Customer equity

Market value

Share-holder

Price premium

(I 61)

Cross-buy-ing intention

(I 54)

Intention to recommend

(I 151)

Repurchase intention

(I 55)

Migrated customers

(I 10)

Purchase intensity

(I 26)

Customer penetration(I 38, I 39)

Cross-buying (I 51)

Recom-mendations

(I 152)

Repur-chases (I 56)

Customer retention (I 137)

Source: adapted from [Bruhn 2003a, p. 104]

Real customer retention ( ) Intentional customer retention ( )


- 76 -

Figure 56: Controlling Level and Indicators of Customer Retention

Investments in customer retention ( in Figure 56) can be measured easily by customer

investment indicators (e.g. retention costs; or total customer costs). However, the evaluation of

retention indicators about attitudes and intentional customer retention (e.g. repurchase or

cross-buying intentions) on the controlling level and , is difficult, since they are hidden,

implicit, inconstant over time and differ from customer to customer. Nevertheless, intentions

determine real customer retention ( ; like the number of repurchases or cross-buying) and

the retention results ( ), which can be analysed by customer performance indicators for

revenue and profitability (e.g. customer turnover or profit). In case of habitualised purchasing

behaviour, a customer who, for instance, has always bought his bread from the same bakery,

can be seen as bound, although he may have no attitudes or clear intentions.

Combining any customer retention indicators of the five controlling levels of customer retention

( ↔ / / ; ↔ / ), fuzzy portfolio analysis enables the classification of indicators of

customer attitude, intentional or real customer retention and the evaluation of retention results.

Considering attitude/intentional retention portfolios, it is very advisable to reduce negative

attitudes (e.g. dissatisfaction) of customers towards the company, who intend to share their

negative feelings with many other persons (C2 in Figure 57a). CRM should set incentives (C3)

and maintain customers with positive attitudes and high intentional retention (C1).

However, since even very positive customer attitudes (like high satisfaction) towards the com-

pany and its products or services do not necessarily lead to higher real customer retention

(e.g. in the case of variety seeking in Figure 57b), fuzzy classified retention portfolios can be

useful to identify very delighted customers to retain (Miller) or real uncertain customers (Ford).

Intentional/real customer retention portfolios (Figure 57c) provide valuable information whether

customers really do repurchase or only intend to do.

Attitude

Intentional customer retention

Real customer retention

Retention results

Controlling level of retention Indicators of customer retention

Turnover (I 31), cash flow (I 30), contribution margins I-IV (I 70 - I 73), customer gross (I 75) or net profit (I 76), customer equity (I 79), number of customers (I 7), market share (I 43) Repurchases (recency, frequency, rhythm; I 20-I 28), cross-/up-buying (I 50-I 53), share of wallet (I 39), payment behaviour (I 66), recommendations (I 56), duration of relationship (I 147) Repurchase intentions (I 55), cross-buying intentions (I 54), intention to recommend (I 151), intention to switch (I 141), intention to dialog (I 157), intention to cooperate (I 171) Perceived product (I 118), service (I 120), relationship quality (I 149) or price-performance ratio (I 122), customer satisfaction (I 126), commitment (I 131), trust (I 133), image (I 115) Customer retention costs (I 89) or total customer costs (I 102): marketing (I 101), administration (I 93), transaction (I 94), con-tacts (I 100), sale (I 95), logistic (I 96) or after sales (I 98) costs Retention in case of habitualised purchase behaviour Source: adapted from [Reinecke and Dittrich 2006, p. 329]

Category of indicator

Cus

tom

er R

elat

ions

hip,

Info

rmat

ion,

R

ecom

men

datio

n an

d C

oope

ratio

n In

dica

tors

(CR

I, C

InfI,

CR

eI, C

CR

)

Cus

tom

er P

erfo

rman

ce In

dica

tor f

or

Rev

enue

and

Pro

fitab

ility

(CP

IP)

Cus

tom

er In

vest

men

t Ind

icat

ors

(CII)

Investments in customer retention


- 77 -

Figure 57: Fuzzy Classified Portfolios of Customer Retention Indicators Attitudes and behavioural intentions are interesting psychological constructs, but what counts

in business practice is real customer retention (i.e. many repurchases, cross-/up-buying, long-

term relationships, etc.) and retention results. Possible retention results are customer turn-

over (I 31), contribution margins (I 70 - I 73) and customer profits (I 75, I 76). A company which

does not realise high profits in the long term is not successful, even if market shares are high

and all customers loyal (zero migration; C3 in Figure 57d). In such situations, the organisation

may have to be restructured, customer costs to be reduced or the efficiency to be increased to

achieve customer excellence (in C1). A company in a highly competitive and dynamic market

with low customer retention (C2), is forced to constantly acquire new customers, to diversify its

product or service portfolio and/or to innovate in order to retain high customer profits.

μ high intention

C2) High real customer retention (to skim)

C1) High

customer retention (to retain)

C4) Little

customer retention

(don’t invest)

C3) High customer

intentional retention (to realise)

Repurchases (I 56) and cross-/up-buying (I 50-I 53)

μ low intention

μ fe

w re

purc

hase

s

0 0

1

1

μ m

any

repu

rcha

ses

Rep

urch

ase

inte

ntio

n (I

55) a

nd

Cro

ss-/u

p-bu

ying

inte

ntio

ns (I

54)

c) Fuzzy Intentional/Real Retention Portfolio

μ high retention

C2)

Acquire, diversify, innovate

C1)

Defend customer

excellence

C4)

Consolidate customer/ product portfolio

C3)

Reduce costs or improve efficiency

Retention results: customer profits

μ low retention

μ lo

w p

rofit

s

00

1

1

μ hi

gh p

rofit

s

Rea

l cus

tom

er re

tent

ion

μ satisfied

C2)

Reduce intention (improve satisfaction)

C1)

Maintain intention

C4)

Don’t care and don’t

invest

C3)

Augment intention

(set incentives)

Intention to recommend (I 151)

μ dissatisfied

μ lo

w in

tent

ion

0 0

1

1

μ hi

gh in

tent

ion

Cus

tom

er s

atis

fact

ion

(I 12

6)

μ satisfied

C2)

Locked-up customers

(to skim)

C1)

Delighted customers (to invest)

C4)

Uncertain customers (to monitor)

C3)

Variety seekers

(to bind or not)

Repurchases (I 56), share of wallet (I 39)

μ dissatisfied

μ fe

w re

purc

h.

00

1

1

μ m

any

repu

rcha

ses

Cus

tom

er s

atis

fact

ion

(I 12

6)

Ford

Smith

Miller

Brown

d) Fuzzy Real Retention/Results Portfolio

Attitude Intentional customer retention

Real customer retention

Retention results

Investments in customer retention

a) Fuzzy Attitude/Intentional Retention Portfolio b) Fuzzy Attitude/Real Retention Portfolio ↔ ↔

↔ ↔


- 78 -

5.1.10 Repurchases

Satisfied, loyal or bound customers are more likely to repurchase the same products or ser-

vice from a company. As a result, customer repurchases (I 56), repurchase intentions (I 55) or

the probability of repurchases (I 57) are meaningful indicators for customer loyalty and reten-

tion. The more often a customer repurchases from the company, the higher is the customer

equity or Customer Lifetime Value (CLV). To increase or retain repurchases, it is a strategic

task to maintain customer satisfaction or loyalty, and to augment the ease of purchase, e.g.

by a high availability or good distribution of products and services. As discussed in the

RFM example in Section 3.5, purchase incentives or loyalty programs may raise the number,

frequency and monetary value of repurchases. In addition, reminders, catalogues, brochures,

newsletters or e-mails can stimulate customers to buy again or more often (see Figure 58).

Figure 58: Examples of Fuzzy Classified Repurchase Portfolios

5.1.11 Add-on Selling A strategic topic and instrument of CRM is add-on selling. Add-on selling can be defined as

“the activity associated with selling any additional products or services to current customers”

[Blattberg et al. 2001, p. 95]. Add-on selling includes down-selling (the selling of less valuable

or cheaper products or services; I 50), cross-selling (of other, similar or new products; I 51)

and up-selling (more expensive or valuable products of the company; I 53).

μ many repurchases

C2)

Augment repurchases

C1)

Retain customer

C4)

Don’t invest

C3)

Augment monetary

value

Monetary value (I 29)

μ few repurchases

μ lo

w v

alue

0 0

1

1

μ hi

gh v

alue

μ frequent

C2)

Augment frequency

C1)

Retain customer

C4)

Don’t invest

C3)

Reactivate customer

Frec

ency

of p

urch

ases

(I 2

8)

μ rare

μ lo

ng a

go

00

1

1

μ

very

rece

nt

Num

ber o

f rep

urch

ases

(I 5

6),

prob

abili

ty o

f rep

urch

ases

(I 5

7) a) b) Recency (of last purchase; I 27)

Repurchases


Customer value


Customer loyalty

Customer retention

Customer equity, CLV

Market value

Share-holder

Add-on selling


Customer value


Customer loyalty

Customer retention


Market value

Share-holder


- 79 -

Figure 59: Examples of Fuzzy Classified Add-on Selling Portfolios

By analysing customer loyalty (I 134) or retention (I 137), the potential for turnover or profit (I 83),

customers needs, the purchased product mix by the company or by the competitors (I 58) and

the purchase behaviour in general, the cross-selling potential (I 52) can be evaluated.

For promising customers or customer segments with high cross- or up-selling potential (C1 in

Figure 59a; C1 and C2 in Figure 59b), customer penetration strategies (to provide offers) or

upgrading strategies (to strengthen relationships) have to be formulated and implemented.

If customers do not buy many different products and have rather low cross-buying potential,

customer relationships should be managed less cost-intensive (C4: downgrading).

5.1.12 Share of Wallet

The customer performance indicator share of wallet (I 39) measures the share of the cus-

tomer’s expenditures for a specific product or services in relation to the total expenditures.

Following example explains the idea of the share of wallet: if a customer purchases the total

amount of a product A by the company 1 in period 1, he has a maximum share of wallet of

brand A (see Figure 60a). In period 2, the customer may switch to brand B by the company 2.

He has now a maximum (100%) share of wallet of brand B, and a minimal one (0%) of A.

However, the customer does not necessarily choose A or B ("crisp" choice in Figure 60a), but

rather choose partially A (10%), partially B (80%) and partially C (10%). This example of [Rust

et al. 2000] shows: the idea of fuzzy logic can be easily applied to the concept of share of wal-

let to analyse the "fuzzy" choices of a customer. As exemplified in Figure 60b, in period 2 the

share of wallet of brand B was increasing and this one of brand A decreasing.

Share of wallet


Customer value


Customer loyalty

Customer retention


Market value

Share-holder

μ high loyalty

C2)

Retention

C1)

High cross-selling

potential (I 52)

C4)

Disinvestment

C3)

Exploitation

Customer potential (I 83) μ

low

pot

entia

l

0 0

1

1

μ hi

gh p

oten

tial

Cus

tom

er lo

yalty

(I 1

34) o

r C

usto

mer

rete

ntio

n (I

137)

a)

μ high cross-selling

C2) Penetration (to provide

cross- & up-selling offers)

C1)

Upgrading (to strengthen relationship)

C4)

Downgrading (to weaken relationship)

C3)

Absorption (to maintain relationship)

Cross-selling potential (I 52)

μ low cross-selling

μ lo

w p

oten

tial

00

1

1

μ hi

gh p

oten

tial

Cro

ss-s

ellin

g (I

51)

or u

p-se

lling

(I 5

3)

b)

Source a): adapted from [Bruhn and Georgi 2006, p. 47]

μ low loyalty


- 80 -

Figure 60: Crisp (a) and Fuzzy (b) Choice

It might be interesting to compare the share of wallet fuzzily with the level of customer pene-

tration (I 38; the percentage of all demanded products bought from the company) to define

penetration or add-on selling strategies (in Figure 61a). Since a company is very dependent (I

136) of a customer with a high market share (I 42), it has to follow intensive customer acquisi-

tion and penetration strategies in order to increase the own market share (see Figure 61b).

Figure 61: Examples of Fuzzy Classified Share of Wallet Portfolios

μ high share of wallet

C2)

Intensive acquisition and

penetration

C1)

Intensive retention

C4)

Moderate acquisition penetration

C3)

Moderate retention

Market share of customer (I 42)

μ low share of wallet

μ lo

w m

arke

t sha

re

00

1

1

μ hi

gh m

arke

t sha

re

Sha

re o

f wal

let (

I 39)

, or

cust

omer

pen

etra

tion

(I 38

)

b)

μ high share of wallet

C2)

Augment share of wallet (of product X)

C1)

Retain customer

C4)

Penetrate customer

C3)

Add-on selling

Sha

re o

f wal

let (

I 39)

μ low share of wallet

μ lo

w p

enet

ratio

n

0 0

1

1

μ

high

pen

etra

tion

a) Customer penetration (I 38)

a) "Crisp" (Sharp) Choice

b) "Fuzzy" Choice (share of wallet)

Source cartoon: [Rust et al. 2000, p. 43]

Period 1

Per

iod

Brand A Brand B

0%

100%

Per

iod

Brand A & B Brand C

0%

100%

Period 2

Period 1 Period 2

A B

A

B

A

B

C

60%

Increasing share of wallet of brand B (by company 2)

Decreasing share of wallet of brand A (by company 1)

Maximum share of wallet of brand A (by company 1)

Maximum share of wallet of brand B (by company 2)


- 81 -

5.1.13 Turnover

According to empirical studies, the financial indicators customer turnover, sales or revenues

(I 31) are regularly measured in most of the companies (see for instance [Reinecke 2004]).

This kind of data can be gathered easily from the accounting or marketing department or is

available in an information system.

To classify customer turnover sharply is problematic, since only small changes of turnover or

sales may cause different classifications of customers.

However, a fuzzy classification of customer turnover can be one- or multidimensional. The

fuzzy classification in Figure 62a, described by [Meier et al. 2005], combines the attribute

turnover with the ‘punctuality of payment’ (I 63) and defines four basic strategies.

In Figure 62b, a dynamic fuzzy classification with the attribute ‘cumulative turnover’ (I 34) and

the negative or positive ‘growth of turnover’ (I 35) is considered. A sharp classification would

be a mistake, as the examples of Smith and Brown show again; although they have approxi-

mately the same values, Brown is labelled as a ‘top’ (C1) and Smith as a ‘flop customer’ (C4).

Fuzzily, the customers are named and managed more sophisticated and manifold.

Figure 62: Examples of Fuzzy Classified Turnover Portfolios

The evaluation of customer’s turnover or revenue does not allow making any statement about

the customer’s costs or profitability. An isolated consideration of the customers’ monthly or

annual turnover may lead to misinterpretations and has to be supplemented with the following

discussed indicators: customer’s contribution margins and customer gross or net profit.

μ punctual

C2)

Improve punctuality of payment

C1)

Commit customer

C4)

Don’t invest

C3)

Augment turnover

Turnover (I 31)

μ unpunctual

μ lo

w tu

rnov

er

0 0

1

1

μ hi

gh tu

rnov

er

μ high cum. turnover

C2)

Growth customers (to invest)

C1)

Top Customers

(to maintain)

C4)

Flop customers

(not to invest)

C3)

Churn customers (to recover)

Cum

ulat

ive

turn

over

(I 3

4)

μ low cum. turnover

μ hi

gh n

eg. g

row

th

00

1

1

μ

high

pos

. gro

wth

Pun

ctua

lity

of p

aym

ent (

I 63)

a) b) Growth of turnover or sales (I 35)

Source: a) adapted from [Meier et al. 2005, p. 20]

Smith

Brown

Ford

Miller

Turnover, sales and cash flows


Customer value


Customer loyalty

Customer retention


Market value

Share-holder


- 82 -

5.1.14 Contribution Margins

Since cost-intensive customers with high turnover are not necessarily profitable, customer

contribution margin accounting is required to determine the profitability of the customers.

The main problem in doing so is to identify, allocate and to trace total customer costs (I 102),

such as administration (I 93), transaction (I 94), sales (I 95), logistic (I 96), service (I 97), after

sales (I 98), communication (I 99), contact (I 100) or marketing (I 101) costs. General or overhead

costs which are not, or only partly traceable to a customer, are not accounted in the example

of multi-level customer contribution margin accounting shown in Figure 63a, nevertheless they

should be. However, customer-related allocation of costs is necessary in order to calculate the

profitability of a customer. Therefore, a customer-oriented calculation of direct costs is useful.

With the calculation scheme shown in Figure 63a, four different customer contribution mar-

gins I (I 70), II (I 71), III (I 72) and IV (I 73) can be calculated and evaluated fuzzily.

Figure 63: Customer Contribution Margin Accounting

Contribution margins


Customer value


Customer loyalty

Customer retention


Market value

Share-holder

N

egat

ive

Lo

w

Med

ium

Hig

h

Tu

rnov

er (I

31;

in €

)

Customer contribution margin I-IV (I 73; in €)

0 1

μ ne

gativ

e co

ntrib

utio

n m

argi

n

hi

gh c

ontri

butio

n m

argi

n

0

1

μ high turnover

μ low turnover

0 250 500 750 1000 1250 1500 3000 6000 9000

≥1250

1000

750

500

200

100

50

0

-50

-100

-150

-200

a) Calculation Scheme for Customer Contribution Margins

Customer gross sales revenue per period

– customer-related revenue reductions (e.g. volume discount, cash discount) = Customer net sales revenue

– Costs of goods/services sold = Customer contribution margin I – Customer-driven marketing costs (I 101; e.g. mailings, calls, trials, card) = Customer contribution margin II – Customer-driven sales costs (I 95; e.g. sales force, ordering, billing) = Customer contribution margin III – Customer-driven service and logistic costs (I 96, I 97, I 98); e.g. after sales service, delivery of goods, helpdesk) = Customer contribution margin IV

Source calculation scheme: adapted from [Bruhn and Georgi 2006, p. 40, Bauer et al. 2006, p. 173]

b) Correlation between

Annual Contribution Margin I - IV and Turnover per Customer

Low Medium High

Miller

Brown

With fuzzy classification, customers can be better analysed and seg-

mented by their contribution margin

Smith

Ford

μ

posi

tive

cont

ribut

ion

mar

gin


- 83 -

As shown in Figure 63b, Ford and many other customers with low turnover have a lightly

negative contribution margin. This means that a high share of the customers is not profitable.

Usually, the contribution margins and their deviation rise with increasing turnover. Since Miller

causes higher costs, he is less profitable than Mr. Brown, despite higher turnover. A detailed

margin accounting can provide important evidence for different customer costs and customer

profitability and therefore for the evaluation of customer attractiveness (I 82).

In Figure 64, four customer contribution margins portfolios give valuable information about

customer costs of marketing (I 101), sales (I 95), service (I 97) or logistic (I 96), about the level of

the customer contribution margins I to IV (I 70 - I 73), and about simple strategies.

Figure 64: Fuzzy Classified Customer Contribution Margins Portfolios

In addition, fuzzy classification is useful for identifying and separating valuable customers with

high contribution margins from unattractive ones with low or lower margins, and for managing

these customers adequately by CRM and marketing.

Contribution margin III (I 72)

C

ontri

butio

n m

argi

n II

(I 71

)

μ high margin IIμ low margin II

μ lo

w m

argi

n III

μ h

igh

mar

gin

III

0 0

1

1

c) Fuzzy Contribution Margin III Portfolio d) Fuzzy Contribution Margin IV Portfolio

μ high sales revenue

Contribution margin I (I 70)C

usto

mer

net

sal

es re

venu

e (I

31)

μ low sales revenue

μ lo

w m

argi

n I

0 0

1

1

μ hi

gh m

argi

n I

a) Fuzzy Contribution Margin I Portfolio

Contribution margin II (I 71)

C

ontri

butio

n m

argi

n I (

I 70)

μ high margin I μ low margin I

μ lo

w m

argi

n II

μ hi

gh m

argi

n II

00

1

1

b) Fuzzy Contribution Margin II Portfolio

Contribution margin IV (I 73)

Con

tribu

tion

mar

gin

III (I

72)

μ high margin III μ low margin III

μ lo

w m

argi

n IV

μ h

igh

mar

gin

IV

00

1

1

C2)

Increase turnover or sales

C1)

Retain customer and

product

C4)

Consolidate product or cus- stomer portfolio

C3)

Reduce costs of goods or

services

C2)

Increase contribution

margin I

C1)

Retain customer and

product

C4)

Consolidate product or cus- omer portfolio

C3)

Reduce marketing

costs (I 101)

C2)

Increase contribution

margin II

C1)

Retain customer and

product

C4)

Consolidate product or customer portfolio

C3)

Reduce sales costs

(I 95)

C2)

Increase contribution margin III

C1)

Retain customer and

product

C4)

Consolidate product or cus- omer portfolio

C3)

Reduce service/logistic costs (I 97,98)


- 84 -

5.1.15 Profitability

According to [Kotler et al. 2005, p. 474], a profitable customer is a person, whose revenues

(I 31) over time exceed the company’s costs of attracting (I 87), selling (I 95) and servicing (I 97)

that customer. Customer gross profit (I 75) is defined here as the customer’s total turnover (I

31) minus total customer’s costs (I 102). Customer net profit (I 76) additionally considers taxes,

interests, depreciations or other expenses. ‘Customer profitability’ (I 78) and ‘customer reten-

tion’ (I 137), both measured either at a customer, customer segment or at the enterprise level,

and the ‘number of customers’ (I 7) can be linked to a multidimensional fuzzy classification (in

Figure 65b). Depending on the profitability and retention of a customer (segment), and on the

number of customers, Figure 65a defines the directions of growth. In addition, the develop-

ment (growth) of all indicators are classified dynamically and fuzzily in Figure 65c.

Figure 65: Fuzzy Classification of Customer Profitability


Customer value


Customer loyalty

Customer retention

Profits


Market value

Share-holder

Description of the Development 2005-2007

In 2005, the retention rate of the company has decreased and the company lost many customers, although profits increased.

CRM reacts and follows fuzzily strategy C2): prices are reduced and loyalty programs launched in order to reduce to churn rate.

Due to lower prices, profits decreased in 2006. However, customer retention and the number of customers strongly increased.

To raise profits again, management reduces production, marketing and customer costs.

As a result, profits were increasing again in 2007 and the number of customers and customer retention remain high.

b) Fuzzy Classification of Customer Profitability a) Defining the Directions of Growth

C6) Improve customer loyalty or swichting cost

C5) Defend

C8) Consolidate customer base

C7) Reduce customer costs, raise price

C2) Offer innovation, cut price, bind customers

C1) Exploit channel or marketing opportunities

C4) Restructure business

C3) Consolidate product portfolio

01 0

μ low retention rate μ high retention rate

μ hi

gh p

rofit

s μ

low

pro

fits

μ low number

μ highnumber

Customer profitability (I 78)


Num

ber o

f cu

stom

ers

(I 7)

Customer retention

Number of customers

high number

low number

high

pro

fits

low

pro

fits

high retention low retention

C1) C2)

C4) C3)

C6) C5)

C8) C7)

Customer profitability

C1) C2)

C4) C3)

C6) C5)

C8) C7)

Source strategies of a): adapted from [Plaster and Alderman 2006, p. 3]

01 0

μ decreasing rate

μ in

crea

sing

pro

fits

μ de

clin

ing

prof

its

μ decreasing number

μ increasing number

Development of the customer retention rate

Dev

elop

men

t of

the

num

ber

of c

usto

mer

s

μ increasing rate

c) Dynamic Fuzzy Classification of Growth

2005

2006

2007

2005

2005

2005 2006

2006 2006

2007

2007

2007

Development of customer profit-

ability (I 77)


- 85 -

To augment the number of customers, the company should exploit channel or marketing op-

portunities in the case of high customer retention and profits (C1 in Figure 65a) or offering

innovations, cutting the prices and bind customers, if customer retention and the number of

customer is low (C2). If profits are low despite high retention and a high number of customers

(C7), customer costs have be to reduced and prices to be raised. If the number of customers

(C3) or customer retention is low as well (C8), the product portfolio or customer base has to be

consolidated. Businesses have probably to be restructured, if all three indicators are low (C4).

Using these three strategic customer indicators and fuzzy classification, the performance of a

customer, of a fuzzy segment or of the whole company can be classified precisely in order to

define and implement the different strategies of growth.

However, the company has different opportunities to achieve customer growth. For instance, it

can either follow the profit maximising growth strategy, the market penetration or the cus-

tomer retention strategy, or all strategies simultaneously (see Figure 66). The company has

to choose the strategy or axis that provides the best opportunity for profitable growth. Under-

standing fuzzily the size and shape of the cubes, CRM has to follow these growth strategies

with the best future theoretical state ("could be") and practical condition ("should be").

The main advantage of this approach of customer strategy definition and implementation is

that it can be applied with any kind of strategic customer indicators, which can be dynamically

and fuzzily analysed, evaluated and controlled by managers or by CRM software.

Figure 66: Customer Growth Strategies

Number of customers

Current

state

Customer retention

Number of customers

Cus

tom

er p

rofit

abili

ty

Future state "could be" operations Future state "should be" operations

Current

state

Customer retention

Cus

tom

er p

rofit

abilit

y

Customer retention

Number of customers

Current

state

Cus

tom

er p

rofit

abilit

y

Current

state

Num

ber o

f cu

stom

ers

Cus

tom

er

prof

itabi

lity

Customer retention strategy

Customer retention

Source: a) adapted from [Plaster and Alderman 2006, p. 2]

a) "All-in-One" Customer Growth Strategy b) Customer Profit Maximising Growth Strategy

c) Market Penetration Growth Strategy d) Customer Retention Growth Strategy

All-in-one

strategy

Pro

fit-m

axim

isin

g st

rate

gy


- 86 -

5.1.16 Customer Equity and Customer Lifetime Value (CLV)

Literature on customer equity increased quickly during the last years. However, there is no

consistent definition of customer equity (see [Cornelsen 2000, Rudolf-Sipötz 2001, Blattberg et

al. 2001, Rust et al. 2000, 2004, 2005, Rogers and Peppers 2005, Bauer et al. 2006, Reichold

2006, Günter and Helm 2006, Bejou and Gopalkrishnan 2006, Aksoy et al. 2007, Krafft 2007]).

Customer equity can be defined as a synonym of Customer Lifetime Value (CLV; I 80): “A

firm’s Customer Equity is the total of the discounted lifetime values of all of its customers”

[Rust et al. 2000, p. 4]. Since it is difficult or even impossible to forecast or discount future cus-

tomer cash flows, customer equity is here not understood as a discounted or a dynamic value.

Customer equity (I 79), which can be considered as “the satisfaction of company with a cus-

tomer” [Palloks-Kahlen 2006], is defined here as the current total monetary and non-monetary

economical value of a customer for a company.

Customer equity management is “a dynamic, integrative marketing system that uses finan-

cial valuation techniques and data about customers to optimise the acquisition of, retention of,

and selling of additional products to a firm’s customers, and maximise the value to the com-

pany of the customers relationship throughout its life cycle” [Blattberg et al. 2001, p. 3].

Considering customer performance indicators of revenue and profitability, customer equity or

CLV is higher with increasing customer (cumulative) turnover, contribution margins or net

profit and customer retention. Figure 67a fuzzily segments customers with high, medium or

low equity. As an aggregate, the total customer equity affects market value and market share,

as well as the firm value and the shareholder value. If the customer equity share is low, the

market share of a company declines or rests on a low level (see Figure 67b).

Figure 67: Examples of Fuzzy Classified Customer Equity Portfolios


Customer value


Customer loyalty

Customer retention


Market value

Share-holder

μ high cum. turnover


μ low cum. turnover

μ lo

w re

tent

ion

0 0

1

1

μ hi

gh re

tent

ion

μ high equity share

C2)

Declining company

C1)

Healthy company

C4)

Sick company

C3)

Growing company

Cus

tom

er e

quity

(sha

re; I

79)

μ low equity share

μ lo

w s

hare

00

1

1

μ h

igh

shar

e

Cum

ulat

ive

turn

over

(I 3

4)

or p

rofit

s (I

76)

a) b) Market share (I 43,I 44)

Source: b) adapted from [Rust et al. 2000, p. 162]

Aug

men

t cus

tom

er

C2)

Medium customer equity

(long-term)

C1)

High customer

equity (/CLV)

C4)

Low customer

equity (/CLV)

C3)

Medium customer equity

(short-term)


- 87 -

However, to augment customer equity (and market share/value), the company has to analyse

on which customers it should focus and which customers should be bound in order to estab-

lish long-term, profitable relationships. The company has to evaluate all customers who are

attractive, using, for instance, fuzzy portfolio analysis.

The customer attractiveness/equity portfolio discussed by [Palloks-Kahlen 2006] consists

of two dimensions (see Figure 68): the vertical axis indicates the level of ‘customer satisfaction

with the company’ (I 126), or customer value (I 124) in a broader sense. In addition, the horizon-

tal axis describes the ‘satisfaction of the company with a customer’, which can be seen as

customer attractiveness (I 82) or customer equity (I 79), measured, for instance, by the cus-

tomer contribution margins I-IV (I 70 - I 73). The six classes C1-C6 reflect different types of cus-

tomers: regular buyers (I 9; C1 in Figure 68;) are convinced and profitable customers to main-

tain and suited for establishing long-term relationships. Satisfied customers (C5) in the "corri-

dor for active CRM and customer retention" are to be developed by augmenting satisfaction

and/or contribution margins. CRM, customer development and customer retention should fo-

cus on promising customers (of C2) in order to turn them into regular buyers.

Figure 68: Fuzzy Classified Customer Satisfaction/Equity Portfolio

To distinguish sharply customers of these different classes is a problem, once again: why

should Brown be labelled as ‘convinced but unprofitable customer’, but Miller as ‘regular

buyer’, although they have nearly the same values? In addition, Ford should not be considered

and managed just as ‘disappointed and unprofitable customer’ and Smith not only as ‘promis-

ing customer’. With fuzzy classification, the transitions between the different classes and CRM

strategies become fluent and customer are mostly mixes of different classes and they can

therefore be evaluated, developed and managed according to their real measured values.

μ lo

w

μ hi

gh

μ m

ediu

m C5)

Satisfied, but unprofitable

customer

C2)

Promising customer

C6) Disappointed

and unprofitable customer

C3) Profitable absorption customer

0 1 Customer equity (I 79) (satisfaction of the company with a customer) measured by: contribution margins I-IV (I 70 - I 74)


C

usto

mer

attr

activ

enes

s (e

quity

) Customer satisfaction (or value)

C5) Satisfied but

unprofitable cu.

C6) Disappointed & unprofitable cu.

C4) Convinced, but unprofitable cu.

C3) Profitable

absorption cu.

C2) Promising customer

low high Contribution margin I-IV

Customer satisfaction (I 126) or value (I 124)(satisfaction of a customer with the company)

μ high contribution marginμ low contribution margin

0

1

Ford

Area of long-term

relationships

Corridor for active customer retention (CRM)

C4) Convinced, but

unprofitable customer

Smith

Brown

C1)

Regular buyer

Direction of differentiated customer development

Source: adapted from [Palloks-Kahlen 2006, p. 302]

C1) Regular buyer

low

med

ium

h

igh

Miller


- 88 -

However, different drivers influence customer equity, and not only customer turnover or contri-

bution margins. [Blattberg et al. 2001], for instance, define three drivers of customer equity:

value equity (driven by quality, price, convenience), brand equity (determined by customer

brand awareness, attitude, brand ethics) and retention equity (driven by loyalty, affininity,

special recognition, treatment programs and others).

An interesting conceptualisation and operationalisation of customer equity is discussed by

[Rudolf-Sipötz 2001]. Using a factor analysis method, different determinants and customer

performance indicators of the concept customer equity were empirically tested.

The indicators and the different determinants (categories of potential) of customer equity are

shown in Table 15. To improve customer performance and customer equity, diverse measures

and actions of operational customer equity management can be implemented.

Table 15: Determinants and Indicators of Customer Equity

Determinant Customer Performance Indicator Actions of Equity Management Customer contribution margin II (I 71) Customer contribution margin III (I 72) Profit

potential Customer contribution margin IV (I 73)

Improvement of customer efficiency (I 108)(e.g. reduction of customer costs) Analysis of customer’s product mix (I 58)

Development of sales/turnover (I 35) Potential of contribution margins (I 84) Probability of repurchases (I 57) Phase in customer lifetime cycle Product portfolio by competitor (I 58) Need for diversification

Developmentpotential

and

Cross-buyingpotential

Cross-buying intention (I 54)

Analysis of needs and demand (I 117) Analysis and enhancement of customer value (I 124) and value drivers Analysis and enhancement of added-value, e.g. customising or personalisation Product and service bundling Diversification and innovation management

Customer satisfaction (I 126) Trust (I 133) M

arke

t pot

entia

l of c

usto

mer

Loyalty potential Duration of relationship (I 147)

Customer loyalty and retention programs Customer care and consulting (I 158) Customer relationship investments (I 87ff)

Expertise for cooperation (I 172) Intention to cooperate (I 171) Cooperation behaviour Potential cooperation topics (I 173) Lead user

Cooperation potential

Product expertise (I 168)

Strategic alliances and networks Partnerships Lead user programs Cooperation programs (e.g. for R&D) Compatible information systems Suggestion boxes, etc.

Intention to recommend (I 151) Number of recommendations (I 152) Recommendation intensity (I 153)

Reference potential

Potential reference recipients (I 155)

Member-get-member-/tell-a-friend-program (incentives for recommendations)

Information and public relations Satisfaction and complaint management

Role as opinion leader (I 156) General intention to dialog (I 157) Number of complaints (I 164) Response rate (I 169)

Information potential

Number/quality of suggestions (I 164)

Customer surveys and questionnaires Hotline, call center, help/information desk Group discussions, conferences User groups Customer communities, clubs, cards, etc.

Cus

tom

er e

quity

Res

ourc

e po

tent

ial o

f cus

tom

er

Synergy potential Compound effect in customer base Economics of scales (e.g. in R&D)

Internal coordination (e.g. centralisation)

Source: adapted from [Rudolf-Sipötz 2001, p. 95, 170, 186]

[Rudolf-Sipötz 2001, pp. 192ff] undertakes a sharp classification to segment customers by

defining a customer cube with the attributes ‘actual market potential of customer’ (i.e. the

total direct, monetary value of a customer for the company), ‘future market potential’ and ‘re-

source potential’ (the total indirect, non-monetary value of a customer for a company).

In Figure 69b, the customer equity cube is adapted to a three-dimensional fuzzy classification.


- 89 -

Figure 69: Three-Dimensional Fuzzy Classification of Customer Equity

Classified fuzzily, the transitions between the classes and customer segments become fluent

and the strategies of Figure 69a have to be adapted, aligned and personalised accordingly.

Another, dynamic method to determine customer equity is the calculation of Customer or

Prospect Lifetime Value (CLV, I 80; PLV, I 81). CLV portfolios should also consider monetary

and non-monetary criteria, as shown in Figure 70a. Monetary CLV can be calculated by the

formula of Figure 70c, qualitative criteria by a fuzzy scoring model or a cost-benefit analysis.

Figure 70: Fuzzy Classified Customer (a) and Prospect (b) Lifetime Value Portfolios

Rt = Total customer revenues (cumulated customer turnover; I 34) Et = Total customer expenses (total customer costs; I 102) t = Duration of customer relationship (I 147) i = Discount rate

Non-monetary criteria

Customer recommendation (I 151-I 156), information (I 157-I 170) and cooperation (I 171-I 173) indicators

Monetary criteria

Revenues: customer turnover (I 31), cum. turnover (I 132), cash flow (I 30), monetary value (I 29); etc.

Expenses: acquisition (I 87) retention (I 89), service (I 97), marketing (I 101), total customer (I 102) costs

Customer Lifetime Value (CLV)

c)

Fuzzy scoring model

μ high retention

C2)

To bind C1)

To commit

C4)

Don’t invest

C3)

To monitor

μ low retention

μ lo

w C

LV

0 0

1

1

μ hi

gh C

LV

μ high probability

C2)

High investment in prospect

C1)

Low investment in prospect

C4)

Medium investment in prospect

C3)

No investment in prospect

Inte

ntio

n to

sw

itch

(I 14

1) o

r sw

itchi

ng p

roba

bilit

y (I

142)

μ low probability to switch

μ lo

w P

LV

00

1

1

μ

high

PLV

Cus

tom

er lo

yalty

(I 1

34) o

r cu

stom

er re

tent

ion

(I 13

7)

a) b) PLV (Prospect Lifetime Value; I 81)

Miller

Brown

Ford

Smith

CLV (I 80; monetary; non-monetary)

∑= +

−=

n

tttt

iERCLV

0 )(1)(

b) Fuzzy Classification of Customer Equity a) Customer Segments and Strategies

C6) Perspective customer to invest and augment profit

C5) Blue-chip-customers to invest and maintain

C8) Future customers to augment turnover

C7) Potential customers to invest & increase commitment

C2) Selective customers Don’t invest, but to maintain

C1) Take along customers to augment share of wallet

C4) Renunciation custom. Don’t invest, reduce costs

C3) Absorption customers to skim, reduce costs

Actual market potential of customers

Future market potential of customer

high future potential

low future potential lo

w

reso

urce

po

tent

ial

high actual potential

low actual potential

0 1 0

μ low actual potential μ high actual potential

μ hi

gh re

sour

ce p

oten

tial

μ lo

w re

sour

ce p

oten

tial

μ low future potential

μ high future potential

Resource potential

Actual market potential of customer

Futu

re m

arke

t po

tent

ial o

f cus

tom

er

C1) C2)

C4) C3)

C6) C5)

C8) C7)

Resource potential

C1) C2)

C4) C3)

C6) C5)

C8) C7)

Source: adapted from [Rudolf-Sipötz 2001, pp. 192ff]

high

re

sour

ce

pote

ntia

l


- 90 -

[Rust et al. 2000, p. 191] define a four-tier system to classify customer equity (see Figure 71):

The platinum tier describes the company’s most profitable customers (I 78), typically those

who are heavy and regular users (I 22) of the product, are not overly price sensitive (I 60), are

willing to invest in and try other or new offerings (I 51), and are committed (I 131) or loyal (I 134)

customers of the company. The gold tier differs the platinum tier in that profitability levels are

not as high, perhaps because the customer want price discounts (I 37) that limit the contribu-

tion margin I to IV (I 70 - I 73) or they are not as loyal. The iron tier contains essential custom-

ers who provide the volume needed to utilise the company’s capacity, but their spending lev-

els, loyalty and profitability are not substantial enough for special treatment. The lead tier

consists of customers who are costly for the company. They demand more attention than they

are due given their spending and profitability and are sometimes problem customers, com-

plaining about the firm to others (I 155), tying up the firm’s resources (I 102).

Figure 71: Sharp (a) and Fuzzy Classified (b) Customer Equity Pyramid

However, it is problematic to label customers sharply just as ‘platinum’, ‘gold’, ‘iron’ or ‘lead’,

since the definitions of these classes are arbitrary. When is a client exactly a platinum cus-

tomer (Miller in Figure 71a) and when a golden one (Brown)? What about customers who are

classified sharply in the same tier, although their customer equity is quite different (Brown and

Smith)? Why should iron Ford, who is as profitable as Smith, not be treated specially as Smith

is? Such questions are quite difficult to answer.

Using fuzzy classification, the transitions between the four tiers becomes fluent and customers

can partly belong to more than one tier at the same time. Miller and Brown are both an "alloy"

of platinum and gold, and also Smith and Ford are partially golden and iron at the same time.

Defining only two terms ‘least profitable’ (or iron) and ‘most profitable’ (or gold) customers in

Figure 71b, the customers can be classified fuzzily in the customer equity pyramid.

The fuzzy classified customer equity pyramid has a more neutral and proper semantic and the

customers are no longer privileged or discriminated.

Platinum

Gold

Iron

Lead

Ford

Smith

Miller Brown

Least profitable customers

b) What (fuzzy) segment spends more with us over time, costs less to maintain and spreas

positive word of mouth?

What (fuzzy) segment costs us in time, effort, and money

yet does not provide the return we want? What fuzzy segment is difficult to do business with?

a)

μ

leas

t pro

fitab

le

10

μ

mos

t pro

fitab

le

Ford

Smith

MillerBrown

Source a): [Rust et al. 2000, p. 193]

Most profitable customers


- 91 -

5.2 Fuzzy Market Segmentation Just as customers, buyers of a whole market differ in their wishes, resources, locations, buying

attitudes and buying practices. Through market segmentation, companies divide large, het-

erogeneous markets into smaller segments of buyers with different needs, characteristics or

behaviour, who might require separate products or marketing mixes [Kotler et al. 2005, p. 391].

In contrast to mass marketing, where the same marketing mix is used for all consumers and no

market segmentation is undertaken, segment marketing adapts the marketing mix to seg-

ments. Niche marketing, in turn, focuses on subgroups (niches) within these segments, where

often is little competition. In the case of complete market segmentation, micromarketing,

products and marketing programmes are tailored to the needs and wants of narrowly defined

geographic, demographic, psychographic or behavioural segments. In the extreme, micromar-

keting becomes individual marketing that means tailoring products and marketing pro-

grammes to the needs and preferences of individual customers [Kotler et al. 2005, p. 395].

Individual marketing has also been labelled ‘markets-of-one marketing’ or ‘one-to-one market-

ing’ as discussed in Section 4.1.3.

Figure 72a and b show the two extremes, ‘no segmentation’ (mass marketing) and ‘atomistic

segmentation’ (individual marketing) of a market with six consumers. Considering three income

classes (1: ‘low’, 2: ‘medium’, 3: ‘high’ in Figure 72c), the market is segmented into three seg-

ments of different sizes. Three consumers have a ‘low’ income (1), one has a ‘medium’ (2) and

two have a ‘high’ income (3). In addition, the market can be segmented by the age of the con-

sumers (Figure 72d; A: ‘young’, B: ‘old’) into two segments. The use of both criteria, income

and age, divides the market into five market segments (1A, 1B, 2B, 3A and 3B in Figure 72e).

Figure 72: Sharp Market Segmentation

Market segments can be defined in many different ways. The different market segments shown

in Figure 72 are demographic segments. According to [Kotler and Keller 2005, p. 241], another

way to curve up a market is to identify preference segments. Three different patterns of prefer-

ences can emerge: homogeneous, diffused or clustered preferences (compare Figure 73).

1

1

1

2

3

3B

B

A

B

A

A 1B

1A

1A

2B

3B

3A

a) No segmentation (mass marketing)

b) Atomistic segmen-tation (individual marketing)

c) Segmentation of income groups (1: ‘low’, 2: ‘me-dium’, 3: ‘high’ income)

d) Segmentation of age groups (A; ‘young, B: ‘old’)

e) Segmentation of income and age groups

Source: adapted from [Kotler and Bliemel 2001, p. 417]


- 92 -

Figure 73: Basic Market-Preferences Patterns

However, the patterns in Figure 73 are theoretical and unrealistic illustrations. There are usu-

ally no homogeneous preferences in real markets and it is also rarely the case that markets

have clear, distinct and homogeneous clusters of different preferences. Real preferences

mostly range somewhere in between homogeneous, diffused and clustered preferences.

The sharp market segmentation in demographic segments shown in Figure 72 is also a prob-

lem. This approach assumes that it can be clearly distinguished between consumers with a

‘low’ (1), ‘medium’ (2) or ‘high’ income (3), and between ‘young’ (A) and ‘old’ (B) consumers.

However, how is a ‘low’, ‘medium’ or a ‘high’ income defined and when is a consumer

‘young’ and when ‘old’? Responses to these questions are usually sharp classifications shown Figure 74: people between 0 and 39 years are ‘young’, ‘middle-aged’ are between 40

and 59, and people over 60 years are ‘old’. An income between 0 and 2499 € is defined as

‘low’, a ‘medium’ income ranges between 2500 and 4999 €, and people with a ‘high’ income

earn more than 5000 €. Although Miller and Brown have nearly the same age (Miller 38 years

and Brown 41) and same income (Miller: 4920 € and Brown: 5100 €), Miller is classified as

‘young’ and his income as ‘medium’, but Brown as ‘middle-aged’ and his income as ‘high’.

Figure 74: Fuzzy Market Segmentation of Income and Age

a) Homogeneous preferences: market with no natural segments: all consumers have same preferences.

b) Diffused preferences: consumers of this market vary greatly in their preferences, and are scattered throughout the space.

c) Clustered preferences: market with three distinct preference clusters, so called

natural market segments.

a) b) c)

Source: adapted from [Kotler and Keller 2005, p. 242]

Und

er 4

0

40-5

9 ye

ars

O

ver 6

0

Age

0 1

μ yo

ung

μ ol

d

1000 2000 3000 4000 5000 6000 20’000

under 2'500 2’500 - 4'999 over 5'000

C3) C2)

Income (gross income per month, in €)

C1)

C6) C4)

C9) C8) C7)

80

75

70

65

60 55 50 45 40 35 30 25

20

μ high incomeμ low income

0

1 μ medium income

C6)

C2)

C9)

C8)

C3)

C5)

C1)

C7)

C4)

In

com

e

0 2499 2500 4999 5000 … low medium

Age

youn

g

mid

dle-

o

ld

ag

ed

0

4039

5960

...

μ m

iddl

e-ag

ed

C5)


Smith

Ford

Brown

Miller

Ford

Smith

Brown

Miller


- 93 -

This example points out: it does not make sense to classify the age and income of a person

sharply: why should a 39 year old person be labelled ‘young’, but a 40 year old one suddenly

‘middle-aged’? Why should a ‘middle-aged’ person of 59 years be classified abruptly as ‘old’

with 60? The transitions between the phases in one’s life are fluent. The membership func-

tions in Figure 74 reflect mathematically these fluent transitions between the phases. With

fuzzy classification, a person can be classified as ‘young’ and ‘middle-aged’ (respectively as

‘middle-aged’ and as ‘old’) at the same time, and the income can be categorised partially as

‘low’ and partially as ‘medium’ (respectively as ‘medium’ and as ‘high).

However, not only socio-demographic market segmentation but also other types and catego-

ries of segmenting criteria could or should be classified fuzzily. According to [Kotler et al.

2005, pp. 398ff], consumer markets can be segmented by geographic, demographic, psycho-

graphic and behavioural market segmentation (see Appendix 5, p. 141):

Geographic segmentation calls for dividing the market into different geographical units,

such as nations, states, regions, counties, cities or neighbourhoods.

Demographic segmentation consists of dividing the market into groups based on variables

such as age, gender, sexual orientation, family size, family life cycle, income, occupation

education, religion, ethnic community and nationality.

Psychographic segmentation divides buyers into groups based on social class, lifestyle or

personality characteristics.

Behavioural segmentation divides buyers into groups based on their knowledge, attitudes,

uses or responses to a product or service, or based on other criteria of purchase behaviour.

As shown in Appendix 5, most of the market segmentation variables are adequate for fuzzy

classification with discrete or continuous membership functions. Especially behavioural seg-

mentation variables are promising to define fuzzy market segments, marketing strategies and

marketing mixes. Figure 75 shows a general example how to define fuzzy market segments.

Figure 75: Fuzzy Market Segments and Strategies

μ high variable X μ low variable X

μ lo

w v

aria

ble

Y

μ

hig

h va

riabl

e Y

00

1

1

fS5; Strategy E

C2)

Fuzzy seg-ment 2 (fS2);

Strategy B

C1)

Fuzzy seg-ment 1 (fS1);

Strategy A

C4)

Fuzzy seg-ment 4 (fS4); Strategy D

C3)

Fuzzy seg-ment 3 (fS3); Strategy C

Market segmentation variable Y

0 0

1

1 Mar

ket s

egm

enta

tion

varia

ble

X

Market segmentation variable Y

Mar

ket s

egm

enta

tion

varia

ble

X

μ high variable Xμ low variable X

μ lo

w v

aria

ble

Y

μ

hig

h va

riabl

e Y

fS4; Strategy D

fS2; Strategy B

fS3; Strategy C fS1; Strategy A

- 94 -

Chapter 6

Fuzzy Credit Rating

Chapter 6: Fuzzy Credit Rating

- 95 -

6.1 Methods of Sharp Credit Rating 6.1.1 Definitions

Another example of customer or market segmentation is the process of credit rating, which

ends in a rating of the rating subject (for instance a loan applicant) in a rating or risk class.

To manage the credit business profitably, a bank has to classify loan applicants according to

their real creditworthiness (I 68), that means according to their default risk. It is in every bank’s

concern to evaluate loan applicants and their creditworthiness as good as possible.

Creditworthiness is the ability, intention and financial capability of a borrower to repay debt.

Credit scoring is a quantitative approach used to measure and evaluate the creditworthiness

of a loan applicant [Hofstrand 2006]. An aim of credit scoring is to determine the credit risk,

that risk assumed for the possible nonpayment of credit extended.

In Anglo-Saxon literature on credit scoring, often the C’s of credit are mentioned, e.g. Char-

acter, Capital, Capacity, Collateral and Condition.

In german literature, a distinction is often made between personal and material creditworthi-

ness. A borrower is considered as personally creditworthy, if he deserves confidence due to

its reliability, professional qualification and its business acumen.

Material creditworthiness is supposed, if the current or expected economical circumstances

of the borrower guarantee the payment of interests and the repayment of the loan.

The following sections discuss the internal rating of banks. Considering conventional methods

of credit rating, subjective expertise is compared to statistical methods.

6.1.2 Subjective Expertise

Subjective heuristics, expertise, checklists and systematic scoring models are widely

used methods of credit rating in banking practice. They contain a number of criteria about the

creditworthiness of a commercial or private borrower. In retail banking, following information

about the personal creditworthiness of a loan applicant for a consumer loan are examined:

age, civil and family status; number and age of the children

profession and qualification

employer, job circumstances and duration of employment

recovery of claims and garnishment

reason for credit.

Considering the material creditworthiness, following information is evaluated:

monthly net income, additional incomes and security of income

property


- 96 -

loan securities

account information at the internal or external bank

alimonies

rent or mortgage rate

leasing rates, other financial obligations and dept service.

Credit agreements often contain the following basic points: amount of credit, interest rate,

repayment conditions, credit period (duration of the credit) and loan securities.

6.1.3 Statistical Methods

In contrast to heuristic scoring, empirical or statistical credit scoring eliminates the subjective-

ness of loan officers. Statistical methods divide borrowers into two classes: customers are

either ‘not creditworthy’ or ‘creditworthy’. The membership to one of the classes depends on a

score based on a criteria catalogue, using a discriminant function.

Important statistical methods for credit rating are, for instance: multivariate discriminant analy-

sis, logit analysis and artificial neuronal network.

The multivariate discriminant analysis measures the dependence between different metric

variables (e.g. income) and a nominal grouping variable (‘not creditworthy’ or ‘creditworthy’). In

doing so, the following discriminant function f(z)i

is estimated that way, that the critical dividing value z* separates optimally the ‘creditworthy’

customers from the ‘not creditworthy’ ones (see Figure 76).

It is a challenge to estimate the coefficients (bK) of the discriminant function that the type I and

II errors (α- and β-errors) are as small as possible.

Figure 76: Discriminant Function and Type I and II Errors

iKK

iii xbxbxbbzf ⋅++⋅+⋅+= ...)( 22110

Group 0 (not creditworthy) Group 1 (creditworthy)

hycreditwortzhycreditwort notz

Type II Error (α-error):

A loan applicant receives a credit, although he is not creditworthy (/solvent)

Type I Error (β-error):

A loan applicant receives no credit, although he is creditworthy (/solvent)

Centroid not creditworthy Centroid creditworthy

Dividing value = critical discriminant value

f(z) z*


- 97 -

The error terms are low, if the centroids z of group 0 and 1 are far away from each other, and

if the variance within each group is very small (for details see [Backhaus et al. 2006]).

In contrast to discriminant analyses, logit analyses or loglinear models consider non-metric,

qualitative variables. They are suited for the analysis of the dependence of several non-metric

independent variables and a nominal grouping variable.

Artificial Neural Networks (ANN) try to reduce type I and II errors by linking logistic, non-

linear functions. A neuronal network can be defined as “a collection of neuron-like processing

units with weighted connections between the units” [Han and Kamber 2006, p. 24].

All neurons, linked to an ANN, receive, process and relay signals. The architecture of a neural

network consists of three layers shown in Figure 77.

Figure 77: Architecture of a Neural Network for Credit Rating

The input layer receives signals of the criteria ci. Neurons of the hidden layer are weighting

and processing each signal received from an input layer and relay them to another hidden or

to the output layer. The weights of the connections, which are adapted during the learning

process, play an important rule, since they represent the experienced knowledge. With in-

creasing training, the non-linear discriminant function fits optimally to the data set in Figure 78.

Figure 78: Discriminant Functions in Discriminant Analysis and ANN

Output data: credit applicant is not creditworthy or creditworthy

Output layer

Hidden layer

Input layer

Input data: creditworthiness criteria ci of a credit applicant c1

not creditworthy creditworthy

Neural network

c2 C3 c4

c1

c2

Separation with discriminant function

z*

c1

c2

Separation with artificial neural network

f(z) Not creditworthy creditworthy

Artificial neural networks achieve good classification results and they often have a higher hit quote than the

discriminant analysis

Source: adapted from [Kilb 2002, p. 51]

Further reading about credit rating with artificial neural network see for

instance: [Krause 1992, Füser 2001, Kilb 2002]


- 98 -

6.1.4 Disadvantages of Sharp Credit Rating

All considered methods of credit rating have certain advantages, and also disadvantages:

Heuristics and checklists are subjective and it is often discretionary to bring contradictory

statements together. Different credit experts mostly evaluate and weight the same credit

with identical facts quite different.

The disadvantage of the multivariate discriminant analysis is that only metric criteria can

be taken into account. According to [Kilb 2002], the quote of misclassifications of the dis-

criminant analysis in retail banking is relatively high.

Logit analyses or logistic regression analyses have the drawback that non-linear correla-

tions are difficult to model. Time invariance and representativeness of the models and the

data often is not guaranteed.

The crucial weakness of artificial neural networks is that the processes, results and the

relations between in- and output data are not transparent for the normal user. The hidden

layer of neuronal networks is a black box. As a result, artificial neural network are rarely

used for credit rating in business practice.

Statistical methods in general do not consider specific and individual circumstances and

particularities. As a result, statistical methods are often combined with subjective expertise

to hybrid methods in credit rating practice,.

Another disadvantage of subjective expertise and statistical methods of credit rating is that

they usually result in sharp classifications of loan applicants. The material or personal cred-

itworthiness, or other criteria for creditworthiness of loan applicants often are rated sharply as

‘worthy’ or as ‘not worthy’.

In critical or border cases, if a credit applicant is rated near the cut-off-score (the dividing value

which separates the creditworthy borrowers from the rest), sharp credit rating may lead to in-

correct and wrong decisions, to misclassifications as the type I and II errors are.

Only a little variation of one of the rating criteria may result in another decision concerning the

loan application.

In addition, the sharp rating of very different risks often results in the same overall credit rating

of a loan applicant. One the other hand, applicants with the same values of the attributes can

be aggregated to a different overall rating.


- 99 -

6.2 Methods of Fuzzy Credit Rating 6.2.1 Existing Literature on Fuzzy Credit Rating

Whether a loan applicant is creditworthy or not, is not a question that can be answered easily

with "yes or no", but rather with "more or less". It is in the nature of credit rating that informa-

tion and data are inconstant, incomplete, imprecise and fuzzy.

The first authors who recognised this and associated credit rating with fuzzy classification

were Hans-Jürgen Zimmermann and Peter Zysno [Zimmermann and Zysno 1982, 1983,

Zimmermann 1993, 1997, Zysno 1980]. They classified hierarchically the category ‘creditwor-

thiness’ into a four-level pyramid with different sub-categories (see Figure 79).

Figure 79: Hierarchy of Creditworthiness with Weights δ and Parameters γ

The fuzzy set ‘creditworthiness’ is defined as a composition of the other fuzzy sets. The model

by Zimmermann and Zysno includes remarkable findings. By testing empirically the discussed

compensatory operators γ and the weights δ, the prediction results of the creditworthiness of

test loan applicants with fuzzy classification were significantly better than with sharp classifi-

cation [Zimmermann and Zysno 1982].

In the last years other reviews about fuzzy credit rating were published. [Romaniuk and Hall

1992] developed an expert system (FUZZNET) by using a fuzzy connectionist model. Simi-

larly, [Levy et al. 1991] programmed a system to evaluate a company’s financial position.

[Hofmann et al. 2002] tested empirically different fuzzy approaches, genetic fuzzy classifiers

and neuro fuzzy algorithm. Their conclusion: fuzzy classifiers achieve better credit rating re-

sults than neuro fuzzy or conventional, sharp algorithms like C4.5.

[Chen and Chiou 1999] chose a similar approach as this thesis does. In their model they de-

fine fuzzy sets, membership functions, membership degrees to five rating levels (A to E), lin-

guistic variables and a fuzzy integral (i.e. an evidence fusion technique).

Material creditworthiness (δ = 1.05)

Loan securities (δ = 0.71) Liquidity (δ = 1.39)

Property minus long term debts

(δ = 0.61)

Potential (δ = 0.93) Business behaviour (δ = 0.97)

Other net property

(δ = 0.81)

Creditworthiness

Income minus

expenses

(δ = 1.53)

Personal creditworthiness (δ = 0.95)

Motivation

(δ = 0.80)

Economic thinking

(δ = 1.01)

Conformity social & econo-mic standards

(δ = 0.93)

Physical & mental

potential

(δ = 1.06)

Continuity of margin

(δ = 1.26)

(γ = 0.59)

(γ = 0.55) (γ = 0.60)

(γ = 0.90)

(γ = 0.78)

(γ = 0.92) (γ = 0.76) (γ = 0.99) (γ = 0.58) (γ = 0.55) (γ = 0.55)

Source: adapted from [Zimmermann 1993, p. 366]


- 100 -

The model was applied to a credit rating system in Taiwan, operated for small and medium

enterprises. The evaluation criteria of this credit rating system were modelled as the hierarchi-

cal decision structure shown in Figure 80.

Figure 80: Credit Rating Hierarchy with the Degree of Importance gi of each Criterion

[Chen and Chiou 1999] proved that with the fuzzy approach the overall credit rating is more

sensitive to changes of credit information, especially to small changes of a single criterion.

“Moreover, the description of the final credit-rating results in terms of the membership degrees

of the five rating levels can provide loan officers with more valuable information in decision

making” [Chen and Chiou 1999, p. 416].

As a result, credit management or contractual terms and conditions, like the amount of credit,

credit period or the interest rate, can be better adapted and aligned to the exact credit infor-

mation and the default risk of a borrower.

The next section will discuss another fuzzy credit rating approach using the fuzzy Classifica-

tion Query Language (fCQL) and a credit rating hierarchy for the bank’s internal credit rating of

private borrowers.

Overall credit level

Financial conditions (g1 = 0.68)

General Management(g2 = 0.55)

Characters & Perspectives

(g3 = 0.44)

Administrations personal credit (g21 = 0.23) Administrations experiences (g22 = 0.16)

Stockholders’ structure type (g23 = 0.11)

Ø sale growth rate last three years (g24 = 0.16) Condition of capital increment (g25 = 0.10)

Outstanding check records (g26 = 0.24)

Equipment & technologies (g31 = 0.24)

Product marketability (g32 = 0.31)

Collateral (g33 = 0.20)

Conditions of industry next y. (g34 = 0.25)

Financial structure ratios (g12 = 0.60)

Profitablitity ratios (g13 = 0.52)

Efficiency ratios (g14 = 0.45)

Quick ratio Current ratio

Debt ratio Long-term asset efficieny ratio

Interest expense to net sales Profit margin before tax Return on net worth before tax Inventory turnover Receivable turnover Total assets turnover

Liquidity ratios (g11 = 0.60)

Source: [Chen and Chiou 1999, pp. 408ff]


- 101 -

6.2.2 Fuzzy Credit Rating with fCQL

The discussed sharp and fuzzy methods of credit rating so far, can be classified in Figure 81.

According to [Füser 2001], fuzzy logic can be categorised as a method of Artificial Intelligence

(AI), beside Artificial Neural Networks (ANN), expert systems, and others.

Figure 81: fCQL as a Method of Artificial Intelligence

As discussed in the second chapter, with fuzzy logic, vague or imprecise linguistic variables of

human thinking and colloquial language can be mathematically reproduced and they become

formally accessible for computers and for an information or expert system.

The approach of fuzzy classification and fCQL allows to work with continuous and discrete

variables and the definition of colloquial terms of credit rating, like ‘definitely creditworthy’,

‘rather creditworthy’ or ‘insufficient creditworthy’. Considering continuous variables, even more

precise statements can be made: for example ‘30% moderately creditworthy’, ‘60% creditwor-

thy’, ’80% rather creditworthy’, and so on.

However, the approach of fuzzy classification and fCQL is to be distinguished from artificial

neuronal networks, expert systems and from other methods of AI (in Figure 81).

Based on an example of credit rating practice, a hierarchy of creditworthiness will now be dis-

cussed. It is assumed that the ‘overall creditworthiness’ of a loan applicant on the top level (L1

in Figure 82) of the hierarchy of creditworthiness is defined by ‘personal creditworthiness’

and ‘material creditworthiness’ on the second level (L2). On the third level (L3), personal

creditworthiness includes the ‘family situation’ and ‘employment’, on the one hand. On the

other hand, material creditworthiness contains the ‘income’ and ‘outgoings’. These four ag-

gregations are defined by a number of different criteria of credit rating on the fourth level (L4).

They are usually asked for in an application form for a private credit and analysed by the bank.

Sub

ject

ive

met

hods

S

tatis

tical

m

etho

ds


Neuronal expert system

Artificial neuronal networks

NFE System

Neuro fuzzy

models

Fuzzy logic

Fuzzy credit rating

static, if-when-rules

Fractal geometry

SQL

Theories of evolution

Genetic algorithms

Chaos theory

Subjective expertise & checklists

Scoring models (point rating systems)

Univariat analysis (traditional financial analysis)

Multiple discriminant analysis

Logit models

Conventional methods Methods of Artifical Intelligence (AI)

fCQL Expert

systems Synergetic

Sharp credit rating

Relational database system

Fuzzy expert systems

fuzzy information processing

fuzzy database queries

fCQL toolkit

Source: adapted from [Füser 2001, p. 270]

adaptive, non-linear


- 102 -

Figure 82: Practice-Related Example of a Hierarchy of Creditworthiness

Regarding the income, for instance, the loan applicant has to answer his income level, addi-

tional income, property, loan securities (e.g. surety, guarantee, lien and mortgage), account

information by the internal or external bank, and other criteria.

Following a bottom-up approach of fuzzy credit scoring, all predefined, and weighted scores of

all the criteria on the bottom level L4 are evaluated by bipolar rating scales.

Qualitative attributes or criteria of credit rating are assigned to discrete domains and member-

ship functions. Figure 83a shows an example of a discrete, six-level rating scale to determine

and rate the attribute ‘loan securities’. The other example, Figure 83b, represents a continuous

scale with continuous membership functions of the quantitative attribute ‘income’. To each

value of the membership functions a number of scores is assigned.

Figure 83: Examples of a Qualitative and a Quantitative Attribute of Fuzzy Credit Scoring

The evaluated score S(Aijkl) of all rating scales of the different attributes (Aijkl) considered in L4

are accumulated with following scoring function.

The scores on the fourth level (L4) define the scores on the third level (L3), ‘family situation’

S(A311), ‘employment’ S(A312), ‘income’ S(A321), and ‘outgoings’ S(A322).

Overall creditworthiness

Personal creditworthiness Material creditworthiness

Age Civil status Family status Living conditions Number/age of children etc.

Profession Qualification Employer Duration of employment etc.

Family situation Employment

Net income Security of income Property Loan securities Account information etc.

Rent or mortgage rate Alimonies Financial obligations Leasing rates Reason for credit etc.

Income Outgoings

L1:

L2:

L3:

L4:

Loan securities

insufficient sufficient

Income

low

Linguistic variable(Attribute)

Terms

Domain

Scale

Score

Context

Membershipfunctions

high

0 3499 3500 ≥10'000

[0, ... , 3'499] [3'500, ... , 10'000] Equivalence class

0.1 scores per 0.01 value of μ high e.g.: 0.56 = 5.6 scores

Equivalence class 0

0.440.56

1μ low μ high

0 0.2 0.4 0.6 0.8

1

very low low rather low rather high high very high

[very low, low, rather low] [rather high, high, very high]


0 2 4 6 8 10

μ insufficient μ sufficient

a) b)


- 103 -

(Scoring function)

The scores on the third level L3 define the scores of the second level (L2) and first level (L1) of

the hierarchy, that means the personal (S(A21)) and material (S(A22)) and the overall creditwor-

thiness (S(A1)). Appendix 6 (see p. 142) shows formal details, i.e. all attributes, levels, classes,

terms and domains of the scoring function and the hierarchy of creditworthiness.

This example of hierarchical fuzzy credit scoring can also be shown in a matrix: the left side of

Figure 84 shows a fuzzy classification of the ‘overall creditworthiness’, the right side the fuzzy

classification of ‘material creditworthiness’ and, at the bottom, the ‘personal creditworthiness’.

The membership degrees of the classified loan applicants Ford, Smith, Brown and Miller can

be calculated on the different levels Li of the hierarchy of creditworthiness.

This hierarchical fuzzy credit classification approach has the advantage that the degree of

creditworthiness can be queried on the different levels of the hierarchy.

If the rating of one or several levels of the creditworthiness of a bank customer degrades, a

trigger mechanism warns the manager. In addition, possible developments of a customer’s

creditworthiness can be simulated with different scenarios (e.g. worst, realistic or best case).

Figure 84: Hierarchical Fuzzy Classification of Creditworthiness

∑∑∑∑=

=

=

=

=

=

=

=

+++=5

1 l422l

5

1 l421l

5

1 l412l

5

1 l411lijkl )S(A)S(A)S(A)S(A )S(A

nnnn

Family situation S(A311)

Employment S(A312)

Income S(A321)

Outgoings S(A322)

Personal creditworthiness S(A21) Material creditworthiness S(A22)

Overall creditworthiness S(A1)

L4:

L3:

L2:

L1:

C2-2)

Outgoings not creditworthy

C2-4) Materially not credit-

worthy

C2-1)

Materially creditworthy

C1-2)

Employment not credit-

worthy

C1-1) Personally

creditworthy

C1-4) Personally

not creditworthy

Income (Score)

Out

goin

gs (S

core

)

0 24 25 50

0

24

25

50

Family situation (Score)

Em

ploy

men

t (S

core

)

0 24 25 50

0

24

25

50

C2-3)

Income not creditworthy

C1-3)

Family situation not creditworthy

Ford

Ford

Smith

Brown

Brown

Miller

Miller

Smith

C3) Materially not creditworthy;

Interest rate: iC3 = 14%

C2) Personally not creditworthy


C1) Creditworthy


C4) Not creditworthy;

no credit (interest rate: iC4 = 18%)

100

50 49

0

μ pers. creditworthy

Material creditworthiness (Score)

Per

sona

l cr

editw

orth

ines

s (S

core

)

μ pers. not creditworthy

μ m

at. n

ot c

redi

twor

thy

0

μ m

at. c

redi

twor

thy

0 49 50 100

Smith Brown

Miller

Ford

100% creditworthy

100% not creditworthy

0

1

34.8% creditworthy30.2% pers. not creditworthy19.1% mat. not creditworthy

15.9% not creditworthy

18.5% creditworthy 24.8% pers. not creditworthy 24.8% mat. not creditworthy 31.9% not creditworthy

0.6

0.4

0.6

0.4

0.44

0.56

0.72 0.28

1


- 104 -

In addition, fuzzy classification or credit scoring enables the calculation of individual interest

rates, i.e. of individual risk premiums. To each class of the fuzzy classification in Figure 84 an

interest rate is assigned; for example: C1: 6%, C2: 10%, C3: 14% and C4: 18%.

The premium is to reflect the risk class as accurate as possible. According to their degree of

creditworthiness, for each of the loan applicant an individual interest rate can be calculated: Interest rate iFord = 1·18 = 18%.

Interest rate iSmith = 0.185·6 + 0.248·10 + 0.248·14 + 0.319·18 = 12.8%

Interest rate iBrown = 0.348·6 + 0.302·10 + 0.191·14 + 0.159·18 = 10.6%

Interest rate iMiller = 1·6 = 6%

Classified sharply, Smith belongs entirely to C4 and has to pay a very high risk premium of

18%, like Ford, although Smith is nearly in the same position as Brown who is classified in C1

and enjoys good conditions: he has to pay an interest rate of 6% only. Although Miller is much

more creditworthy than Brown, he is also classified in C1 and pays the same interests of 6%.

With a fuzzy calculated interest rate, customer Smith is not disadvantaged any longer in

comparison to Brown, but still has to pay quite high interests (of 12.8%). Brown is no longer

privileged and has to pay 10.6%. Miller still has to pay 6% only.

Fuzzy classification enables a fair and non-discriminatory calculation of risk premiums for loan

applicants or bank customers, according to their creditworthiness or default risk.

However, in the case of the fuzzy credit rating, the bank manager or the loan officer still is

confronted with the problem he may not allow a credit beyond a certain limit of default risk

or lack of creditworthiness. This limit (cut-off-score) can be fixed at a certain level of the inter-

est rate (e.g. no higher interest rates than 15%). Alternatively, the credit is refused, if the fuzzy

calculated membership degree of a classified loan applicant to C1 decreases, for instance,

under 10%, or if the membership degree to C2, C3 or to C4 increases over 50%.

Depending on the requirements of bank, or on the knowledge of the loan officer, or depending

on the situation, the credit rating can be more or less strictly.

According to [Gachet 2006], not only individual interest rates can be calculated with fuzzy

classification, but also an individual credit period.

Going one step further, the individual interest rate could be calculated based on the attribute

‘creditworthiness’ (with the terms ‘sufficient’ or ‘excellent’), ‘credit period’ (‘short-term’ and

‘long-term’) and on the attribute ‘amount of credit’ (‘small credit’ or ‘large credit’). Figure 85

shows such a three-dimensional fuzzy classification in credit rating.

To each of the classes an interest rate is assigned under following realistic assumptions: the

nominal interest rate is lower for

long-term credits than for short-term credits

large credits than for small credits and for

clients with an excellent creditworthiness than for clients with a sufficient creditworthiness.


- 105 -

Figure 85: Thee-Dimensional Sharp (a) and Fuzzy (b) Credit Rating

The attributes, equivalence classes [in squared brackets] and the terms (in round brackets) of

the eight classes C1 to C8 in Figure 85a) and the different interest rates are shown in Table 16.

Table 16: Interest Rates for Different Loan Categories

Attributes, [equivalence classes] and (terms) Creditworthiness Amount of credit Credit period Class

[Score] (Term) [Euros] (Term) [Months] (Term)

Interest rate

C1 [100, 200] Excellent [20'000, 100'000] Large credit [12, 35] Short-term 8% C2 [100, 200] Excellent [3000, 19’000] Small credit [12, 35 ] Short-term 10% C3 [50, 99] Sufficient [20'000, 100'000] Large credit [12, 35] Short-term 12% C4 [50, 99] Sufficient [3000, 19’000] Small credit [12, 35] Short-term 14% C5 [100, 200] Excellent [20'000, 100'000] Large credit [36, 60] Long-term 5% C6 [100, 200] Excellent [3000,19’000] Small credit [36, 60] Long-term 7% C7 [50, 99] Sufficient [20'000, 100'000] Large credit [36, 60] Long-term 8% C8 [50, 99] Sufficient [3000, 19’000] Small credit [36, 60] Long-term 9%

If the customers from the example above were applying for a credit, they are sharply classified

to the classes of the following Table 17:

Table 17: Sharp Classification of the Loan Applicants

Attributes, [values of the loan applicants] and (terms) Creditworthiness Amount of credit Credit period Loan applicant Class

[Score] (Term) [Euros] (Term) [Months] (Term)

Interest rate

Smith C3 97 Sufficient 24'000 Large credit 32 Short-term 12% Ford C4 10 Sufficient 16'000 Small credit 18 Short-term 14% Brown C5 109 Excellent 30'000 Large credit 38 Long-term 5% Miller C5 189 Excellent 90'000 Large credit 54 Long-term 5%

Although Smith and Brown have similar values (their score of creditworthiness: 97, 109; their

amount of credit: 24'000, 30'000 €; their credit period: 32, 38 months), they are classified in

different classes with different interest rates. Smith (12%) has to pay a 2.4 times higher inter-

est rate than Brown (5%). Miller, with 80 scores more, a three times higher amount of credit

and a contract which is two years longer than Browns, has to pay the same 5%.

Credit period (months)

Amount of credit (in thousand €)

Creditworthiness (Score)

010

1 μ small credit μ large credit

μ ex

celle

nt

μ su

ffici

ent

Ford

Smith Brown

μ short-term

C5) 100%

μ long-term

Müller

Amount of credit (thousand €)

Long-term

Short-term

E

xcel

lent

Large credit Small credit

Creditworth-iness (Score)

Brown, Miller C5) 100% Smith

C3) 100% Ford C4) 100%

Suffi

cien

t

Credit period (months)

3 19 20 100 50

99 100

200

12

35 36

60

3 19 20 100 55

99 100

200

12

35

a) b)

60

36

Smith

C3) 42.9% C4) 57.1%

C1) 12.8% C2) 9.6% C3) 11.2% C4) 8.3% C5) 17.5% C6) 13.4% C7) 15.4% C8) 11.7%

C1) 14.9% C2) 13.1% C3) 17.3% C4) 15.2% C5) 9.7% C6) 8.4% C7) 11.5% C8) 9.9%


- 106 -

With the fuzzy classification approach (compare Figure 85b), such discrepancies are elimi-

nated and a exact, individual interest rate can be calculated, according to the values of the

attributes ‘creditworthiness’, ‘credit period’ and to ‘amount of credit’ of a loan applicant: Interest rate iFord = 0.429·12 + 0.571·14 = 13.14%

Interest rate iSmith = 0.149·8 + 0.131·10 + 0.173·12 + 0.152·14 + 0.097·5 + 0.084·7 + 0.115·8 + 0.099·9 = 9.59%

Interest rate iBrown = 0.128·8 + 0.096·10 + 0.112·12 + 0.083·14 + 0.175·5 + 0.134·7 + 0.154·8 + 0.117·9 = 8.59%

Interest rate iMiller = 1·5 = 5%

Classified fuzzily, Smith is to pay a lower interest rate of 9.59% than in a sharp classification

(12%). Ford is also to pay a slightly lower rate of 13.14% (sharp: 14%). In contrast, Brown has

to pay a higher rate of 8.59% (sharp: 5%). For Miller the interest rate (5%) remains the same.

6.2.3 Other Applications for Fuzzy Classification in Banking

Considering other important criteria of bank customer in private banking, e.g. a customer’s

assets, capital, turnover, profit, lifetime value or his cross-selling potential, fuzzy classification

is interesting for offering offer individualised and personalised services or bank products. Such mass customisation, based on the fuzzy classification approach, leads to added-value

for the customer, may to higher customer retention (i.e. profits) and to competitive advantages

for the bank.

Other banking sectors, e.g. the credit rating of companies, are also promising for fuzzy classi-

fication. The credit rating of companies or business customers is much more complex than

the rating of private customers, since the evaluation deals with many different rating criteria

and standards. In this case, the application of fuzzy classification is even more pertinent than

the credit rating of private loan applicants discussed in this chapter.

- 107 -

Chapter 7

Conclusion

Chapter 7: Conclusion

- 108 -

7.1 Summary Conventional data analysis methods in statistics and data mining have certain disadvantages:

classified elements are mostly assigned sharply to one single class. Such sharp classifications

can be arbitrary, problematic, incorrect and unfair, as many examples of the master thesis

explained: although elements had nearly the same values, they were classified in different

classes. In contrast, elements with very different values can be classified in the same class.

In business practice, such misclassifications and discrepancies may have negative effects, for

instance, if a key account is managed in the same way as an unprofitable customer, or if two

similar customers notice that they are treated differently by the company.

With fuzzy classification, these problems do not occur. Fuzzy classification and fCQL (fuzzy

Classification Query Language) combine fuzzy logic and relational databases and allow the

classification of customers, or any other elements, into more than one class at the same time.

Defining attributes, terms and fuzzy sets, which are determined by membership functions over

the whole domain of an attribute, the classification space becomes fuzzy. That means that the

classes sharp borders disappear and there are continuous transitions between the classes.

The fuzzy approach with fCQL has further advantages: it enables the reduction of complexity

without loss of information, the use of numerical (quantitative) and non-numerical (qualitative)

values, a clear semantic and a human-oriented query process with linguistic variables and

terms. In addition, dynamic and multidimensional fuzzy classifications can be undertaken.

This thesis has basic questions and answers. Following basic findings can be summarised:

‘Where’ can fuzzy classification be used in business management?

It could be used, for instance, in different fields of marketing ( Table 18; RQ1) and of

Customer Relationship Management (CRM; Table 20; RQ3)

‘How’ could fuzzy classification be applied?

Combined with management tools, fuzzy classification can be applied as fuzzy portfolio analysis,

fuzzy SWOT analysis, fuzzy ABC analysis and as fuzzy scoring method ( Table 19; RQ2).

‘What for’ can fuzzy classification be used?

It is especially suited for Customer Performance Measurement (CPM; Table 21 RQ4), using

relevant Customer Performance Indicators (CPI; Table 22; RQ5). Since the classification of

the indicators is a precondition for the analysis, evaluation and segmentation of customers ( Table 23; RQ6), fuzzy classification is a crucial task of customer performance measurement.

In addition, fuzzy customer segmentation can be used for credit rating ( Table 24; RQ7).

‘Why’ should fuzzy classification be used?

In contrast to sharp methods, fuzzy classification avoids misclassifications, enables a more precise

classification and management of customers according to their values, an improved exploitation of

customer potential and a better allocation of limited ressources for effective and efficient CRM.


- 109 -

Table 18: Results of Research Question (RQ) 1

What are potential fields and topics for business applications for fuzzy classification in general?

1

Fuzzy classification (fc) supports generally the management functions analysis and control;

for instance, the analysing and controlling of performance indicators in CRM and marketing.

As summarised above, fc is suited for Customer Relationship Management (CRM; Section 4.1;

RQ 2), Customer Performance Measurement (CPM; Section 4.2; RQ 3), fuzzy customer segmentation ( Section 5.1; RQ 4) and for fuzzy credit rating ( Section 6.2; RQ 6).

Data of market research can be used for fuzzy market segmentation ( Section 5.2).

Fuzzy classification can be applied to widely used management tools and methods ( RQ 5),

as fuzzy portfolio analysis ( Section 3.2), fuzzy SWOT analysis ( Section 3.3; fuzzy Strength,

Weakness, Opportunity and Threat/risk matrix), fuzzy ABC analysis ( Section 3.4), fuzzy scoring

methods or fuzzy RFM method ( Section 3.5) and as fuzzy cost-benefit analysis.

Empirical research confirms that CRM, customer segmentation and performance management

became very important in business practice. These concepts are also most suited for the ap-

plication of fuzzy classification, as discussed in Chapters 4 and 5, and shown in Figure 86.

However, the most promising topic for the application of fuzzy classification is customer

portfolio analysis, which is easy to understand and implement, and widely used in practice.

According to a study of [Krafft 2007], 44% of all firms use customer portfolios analysis. 32% of

the firms analyse customer portfolios regularly, 48% sporadically [Reinecke and Herzog 2006].

Figure 86: Promising Management Tools, Methods and Concepts for Fuzzy Classification

Suitability for fuzzy application

Portfolio analysis

Importance in business practice

Management tool Statistical or data mining method Marketing concept or field

Conventional or sharp approach

Regression analysis

Mass marketing

Mass customisation Analysis of

variance

Fuzzy classification management tool Fuzzy statistical or data mining method

Fuzzy application to marketing concept or field Sharp or fuzzy application possible

Artificial neural network

Cluster analysis

Decision trees

Correlation analysis

Multidimensional scaling

Performance measurement

ABC-analysis

Scoring- models


Discriminant analyse

Customer profiling

Personalisation

Relationship marketing

Balanced scorecard

Transaction marketing

Credit rating

Logit- analysis

Benchmarking

Supply chain management Total quality

management

Mission and vision statement

Strategic planning

Business process reengineering

CRM

Five-force- model

SWOT-analysis...

Core competences

...

...

Conjoint-analysis

Market segmentation

...

... Value chain

Cost-benefitanalysis

Association analysis

Promising topics

...

Mikromarketing One-to-one marketing Individual marketing

Customised marketing

... ...


- 110 -

Figure 87: Fuzzy Classified Customer Portfolio (a) and Fuzzy ABC Analysis (b)

Conventional portfolio analysis is applied in such a way that sharp classes are defined, which

means that classified elements belong to one single class only. For instance, a customer is

classified as a ‘star’ (Brown in Figure 87a) or as ‘renunciation customer’ (Smith). The problem:

Brown and Smith are classified in different classes, although they have a similar values of the

attributes. With fuzzy classification, the different values of customers are taken exactly into

account: Brown and Smith belong partly to all four classes of the portfolio at the same time.

Besides portfolio analysis, the ABC analysis is used by the majority of companies to segment

customers, products or other objects. Surprisingly, the ABC analysis has also been applied in

a sharp manner so far, although it is problematic to distinguish sharply between A-, B- and C-

customers (Figure 87b). Classified sharply, Miller is an A-, and Brown a B-customer, although

they have both nearly the same turnover. If a customer (Brown) had little higher turnover, may

be only a few euros, he would slide into class A and might be managed differently as a key

account. In contrast, customers with different turnover can be classified in the same class of a

sharp ABC analysis: Smith is classified as a B-customer although his turnover is much lower

than Brown’s. In a fuzzy ABC analysis, the transitions between A-, B- and C-customers be-

come fluent and the customers can be precisely classified according to their real turnover.

Fuzzily, Brown and Miller are partly A- and B-customers; Smith belongs partially to B and C.

After the discussion of different fuzzy classification management tools (compare Table 19),

CRM and possible fields, tasks or processes for fuzzy classification (see Table 20), Customer

Performance Measurement (CPM) turns out to be an important task of analytical CRM.

CPM is defined here as the acquisition, analysis and the evaluation of performance-related

customer information, using Customer Performance Indicators (CPIs), i.e. customer-related

monetary or non-monetary criteria (measures, metrics, indices, figures or ratios) about cus-

tomer performance. Since it is a problem to classify and evaluate customer performance

sharply, a fuzzy classification of all indicators in CPM is suggested.


0

1μ A μ C 1μ B

Miller

Brown

0

Cum

ulat

ive

shar

e of

cus

tom

ers

Cumulative turnover

C2)

Development customers

(question marks)

C1)

Star customers

(stars)

C4)

Renunciation customers

(poor dogs)

C3)

Absorption customers (cash cows)


0 0

1

1

Com

petit

ive

posi

tion

μ strongμ weak

μ un

attra

ctiv

e μ

attra

ctiv

e

Smith Brown

Miller

100%

renunciation

Smith

Ford 14% C1) 21% C2) 26% C3) 39% C4)

35% C1) star 24% C2) development 25% C3) absorption 16% C4) renunciation customer

100% C1) star customer

a) b)


- 111 -

Table 19: Results of Research Question 2

What are potential management tools and methods for fuzzy classification? ( Chapter 3)

2

Fuzzy portfolio analysis ( Section 3.2) facilitates the classification and management of cus-

tomers, businesses, Strategic Business Units (SBUs) or Fields (SBFs). Further advantages are:

- dynamic analysis and monitoring of a portfolio and implementation of a trigger mechanism

- fuzzy investments proportionally to the membership degrees of business units to each class

- balancing of portfolio (of cash flow producing and requiring business units) by defining a

balance indicator and the implementation of an trigger mechanism, which warns if the

portfolio is not balanced (portfolio with insufficient actual cash flow or future potential or both)

- improved management of businesses and better allocation of limited ressources.

Fuzzy SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) ( Section 3.3):

- fuzzy strength matrix: fuzzy classification of strategic strengths (competitive advantages)

- fuzzy weakness matrix: classification of primary weaknesses (competitive disadvantages) - fuzzy opportunity matrix: fuzzy classification of high opportunities (for investments) and

- fuzzy threat/risk matrix: fuzzy classification of major threats/extreme risks (risk management).

Fuzzy ABC analysis ( Section 3.4): fluent transitions between A-, B- and C-customers allow a

fair segmentation of customers according to their real turnover (or according to other indicators).

Combination of fuzzy portfolio and ABC analysis and definition of different customer strategies.

Fuzzy scoring methods ( Section 3.5): discussion of a sharp Recency Frequency Monetary value method (problem: 20% higher score of Miller despite same purchase behaviour as Brown)

- development of an improved fuzzy RFM method and the fuzzy RFM scoring of customers

- fuzzy calculation of RFM incentives and personalised accounts.


What are potential fields, processes & instruments for fuzzy classification in CRM? ( Section 4.1)

3

Promising fields and concepts for the use of fc marketing and customer management are:

customer acquisition and retention; individual or one-to-one marketing; micromarketing;

key account management ; mass customisation; personalisation; customer profiling.

The possible use of fc in customer-oriented tasks and processes of CRM: customer specific

information or advertising; personalised campaigns, loyalty or recovery programs; customisation

of products; fuzzy calculation of individual prices, accounts, incentives, paying conditions or war-

ranties; customer specific services and customer care according to their value to the company.

Definition of a "small CRM success chain" (from customer orientation to shareholder value).

Conclusion: the most promising fields for fuzzy classification in CRM: analytical CRM (aCRM)

and Customer Performance Measurement ( Section 4.2; RQ 4).

According to Table 21, CRM and CPM require a comprehensive number of customer perform-

ance indicators to manage and maximise customers as an asset. This work collected, defined,

operationalised and discussed 170+ Customer Performance Indicators (CPIs; see Table 22,

Appendix 4, pp. 136ff), which can be measured on an individual and on an aggregated level.


- 112 -


What are the benefits of fuzzy classification in customer performance measurement? ( Section 4.2)

4

Fuzzy classification enables fair and exact Customer Performance Measurement (CPM).

CPM is a precondition to manage and maximise customer as an asset of the company.

Different dimensions and characteristics can be taken into account for CPM and fuzzy

classification: unit (monetary/non-monetary), format (quantitative/qualitative), planning

interval (short-/long-term), time (ex-post/ex-ante), alignment (internal/external), incentives

(variation/improvement), CRM layer (operational/strategic) and aggregation level (low/high).

Consideration of empirical studies of customer performance measurement in business practice.

Conclusion: an adequate number of Customer Performance Indicators (CPIs) is required to

manage customer best possible ( Section 4.3; RQ 5).


What are important customer performance indicators for CPM? ( Section 4.3)

5

170+ Customer Performance Indicators (CPIs) were defined, discussed and operationalised

( Appendix 4, pp. 136ff).

Description of different functions of CPIs and requirements for indicators. Discussion of six different categories of indicators:

1) Customer Performance Indicators of Revenue and Profitability (CPIP), such as customer

turnover, contribution margins I-IV, gross/net profit, customer equity or customer lifetime value

2) Customer Investment Indicators (CII): customer costs of acquisition, retention and recovery;

transaction, service, communication, contact, marketing and total customer costs; return on’s

3) Customer Relationship Indicators (CRI), e.g. customer value, satisfaction, loyalty or retention

4) Customer Recommendation Indicators (CReI), e.g. number/intensity of recommendations

5) Customer Information Indicators (Cinfl), e.g. number/quality of suggestions or complaints

6) Customer Cooperation Indicators (CCI), e.g. intention to or expertise for cooperation.

Classification of the 170+ CPIs into a "big CRM success chain" ( Figure 42; p. 60).

Fuzzy classification and fuzzy Classification Management Tools (fCMT), like the fuzzy portfolio

analysis or ABC analysis and scoring methods, are general instruments to analyse and clas-

sify customer performance indicators. In addition to these methods, customer contribution

margin accounting and the calculation of customer equity or of Customer Lifetime Value

(CLV) are possible tools to measure customer performance and customer attractiveness.

By responding to central questions of each tool (see Figure 88) and defining adequate Key

Customer Performance Indicators (KCPI), companies can enhance their customer perform-

ance measurement in order to improve the quality and profitability of customer relationships. In

addition, customer performance measurement enables fuzzy customer segmentation, which

is defined as the fuzzy classification of the company’s current customers into similar, fuzzy

segments, using different customer performance indicators.


- 113 -

Figure 88: Tools and Indicators for Customer Performance Measurement

Two different approaches can be used to define fuzzy segments. Any class of a one-, two- or

multidimensional fuzzy classification can be defined as a fuzzy customer segment. Another

way is to define fuzzy clusters according to the patterns of the data, using fuzzy algorithms.

The definition of fuzzy segments has important outcomes. Firstly, customers can partly belong

to different segments at the same time. If customer strategies are assigned to fuzzy segments,

customers have to be managed according to several, may be contradictory strategies at same

time. This forces customer managers to combine, align and personalise customer strategies.

However, to avoid contradictions, basic requirements should be well defined. For instance, the

company defines the basic requirement that a customer has to be profitable in the long term or

that his creditworthiness has to exceed a certain level.

Fuzzy scoring or RFM method

Section 3.5)

C1)C2)

C3)C4)

C5) C6)

C7)

Mon

etar

y va

lue

Rec

ency

Fre-quency

Customer contribu-tion margin analysis

(Subsection 5.1.14)

Calculation of customer equity andCustomer Lifetime

Value (CLV) (Subsection 5.1.16)

Fuzzy customer portfolio analysis

(Section 3.2)

Fuzzy customer ABC-analysis

(Section 3.4)

Cus

tom

er P

erfo

rman

ce In

dica

tors

(CP

Is)

(Sec

tion

4.3)

C4)

C2)

C1)

C3)

Indicator Y

Indi

cato

r X

Fuzz

y cu

stom

er a

naly

sis

∑= +

−=

n

tttt

iERCLV

0 )(1)(

Which performance indicators are consid-ered in fuzzy portfolios?

What are the member-ship degrees of one/all customers to each class?

How can the portfolios be optimised?

Who are the customers with the highest/lowest turnover or profit?

How many customers generate how much of the turnover or profit?

How are A-, B- or C-customers managed?

Which indicators are considered in the scoring model & how weighted?

Which customers pur-chase most recently, fre-qent and at a high value?

Which incentives are offered to improve RFM?

How can customer contribution margins I-IV can be improved?

Which (non-/)monetary indicators are taken into account to model and calculate CLV or customer equity?

A-customers B-customers

C-customers

Cumulative turnover/profit

Cum. share of customers

Cen

tral q

uest

ions

Customer Performance Measurement (CPM) and management

(= analysis, planning, implementation and control)

Measurement level Examples of indicators

Turnover (I 31), cash flow (I 30), contribution margins I-IV (I 70 - I 73), customer gross or net profit (I 75,I 76), customer equity, CLV (I 79,I 80), # of new customers (I 4), # of customers (I 7), market share (I 43) Repurchases (recency, frequency, rhythm; I 20-I 28), cross-/up-selling (I 50-I 53), share of wallet, penetration (I 39,I 38), payment behaviour (I 66), recommendations (I 156), complaints (I 164), duration of CR (I 147) Repurchase intentions (I 55), cross-buying intentions (I 54), intention to recommend (I 151), intention to switch (I 141), customer loyalty (I 134), intention to dialog (I 157), intention to cooperate (I 171) Perceived product (I 118), service (I 120) and relationship quality (I 149), or price-performance ratio (I 122), image (I 115), customer value (I 123 - I 125), customer satisfaction (I 126), commitment (I 131), trust (I 133) Marketing costs (I 101), total customer costs (I 102), acquisition, reten-tion and recovery costs (I 87-I 92) administration (I 93), transaction (I 94), contacts (I 100), (after) sales (I 95), logistic (I 96), service (I 97) costs

Customer attitude

Behaviouralintentions

Customer behaviour

Customer results

Customer investments

Category

Cus

tom

er R

elat

ions

hip,

Rec

omm

en-

datio

n, In

form

atio

n, a

nd C

oope

ratio

n In

dica

tors

(CR

I, C

InfI,

CR

eI, C

CR

)

Cus

tom

er P

erfo

rman

ce In

dica

tor f

or

Rev

enue

s an

d Pr

ofita

bilit

y (C

PIP

)

Cus

tom

er In

vest

men

t Ind

icat

ors

(CII)

C8)


- 114 -

Further points about fuzzy customer segmentation are summarised in Table 23.


How can customers be segmented fuzzily? ( Section 5.1)

6

Fuzzy customer segmentation can be realised by one-, two- or multidimensional fuzzy classifica-

tion using fuzzy classification management tools ( RQ 2) and CPIs or KCPIs ( RQ 5).

Customer segmentation and the management and profitability of customer relationships can be

improved using portfolio analysis with important indicators ( Subsections 5.1.5 to 5.1.16):

- fuzzy classified customer orientation and customer value portfolios

- fuzzy classified customer satisfaction portfolios

- fuzzy classified customer loyalty and retention portfolios

- fuzzy classified customer repurchases, add-on selling and share of wallet portfolios

- fuzzy classified customer turnover, contribution margins and profit portfolios

- fuzzy classified customer equity and Customer Lifetime Value (CLV) portfolios.

This thesis suggests to classify and segment all customer performance indicators fuzzily in

order to avoid misclassifications and to improve the quality of customer evaluations.

Customer segmentation often is a strategic task in firms: banks and other financial institutions,

for instance, have to segment ‘creditworthy’ loan applicants from ‘not creditworthy’ ones. By

defining a four-level hierarchy of creditworthiness with different practice-relevant criteria in

Section 6.2, a fuzzy credit scoring approach was applied to the rating of a loan applicant’s

personal, material and overall creditworthiness.

In addition, a multidimensional fuzzy credit rating approach allows to calculate individual

interest rates (risk premiums) according to the customer’s values of the attributes ‘creditwor-

thiness’, ‘amount of credit’ and ‘credit period’. Table 24 summarises further points.


What are the benefits of the fuzzy classification approach in credit rating? ( Section 6.2)

7

The decomposition principle applied to a fuzzy credit scoring model enables a fair and exact

credit rating of loan applicants on different levels of a hierarchy of creditworthiness.

A three-dimensional fuzzy classification model allows to calculate individual interest rate according to the values of the attributes ‘creditworthiness’, ‘amount of credit’ and ‘credit period’.

The decision to allow a credit is sharp, but contractual terms/conditions can be adapted fuzzily.

Conclusion: fuzzy classification enables

- a fair, exact, differentiated and sensitive rating of loan applicants according to the default risk

- the reduction of misclassifications (that a loan applicant receives no credit although he is

creditworthy, or that a loan applicant receives a credit, although he is not creditworthy)

- an improvement of the risk structure of the whole credit portfolio and

- the mass customisation of products and services in banking.


- 115 -

7.2 Critical Remarks As summarised in this chapter, the application of fuzzy classification has many advantages.

However, fuzzy classification is also confronted with certain problems:

Sharp classification usually is clear, simple, straightforward and understandable for every-

one. In contrast, fuzzy classification is more complicated and not as easy to understand, to

communicate and to implement as sharp classification. If a classified customer belong si-

multaneously to different classes and strategies, this can be confusing, indeed.

How can it be explained to customer Brown that he was fuzzy classified into different

classes at same time and therefore receives a personal discount of 7.4% or has to pay an

interest rate of 8.59%? Since many people do not like being classified or labelled as some-

thing and may not understand or accept this, one strategy could be not to communicate

customer classifications. However, fuzzy classification may has to be communicated in a

subtle, indirect way, writing, for example: “Dear Mr. Brown, our company thanks you for your

trust and loyalty and offers you a personal discount of 7% for your next purchase.”

The communication of the fuzzy classification concept to employees can be difficult as well.

However, to successfully realise fuzzy customer segmentation, all relevant people have to

understand and support the idea and the implementation of fuzzy classification.

The benefit of a classification always depends on the context. It can be inefficient to clas-

sify objects, if the monetary or non-monetary, direct or indirect costs of classification are

too high in comparison to the benefit of the classification. Possible classification costs are:

- time required to collect, gather, handle, store, administrate and classify (customer) data

- staff (e.g. CIO, CCO, customer managers, data architect, employees of CRM or IT)

- ICT, technical infrastructure; hardware, information systems, CRM software, etc.

If an entrepreneur and the employees of a small enterprise personally know very well their

customers and their different value for the company, (fuzzy) classification with a data mining

tool is not necessary. However, with an increasing number of anonymous customers in me-

dium or large enterprises, such implicit customer evaluation and segmentation is often not

possible anymore. This means that the benefits of CRM and of fuzzy customer classification

or segmentation for information management is higher with a large number of customers.

It is theoretical and empirical difficult to weight attributes or criteria optimally, for instance to

define the right degree of gamma or the number of points to each class of a scoring model.

Many basic decisions in everyday and business life have to be made in a sharp manner.

For example: a relationship is continued, or it is not. A contract is signed, or it is not; an or-

der is accepted, or it is not. However, such sharp decisions do not exclude that for instance

the terms or the conditions of a contract or of an order still can be adapted in a fuzzy way.


- 116 -

Considering customer performance measurement, the following points have to be commented:

Much intangible, but important customer information cannot be handled by Customer Per-

formance Measurement (CPM) or by a system (CPMS). In the words of Albert Einstein:

“Sometimes what counts can’t be counted, and what can be counted doesn’t count.”

Consequently, the measurability of customers, or of marketing in general, is a problem and

challenge of marketing controlling. An empirical study of the market research organisation

IHA-GfK and the Swiss Marketing Association (GfM) confirms this problem (see Figure 89).

Figure 89: The Main Challenges of Marketing Controlling in Practice In addition, the basis of information and data on marketing and customers is challenge as

well, as shown in Figure 89.

However, both the measurability of marketing or CRM and the basis of information can be

improved by clearly defining and operationalising (customer) performance indicators, as

proposed in this thesis.

It is methodically difficult to determine cause-and-effect relations between the indicators in

models of performance measurement (see e.g. [Malina and Selto 2004]). Consequently,

causalities as proposed in the "big CRM success chain" have to be analysed critically.

The exact profit or equity of a customer cannot be determined, because not all customer

costs, revenues or benefits can be assigned clearly to a single customer or to a marketing

decision, process or action.

CPM(S) is only one instrument of CRM, customer and marketing controlling and should

not be considered or implemented in an isolated manner.

CPM and customer performance indicators are not an end in itself: they have to support

profitably marketing and business or corporate strategies.

Measurability of marketing Basis of information and data on marketing Know-how in the range of marketing Engagement of marketing and sales management for marketing controlling Cross-functional support of marketing controlling Lack of ressources for marketing controlling

56

37

28

23

19

18

32

44

46

44

45

37

12

19

26

33

36

45

Big challenge Indifferent No challenge 0% 100%

Source: [Reinecke and Herzog 2006, p. 87]


- 117 -

7.3 Outlook Many questions about fuzzy classification still are to be answered. Following open-ended

questions are only few ideas for further research on fuzzy classification in the field of CRM.

How could a customer Data Warehouse (DWH) with fuzzy classes be used for Customer

Performance Measurement (CPM)? How has the architecture of a DWH to be designed?

How can an open source DWH be adapted and implemented in an analytical CRM system?

What has to be considered from the information technology, and what from the business

management point of view? What are the advantages and problems of such a Customer

Performance Measurement System (CPMS) or of a DWH for information management?

How do CPMS or DWH architectures in business practice look like?

How could a hierarchical multidimensional fuzzy classification be combined with a scor-

ing model to evaluate customers in business practice? How do performance indicators have

to be aggregated and weighted (with points or the y-operator), to receive a valid valuation

method to classify fuzzily, for instance, customer attractiveness or customer equity?

How could sharp and fuzzy classification be combined in order to improve managerial de-

cisions? How can basic requirements, which have to be fulfilled sharply (e.g. a customer

has to be profitable in the long-term), be optimally defined within a fuzzy classification?

How does a fuzzy credit rating model for business customers or enterprises could look like?

Which rating criteria, figures, ratios and measures of companies have to be considered in a

hierarchical fuzzy classification of credit rating? What is the benefit of a fuzzy rating of secu-

rities, stocks and bonds in the fields of finance and investment?

Unfortunately, this master thesis could not realise empirical studies about the business ap-

plications for fuzzy classification. Future empirical research and case studies may focus on

the implementations of the fCQL toolkit in firms to demonstrate the discussed advantages of

fuzzy portfolio analysis, fuzzy ABC analysis and fuzzy customer segmentation or evaluation.

Since little theoretical and empirical research has been done on market- or customer-oriented

performance measurement, the following questions raised:

How many and which indicators are used for CPM in business practice? Which indicators

are important in companies of different sizes and in different industries? How and why can

customer performance indicators support CRM or marketing controlling in daily business?

What are Key Customer Performance Indicators (KCPIs; "indicators that matter") of suc-

cessful companies in general or in different industries, and why do they matter?

How have customer performance indicators to be defined, operationalised, implemented,

analysed and controlled specifically? Empirical research and case studies of companies

may answer these questions and provide valuable information in oder to improve CRM.

- 118 -

References and Further Reading

A

[Aksoy et al. 2007] Aksoy, L., Bejou, D., Keiningham, T. (Eds.): Customer Lifetime Value: Reshaping the Way We Manage to Maximize Profits, Best Business Books, New York, 2007.

[Albrecht 2005] Albrecht, M.: Unscharfe Klassifikation von Kunden im Telekommunikationsmarkt, EM-BA Project, International Institute of Management in Technology (iimt), 2005.

[Ambler 2000] Ambler, T.: Marketing and the Bottom Line – The New Metrics of Corporate Wealth, Prentice Hall, London, 2000.

[Ambler and Kokkinaki 1997] Ambler, T., Kokkinaki, F.: Measures of Marketing Success, In: Journal of Marketing Management, Vol. 13, 1997, pp. 665-678.

[Anderson and Sullivan 1993] Anderson, E., Sullivan, M.: The Antecedents and Consequences of Customer Satisfaction for Firms, In: Marketing Science, Vol. 12, Spring 1993, pp. 125-143.

[Anderson and Narus 1998] Anderson, J., Narus, J.A.: Business Marketing: Understand Customer Value, In: Harvard Business Review, Vol. 76, November/December, 1998, pp. 53-65.

[Anderson and Narus 1999] Anderson, J., Narus, J.A.: Business Market Management: Understanding, Creating, and Delivering Value, Upper Saddle River, New Jersey, 1999.

[Anderson et al. 1994] Anderson, E., Fornell, C., Lehmann, D.: Customer Satisfaction, Market Share and Profitability, In: Journal of Marketing, Vol. 56, July 1994, pp. 53-66.

[Anderson et al. 1997] Anderson, E., Fornell, C., Rust, R.: Customer Satisfaction Productivity and Profitability: Differences between Goods and Services, In: Marketing Sciences, Vol. 16, February 1997, pp. 129-45.

[Anderson et al. 2004] Anderson, E., Fornell, C., Mazvanheryl, S.: Customer Satisfaction and Shareholder Value, in: Journal of Marketing, Vol. 68, October 2004, pp. 172-185.

[Appel 1998] Appel, A.W.: Modern Compiler Implementation in Java, Cambridge University Press, Boston, 1998.

[Arabie et al. 1981] Arabie, P., Carrol, J.D., DeSarbo, W.S., Wind, J.: Overlapping clustering: A new method for product positioning, In: Journal of Marketing Research, Volume 18, 1981, pp. 310-317.

[Arabie et al. 1996] Arabie, P., Lawrence, H., DeSoete, G. (Eds): Clustering and Classification, World Scientific, Singapore, 1996.

[Arotaritei and Mittra 2004] Arotaritei, D., Mitra, S.: Web mining: a survey of alternative approaches, In: Fuzzy Sets and Systems, Vol. 148, January 2004, pp. 5-19.

[Atkinson et al. 2001] Atkinson, A., Banker, R., Kaplan, R., Young, M.: Management Accounting, Prentice Hall, Englewood Cliffs, 2001.

[Auer 2004] Auer, C.: Performance Measurement für das Customer Relationship Management – Controlling des IKT-basierten Kundenbeziehungsmanagement, Deutscher Universitätsverlag, Wiesbaden, 2004.

B

[Bach and Österle 2000] Bach, V., Österle, H. (Eds.): Customer Relationship Management in der Pra-xis – Erfolgreiche Wege zu kundenzentrierten Lösungen, Springer, Heidelberg, 2000.

[Bacher 1996] Bacher, J.: Clusteranalyse, Oldenburg, München, 1996.

[Backhaus et al. 2006] Backhaus, K., Erichson, B., Plinke, W., Weiber, R.: Multivariate Analyse-methoden: Eine answendungsorientierte Einführung, 11. Auflage, Springer, Berlin, 2006.

References

- 119 -

[Baetge and Heitmann 2000] Baetge, J., Heitmann, C.: Creating a fuzzy rule-based indicator for the review of credit standing, In: Schmalebach Business Review, Vol. 52, No. 4, 2000, pp. 318-343.

[Bain & Company 2005] Bain & Company, Management Tools and Trends 2005, available: www.bain.com/management_tools/Management_Tools_and_Trends_2005.pdf, accessed 7th of March

2007.

[Baldwin 1996] Baldwin, J.: Fuzzy Logic, John Wiley and Sons, Chichester, New York, 1996.

[Barwise and Farley 2004] Barwise, P., Farley, J.: Marketing Metrics: Status of Six Metrics in Five Countries, In: European Management Journal, Vol. 22, March 2004, pp. 257-262.

[Bauer et al. 2006] Bauer, H., Stokburger, G., Hammerschmidt, M.: Marketing Performance. Messen, Analysieren, Optimieren, Gabler, Wiesbaden, 2006.

[Beardan and Teel 1983] Beardan, W., Teel, J: Selected Determinants of Consumer Satisfaction and Complaint Reports, In: Journal of Marketing Research, Vol. 20, February 1983, pp. 21-28.

[Bejou and Gopalkrishnan 2006] Bejou, D., Gopalkrishnan, I. (Eds.): Capturing Customer Equity: Moving from Products to Customers, Haworth, 2006

[Belz 2004] Belz, Ch.: Gefahren der „Marketing Metrics“, In: Thexis, Vol. 21, March 2004. available: http://www.imh.unisg.ch/org/imh/web.nsf/1176ad62df2ddb13c12568f000482b94/e649e94713ff50aec1256bc200235051/$FILE/ATT72FDB, accessed 7th of March 2007.

[Belz and Bieger 2004] Belz, C., Bieger, T. (Eds.): Customer Value, Kundenvorteile schaffen Unternehmensvorteile, moderne industrie, Frankfurt, 2004.

[Belz and Bieger 2006] Belz, C., Bieger, T. (Eds.): Customer Value, Kundenvorteile schaffen Unternehmensvorteile, 2. Auflage, moderne industrie, Frankfurt, 2006.

[Belz and Bussmann 2002] Belz, C., Bussmann, W.: Performance Selling – Succesful Salesman Create Customer Value, Thexis Publishers, St. Gallen, 2002.

[Berson et al. 2000] Berson, A., Smith, S., Thearling, K.: Building Data Mining Applications for CRM, McGraw-Hill, New York, 2000.

[Berry and Linoff 2000] Berry, M., Linoff, G.: Mastering Data Mining. The Art and Science of Customer Relationship Management, John Wiley, New York, 2000.

[Biewer 1997] Biewer, B.: Fuzzy-Methoden, Praxisrelevante Rechenmodelle und Fuzzy-Programmiersprachen, Springer, Berlin, 1997.

[Blattberg et al. 2001] Blattberg, R.C., Getz, G., Thomas, J.S.: Customer Equity – Building and Managing Relationships as Valuable Assets. Harvard Business School Press, Boston, 2001.

[Blum 2006] Blum, I.: Data Mining Techniken im Marketing und Vertrieb, VDM, Saarbrücken, 2006.

[Bolton 1998] Bolton, J.: A Dynamic Model of the Duration of Customers’ Assessment of Service Quality and Value, In: Marketing Science, Vol. 17, Winter 1998, pp. 45-65.

[Boulding et al. 1993] Boulding, W., Kalra, A., Staelin, R., Zeithaml, V.: A Dynamic Process Model of Service Quality: From Expectations to Bevavioral Intentions, In: Journal of Marketing

Research, Vol. 30, February 1993, pp. 7-27.

[Bordogna and Pasi 2000], Bordogna, G., Pasi G. (Eds.): Recent Issues on Fuzzy Databases. Physica, Heidelberg, 2000.

[Borgelt 2005], Borgelt, Ch.: Prototype-based Classification and Clustering, Habilitationsschrift, Otto-von-Guericke-Universität, Magdeburg, 2005. available: http://whorf.cs.uni-magdeburg.de/~borgelt/habil/pbcc.pdf, accessed 7th of March 2007.

[Bosc and Kacprzyk 1995] Bosc, P., Kacprzyk, J. (Eds.): Fuzzyness in Database Management Systems. Physica, Berlin, 1995.

[Bosc and Pivert 1995] Bosc, P., Pivert, O.: SQLf: a Relational Database Language for Fuzzy Querying, In: IEEE Transactions on Fuzzy Systems, Vol. 3, Elsevier, 1995, pp. 1-17.

[Bosc and Pivert 2000] Bosc, P., Pivert, O.: SQLf query functionality on top of a regular relational database management system, In: [Pons et al. 2000, pp. 171-190].

[Bruhn 1995] Bruhn, M.: Internes Marketing als Baustein der Kundenorientierung. In: Die Unternehmung, Vol. 6, 1995, pp. 381-402.

References

- 120 -

[Bruhn 1998] Bruhn, M.: Wirtschaftlichkeit des Qualitätsmanagements. Qualitätscontrolling für Dienstleistungen. Springer, Heidelberg, 1998.

[Bruhn 2001] Bruhn, M.: Relationship Marketing. Das Management von Kundenbeziehungen, Vahlen, München, 2001.

[Bruhn 2002] Bruhn, M.: Integrierte Kundenorientierung. Implementierung einer kundenorientierten Unternehmensführung, Gabler, Wiesbaden, 2002.

[Bruhn 2003a] Bruhn, M.: Relationship Marketing – Management of Customer Relationships. Prentice Hall, Harlow, 2003.

[Bruhn 2003b] Bruhn, M.: Kundenorientierung. Bausteine für ein exzellentes Customer Relationship Management (CRM), 2., vollständig überarbeite Auflage, DTV-Beck, München, 2003.

[Bruhn 2004] Bruhn, M.: Marketing. Grundlagen für Studium und Praxis, 7., überarbeite Auflage, Gable, Wiesbaden, 2004.

[Bruhn 2005] Bruhn, M.: Kommunikationspolitik, Systematischer Ansatz der Kommunikation für Unternehmen, Vahlen, München, 2005.

[Bruhn 2007] Bruhn, M.: Kundenorientierung. Bausteine für ein exzellentes Customer Relationship Management (CRM), 3., vollständig überarbeite Auflage, DTV-Beck, München, 2007.

[Bruhn and Georgi 2006] Bruhn, M., Georgi, D.: Services Marketing – Managing The Service Value Chain, Prentice Hall, Harlow, 2006.

[Bruhn and Homburg 2000] Bruhn, M., Homburg, Ch. (Eds.): Handbuch Kundenbindungsmanagement, Strategien und Instrumente für ein erfolgreiches CRM, 3. Auflage, Gabler, Wiesbaden, 2000.



[Bruhn and Homburg 2006] Bruhn, M., Homburg, Ch. (Eds.): Gabler Lexikon Marketing, 2. Auflage, Gabler, Wiesbaden, 2006.

[Bruhn and Meffert 2006] Bruhn, M., Meffert, H.: Dienstleistungsmarketing. Grundlagen, Konzepte, Methoden. Mit Fallstudien, 4. Auflage, Gabler, Wiesbaden, 2006.

[Bruhn et al. 2004] Bruhn, M., Georgi, D., Schusser, S.: Einsatz des Customer Lifetime Value (CLV) im Commitmentgeschäft, In: [Hippner and Wilde 2004b, pp. 429-451].

[Büschken 2004] Büschken, J.: Higher Profits Through Customer Lock-In: A Roadmap, Thomson, Mason (OH), USA, 2004.

[Büschken and von Thaden 2000] Büschken, J., von Thaden, Ch.: Clusteranalyse, In: [Herrmann and Homburg 2000, pp. 337-380].

[Buttle 2004] Buttle, F.: Customer Relationship Management – Concept and Tools, Elsevier Butterworth-Heinemann, Burlington, 2004.

[Burnett 2001] Burnett, K.: The Handbook of Key Customer Relationship Management – The definitive guide to winning, managing and developing key account business, Prentice Hall, Harlow, 2001.

[Buser and Welte 2006] Buser, T., Welte, B.: Customer Relationship Management für die Praxis, Versus, Zürich, 2006.

C

[Casabayo et al. 2004] Casabayo, M., Agell, N., Aguado, J.: Using AI techniques in the crocery industry: Identifying the customers most likely to defect, In: International Review of Retail,

Distribution and Consumer Research, Vol. 14, March 2004, pp. 295-308.

[Chen 1992] Chen, G.: Design of Fuzzy Relational Databases Based on Fuzzy Functional Dependency. Ph.D. Dissertation Nr. 84, Unversity of Leuven, Belgium, 1992.

[Chen 1998] Chen, G.: Fuzzy Logic in Data Modeling – Semantics, Constraints, and Database Design. Kluwer Academic Publishers, London, 1998.

References

- 121 -

[Chen and Chiou 1999] Chen, L.-H., Chiou, T.-W.: A fuzzy credit-rating approach for commercial loans: a Taiwan case, In: Omega, International Journal of Management Science, Vol. 27, 1999, pp. 407-419.

[Čižek et al. 2005] Čižek, P. (Ed.), Härdle, W., Weron, R.: Statistical Tools in Finance and Insurance, Springer, Berlin, 2005.

[Claycomb and Franckwick 1997], The Dynamicso of Buyers’ Perceived Costs During the Relationship Development Process, In: Journal of Business-to-Business Marketing, Vol. 1, January 1997, pp. 1-37.

[Cokins 2004] Cokins, G.: Performance Management, Finding the missing pieces to close the intelligence Gap, Wiley, New York, 2004.

[Cordts and Lensing 1991] Cordts, J., Lensing, H.-J.: ABC-Analyse. Preisanalyse für Einkäufer, Gabler, Wiesbaden, 1991.

[Corfman and Lynch 1996] Corfman, K.P., Lynch, J.G.Jr. (Eds.): Advances in Consumer Research, Assn for Consumer Research, Provo, UT, 1996.

[Cornelsen 2000] Cornelsen, J.: Kundenwertanalyse im Beziehungsmarketing, GIM, Nürnberg, 2000.

[Cox 1995] Cox, E.: Fuzzy Logic for Business for Business and Industry, Charles River Media, Rockland, USA, 1995.

[Christensen and Demski 2002] Christensen, J., Demski, J.: Accounting Theory – an information content perspective, McGraw-Hill, Irwin, 2002.

D

[Davis 2006] Davis, J.: Measuring Marketing: 103 Key Metrics Every Marketer Needs, John Wiley, New York, 2006.

[Dikolli et al. 2006] Dikolli, S., Kinney, W., Sedatole, K.: Measuring Customer Relationship Value: The Role of Switching Cost. In: Contemporary Accounting Research, Forthcoming. available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=947567, accessed 7th of March 2007.

[Dombi 1991] Dombi, J.: Membership Functions as an Evaluation. In: Fuzzy Sets and Systems. Vol. 35, Elsevier, 1991, pp. 1-21.

[Drongelen and Cook 1997] Drongelen, C., Cook, A.: Design principles for the development of meas-urement systems for research and development processes. In: R&D Management, Vol. 27, April 1997, pp. 345-357.

[Drozdenko and Drake 2002] Drozdenko, R., Drake, P.: Optimal Database Marketing – Strategy, Development and Data Mining, SAGE Publications, London, 2002.

[Dwight 1999] Dwight, R.: Searching for real maintenance performance measures, In: Journal of Quality in Maintenance engineering, Vol. 5, March 1999, pp. 258-275.

E

[Eggert 1999] Eggert, A.: Kundenbindung aus Kundensicht, Gabler, Wiesbaden, 1999.

[Eggert 2006] Eggert, A.: Die zwei Perspektiven des Kundenwerts: Darstellung und Versuch einer Integration, In: [Günter and Helm 2006, pp. 41-59].

[Ester and Sander 2000] Ester, M., Sander, J.: Knowledge Discovery in Databases: Techniken und Anwendungen, Springer, Berlin, 2000.

[Ewert and Wagenhofer 2005] Ewert, R., Wagenhofer, A.: Interne Unternehmensrechung, Springer, Berlin, 2005.

[Everling and Schneck 2004] Everling, O., Schneck, O.: Das Rating ABC, Wiley, Weinheim, 2004.

F

[Farris et al. 2006] Farris, P., Bendle, N., Pfeifer, P.: Marketing Metrics: 50+ Metrics Every Executive Should Master, Prentice Hall, London, 2006.

[Finnerty and Shenoi 1993] Finnerty, S., Shenoi, S.: Abstraction-based Query Languages for Relational Databases. In: Wang P.P. (Ed.): Advances in Fuzzy Theory and Technology. Vol. 1,

Bookwrights Durham, 1993, pp. 195-218.

References

- 122 -

[Fornell 1992] Fornell, C.: A National Customer Satisfaction Barometer: The Swedish Experience, In: Journal of Marketing, Vol. 56, No. 1, 1992, pp. 1-21.

[Fornell et al. 2006] Fornell, C., Mithas, S., Morgeson, F., Krishnan, M.: Customer Satisfaction and Stock Prices: High Returns, Low Risk, In: Journal of Marketing, Vol. 70, No. 1, 2006, pp. 3-14. available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=891590, accessed 7th of March 2007.

[Förster and Kreuz 2002] Förster, A., Kreuz, P.: Offensives Marketing im E-Business – loyale Kunden gewinnen – CRM-Potenziale nutzen, Springer, Heidelberg, 2002.

[Foss et al. 2003] Foss, B., Stone, M., Woodcock, N.: Customer Management Scorecard, Kogan Page, London, 2003.

[Fritz 2004] Fritz, W.: Internet-Marketing und Electronic Commerce, Grundlagen – Rahmenbedingungen, Instrumente, Gabler, Wiesbaden, 2004.

[Füser 2001] Füser, K.: Intelligentes Scoring und Rating: moderne Verfahren der Kreditwürdigkeitsprüfung, Gabler, Wiesbaden, 2001. G

[Gachet 2006] Gachet, A.: La classification floue dans le domaine bancaire: Un outil de personnalisation et d’optimisation de la relation avec la clientèle, Bachelor Thesis,

University of Fribourg, 2006. available: http://diuf.unifr.ch/is/studentprojects/pdf/B-2006_Anne_Gachet.pdf, accessed 7th of March 2007.

[Galindo et al. 2005] Galindo, J., Urrutia, A., Piattini, M.: Fuzzy Databases – Modelling, Design and Implementation, IDEA Group Publishing, London 2005.

[Gaulden and Jackson 2001] Gaulden, C., Jackson, W.: Fuzzy Marketing: Implications for Small Business, In: Proc. of the Association for Small Business and Entrepreneurship Conference,

New Orleans, February 27 - March 3, 2001.

available: http://www.sbaer.uca.edu/research/asbe/2001/15.pdf, accessed 7th of March 2007.

[Gladen 2006] Gladen, W.: Performance Measurement, 3. Auflage, Gabler, Wiesbaden, 2006.

[Gleich 2001] Gleich, R.: Das System des Performance Measurement: Theoretisches Grundkonzept, Entwicklungs- und Anwendungsstand, Vahlen, München, 2001.

[Grant 2002] Grant, R: Contemporary Strategy Analysis, 4th edition, Blackwall, Massachusetts, 2002.

[Greenwich et al. 1996] Greenwich, C.T. (Editor), Ghosh, S., Fedor, D.: Advances in Management of Organizational Quality, JAI Press, 1996.

[Grönroos 1992] Grönroos, C.: Facing the Challenge of Service Competition, In: [Kunst and Lemmink 1992], pp. 129-140.

[Grönroos 1994] Grönroos, C.: From marketing mix to relationship marketing – towards a paradigm shift in marketing, In: Asia-Australia Marketing Journal, Vol. 2, No. 1, 1994, pp. 322-334.

[Grönroos 1998] Grönroos, C.: Relationship Marketing: Value, Interaction and Dialogue Process, CERS, Swedish School of Economics and Business Administration, Helsinki, 1998.

[Grönroos 2000] Grönroos, C.: Service Management and Marketing. A Customer Relationship Management Approach, John Wiley and Sons, New York, 2000.

[Grünig and Kühn 2005a] Grünig, R., Kühn, R.: Methodik der strategischen Planung – Ein prozessorientierter Ansatz für Strategieplanungsprojekte, 3., überarbeitete Auflage,

Haupt, Bern, 2005.

[Grünig and Kühn 2005b] Grünig, R., Kühn, R.: Process-based Strategic Planning, 3rd Edition, Springer, Berlin, 2005.

[Gummesson 1999] Gummesson, E.: Return on Relationship (ROR): Building the Future with Intellectual Capital, In: Proceedings of 2nd Conference on Relationship Marketing, February

2000.

[Günter and Helm 2003] Günter, B., Helm, S. (Eds.): Kundenwert, Grundlagen – Innovative Konzepte – Praktische Umsetzungen, 2. Auflage, Gabler, Wiesbaden, 2003.

[Günter and Helm 2006] Günter, B., Helm, S. (Eds.): Kundenwert, Grundlagen – Innovative Konzepte – Praktische Umsetzungen, 3. Auflage, Gabler, Wiesbaden, 2006.

References

- 123 -

H

[Han and Kamper 2006] Han, J., M.: Data Mining: Concepts and Techniques, Academic Press, London, 2006.

[Hansen et al. 1999] Hansen, N., Nohria, N., Tierney, T.: What’s Your Strategy for Managing Knowledge?, In: Harvard Business Review, March 1999, pp. 106-116.

[Harrison 2000] Harrison, T.: Financial Service Marketing, Pearson Education, Essex, 2000.

[Hartmann-Wendels et al. 2000] Hartmann-Wendels, T., Pfingsten, A., Weber, M.: Bankbetriebslehre, zweite Auflage, Springer, Heidelberg, 2000.

[Harrison 2000] Harrison, T.: Financial Services Marketing. Pearson Education, Essex, 2000.

[Hayman and Schultz 1999] Hayman, D., Schultz, D.: Measuring Returns on Marketing and Communications investments, In: Strategy & Leadership, Vol. 27, March 1999, pp. 26-33.

[Helm and Günter 2003] Helm, S., Günter, B.: Kundenwert – eine Einführung in die theoretischen und praktischen Herausforderungen der Bewertung von Kundenbeziehungen, In: [Günter and Helm 2003], pp. 3-38.

[Helmke et al. 2003] Helmke, S., Uebel, M., Dangelmaier, W. (Eds.): Effektives Customer Relationship Management. Instrumente – Einführungskonzepte – Organisation, 3., überarbeitete und

erweiterte Auflage, Gabler, Wiesbaden, 2003.

[Herrmann and Homburg 2000] Herrmann, A., Homburg, Ch.: Marktforschung, 2. Auflage, Gabler, Wiesbaden, 2000.

[Herrmann et al. 2004] Herrmann, A., Huber, F., Braunstein, C.: Kundenzufriedenheit garantiert nicht immer mehr Gewinn, In: Harvard Business Manager, Vol. 1, 2000, pp. 45-55.

[Hippner 2004] Hippner, H.: Zur Konzeption von Kundenbeziehungsstrategien, In: [Hippner and Wilde 2004b, pp. 33-65].

[Hippner and Wilde 2003a] Hippner, H., Wilde, K.: Data Mining im CRM, In: [Helmke et al. 2003, pp. 211-231].

[Hippner and Wilde 2003b] Hippner, H., Wilde, K.: Informationstechnische Grundlagen der Kundenbindung, In: [Bruhn and Homburg 2003, pp. 451-481].

[Hippner and Wilde 2004a] Hippner, H., Wilde, K. (Eds.): Grundlagen des CRM: Konzepte und Gestaltung, Gabler, Wiesbaden, 2004.

[Hippner and Wilde 2004b] Hippner, H., Wilde, K. (Eds.): Management von CRM-Projekten, Handlungsempfehlungen und Branchenkonzepte, Gabler, Wiesbaden, 2004.

[Hippner and Wilde 2006] Hippner, H., Wilde, K. (Eds.): Grundlagen des CRM: Konzepte und Gestaltung, 2., überarbeitete und erweiterte Auflage, Gabler, Wiesbaden, 2006.

[Hippner et al. 2001a] Hippner, H., Küsters, U., Meyer, M., Wilde, K. (Eds.): Handbuch Data Mining im Marketing, Knowledge Discovery in Marketing Databases, Vieweg, Wiesbaden, 2001.

[Hippner et al. 2001b] Hippner, H., Martin, S., Wilde, K.D.: Customer Relationship Management, In: WiSt, Vol. 30, August 2001, pp. 417-422.

[Hippner et al. 2004] Hippner, H., Leber, M., Wilde, K.: Controlling von CRM-Projekten, In: [Hippner and Wilde 2004b, pp. 135-181].

[Hofmann and Mertiens 2000] Hofmann, M., Mertiens, M.: Customer-Lifetime-Value-Management, Kundenwert schaffen und erhöhen: Konzepte, Strategien, Praxisbeispiele, Gabler, Wiesba-den, 2000.

[Hofmann et al. 2002] Hofmann, F., Baesens, B., Martens, J., Put, F, Vanthienen, J.: Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Rating, In: International Journal of

Intelligent Systems, Vol. 17, October 2002, pp. 1067-1083.

[Hofstrand 2006] Hofstrand, D.: Financial Terms, Iowa State University, 2006.

available: http://www.extension.iastate.edu/agdm/wholefarm/html/c3-05.html, accessed 7th of March 2007.

[Holbrook 1996] Holbrook, M.B.: Customer Value, In: [Corfman and Lynch 1996, pp. 138-142].

References

- 124 -

[Homburg 1998] Homber, Ch.: Kundennähe von Industriegüterunternehmen: Konzeption – Erfolgsauswirkungen – Determinanten, 2. Auflage, Gabler, Wiesbaden, 1998.

[Homburg 2000] Homburg, Ch.: Kundennähe von Industriegüterunternehmen. Konzeption – Erfolgsaussichten – Determinanten, 3. Auflage, Gabler, Wiesbaden, 2000.

[Homburg 2001] Homburg, Ch. (Editor): Kundenzufriedenheit: Konzepte – Methoden – Erfahrungen, 4. Auflage, Gabler, Wiesbaden, 2001.



[Homburg and Beutin 2006] Homburg, Ch., Beutin, N.: Kundenstrukturmanagement als Controllingher-ausforderung, In: [Reinecke and Tomczak 2006], pp. 225-251.

[Homburg and Bruhn 2003] Homburg, Ch., Bruhn, M.: Kundenbindungsmanagement – Eine Einführung in die theoretischen und praktischen Problemstellungen, In: [Bruhn and Homburg 2003], pp. 3-37.

[Homburg and Krohmer 2003] Homburg Ch., Krohmer, H.: Marketingmanagement, Strategie – Instrumente – Umsetzung – Unternehmensführung. Gabler, Wiesbaden, 2003.

[Homburg and Krohmer 2006] Homburg Ch., Krohmer, H.: Marketingmanagement, Strategie – Instrumente – Umsetzung – Unternehmensführung, 2. Auflage, Gabler, Wiesbaden, 2006.

[Homburg and Sieben 2003] Homburg Ch., Sieben, F.: Customer Relationship Management (CRM) – Strategische Ausrichtung statt IT-getriebenem Aktivismus,

In: [Bruhn and Homburg 2003, pp. 423-450].

[Homburg and Stock 2001] Homburg, Ch., Stock, R.: Theoretische Perspektiven zur Kundenzufriedenheit, In: [Homburg 2001, pp. 17-50].

[Höppner et al. 1999] Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis, Wiley, Chichester, New York, 1999.

[Horngren et al. 2002] Horngren, C., Foster G., Datar, S.: Cost Accounting – A Managerial Emphasis, Prentice-Hall, Englewood Cliffs, 2002.

[Hooley et al. 2004] Hooley, G., Saunders, J., Piercy, N.: Marketing Strategy and Competitive Positioning, Prentice Hall, Harlow, 2004.

[Hruschka 1986] Hruschka, H.: Market Definition and Segmentation Using Fuzzy Custering Methods, In: International Journal of Research in Marketing, Vol. 3, February 1986, pp. 117-134.

[Hsu 2000] Hsu, T.-H.: An Application of Fuzzy Clustering in Group-Positioning Analysis, In: Proc. National Science Council of the Republic of China, Vol. 10, No. 2, 2000, pp. 157-167.

I

[Imielinski and Mannila 1996] Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery, In: Communications of the ACM, Vol. 39, No. 11, 1996, pp. 58-64.

[Inuiguchi and Ramik 2000] Inuiguchi, M., Ramik, J.: Possibilitic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem, In: Fuzzy Sets and Systems, Vol. 111, 2000, pp. 3-28.

[Ittner and Larcker 1996] Ittner, C., Larcker, D.: Measuring the Impact of Quality Initiatives on Firm Financial Performance, In: [Greenwich et al. 1996, pp. 1-37].

[Ittner and Larcker 1998] Ittner, C., Larcker, D.: Are Non-Financial Measures Leading Indicators of Financial Performance? An Analysis of Customer Satisfaction, In: Journal of Accounting

Research, Vol. 36, March 1998, pp. 1-35.

[Ittner et al. 2003] Ittner, C., Larcker, D., Meyer, M.: Subjectivity and the Weighting of Performance Measures: Evidence from a Balanced Scorecard, In: The Accounting Review, Vol. 78, No. 3, pp. 725-758.

[Ishibuchi et al. 2004] Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules, Springer, Berlin, 2004.

References

- 125 -

J

[Jensen 2004] Jensen, O.: Key-Account-Management: Gestaltung – Determinanten – Erfolgsauswirkungen, 2. Auflage, Deutscher Universitäts-Verlag, Wiesbaden, 2004.

[Janikow 1998] Janikow, C.: Fuzzy Decision Trees: Issues and Methods, In: IEEE Transactions on Systems, Man, and Cybernetics, Vol. 28, Issue 1, 1998, pp. 1-14.

Available: http://www.cs.umsl.edu/~janikow/fid/fid34/papers/fid.ieeesmc.pdf, accessed 7th of March 2007.

K

[Kacprzyk and Zadrozny 1995] Kacprzyk, J., Zadrozny, S.: FQUERY for Access – Fuzzy Querying for a Windows-Based DBMS, In: [Bosc and Kacprzyk 1995, pp. 415-433].

[Kacprzyk and Zadrozny 2000] Kacprzyk, J., Zadrozny, S.: Data Mining via Fuzzy Querying over the Internet, In: [Pons et al. 2000, pp. 211-233].

[Kaplan and Norton 1996] Kaplan, R., Norton, D.: The Balanced Scorecard. Translating Strategy into Action, Harvard Business School Press, Boston, 1996.

[Kaplan and Norton 2006] Kaplan, R., Norton, D.: Alignment. How to Apply the Balanced Scorecard to Corporate Strategy, Harvard Business School Press, Boston, 2006.

[Kemper et al. 2006] Kemper, H.-G., Mehanna, W., Unger, C.: Business Intelligence – Grundlagen und praktischen Anwendungen. Eine Einführung in die IT-basierte Managementunterstützung, Vieweg, 2., ergänzte Auflage, Wiesbaden, 2006.

[Kent 2005] Kent, R.: Cases as Configurations: Using Combinatorial and Fuzzy Logic to Analyse Marketing Data, In: International Journal of Research in Marketing, Vol. 3, No. 2, 2005, pp. 205-228.

[Kilb 2002] Kilb, T.: Credit Rating in Banken: Interne Verfahren im Vergleich, Dissertation der Universität St.Gallen, 2002.

[Kleinaltenkamp and Plinke 1997] Kleinaltenkamp, M., Plinke, W. (Eds.): Geschäftsbeziehungsmanagement, Springer, Berlin, 1997.

[Kley 2003] Kley, Ch., Mittelstands-Rating, Dissertation der Universität St.Gallen, 2003.

[Köhler 2005] Köhler, R.: Kundenorientiertes Rechnungswesen, In: [Bruhn and Homburg 2005], pp. 391-422.

[Kotler and Bliemel 2001] Kotler, P., Bliemel, F.: Marketing Management, 10. Auflage, Schäffer-Poeschel, Stuttgart, 2001.

[Kotler and Bliemel 2005] Kotler, P., Bliemel, F.: Marketing-Management. Analyse, Planung und Verwirklichung, 10., überarbeitete und aktualisierte Auflage, Pearson, München, 2005.

[Kotler and Keller 2005] Kotler, P., Keller, K.: Marketing Management, 12th Edition, Prentice Hall, Harlow, 2005.

[Kotler et al. 2005] Kotler, P., Wong, V., Saunders, J., Armstrong, G.: Principles of Marketing, 40th Edition, Prentice Hall, Harlow, 2005.

[Krafft 1997] Krafft, M.: Kundenzufriedenheit und Kundenwert, Ergebnisse der gleichnamigen Studie der VDI-Gesellschaft Entwicklung Konstruktion Vertrieb und von CEO, Düsseldorf, 1997.

[Krafft 2002] Krafft, M.: Kundenbindung und Kundenwert, Physica, Heidelberg, 2002.

[Krafft 2007] Krafft, M.: Kundenbindung und Kundenwert, zweite Auflage, Physica, Heidelberg, 2007.

[Krafft and Albers 2000] Krafft, M., Albers, S:. Ansätze zur Segmentierung von Kunden – Wie geeignet sind herkömmliche Konzepte?, In: Schmalenbachs Zeitschrift für betriebswirtschaftliche For-schung, Vol. 52, 2000, pp. 515-536.

[Krafft and Rutsatz 2003] Krafft, M., Rutsatz, U.: Konzepte zur Messung des ökonomischen Kundenwertes, In: [Günter and Helm 2003, pp. 275-297].

[Krause 1992] Krause, C.: Kreditwürdigkeitsprüfung mit Neuronalen Netzen, IDW, Düsseldorf, 1992.

[Krause 2005] Krause, O.: Performance Management – Eine Stakeholder-Nutzenorientierte und Geschäftsprozess-basierte Methode, Dissertation der Technischen Universität Berlin, 2005. available: http://edocs.tu-berlin.de/diss/2004/krause_oliver.pdf, accessed 7th of March 2007.

References

- 126 -

[Kreutzer 1992] Kreutzer, R.: Zielgruppen-Management mit Kunden-Datenbanken, In: Die Betriebswirtschaft, Vol. 52, pp. 325-340.

[Krishnapuram and Keller 1993] Krishnapuram, R., Keller, J.: A Possibilitic Approach to Clustering, In: IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, 1993, pp. 98-110.

[Kumar and Reinartz 2006] Kumar, V., Reinartz, W.: Customer Relationship Management – A Databased Approach, John Wiley & Sons, Hoboken, NJ, 2006.

[Kunst and Lemmink 1992] Kunst, P., Lemmink, J. (Eds.): Quality Management in Services, Van Gorcum, Maastricht, 1992.

L

[Larose 2006] Larose, D.: Data Mining Methods and Models, IEEE Computer Society Press, Wiley, New York, 2006.

[Lebas 1995] Lebas, M.: Performance measurement and performance management. In: International Journal of Production Economics, Vol. 41, September 1995, pp. 23-35.

[Leino 2005] Leino, M.: Value Creation in Professional Service Processes, Swedish School of Economics and Business Administration, Helsinki, 2005. available: http://brunnen.shh.fi/portals/pubmanager/pdf/129-951-555-837-9.pdf, accessed 7th of March 2007.

[Levy et al. 1991] Levy, J., Mallach, E., Duchessi, P.: A fuzzy logic evaluation system for commercial loan analysis, In: International Journal of Management Science, Vol. 19, 1991, pp. 651-669.

[Link and Hildebrand 1993] Link, J., Hildebrand, V.: Database Marketing und Computer Aided Selling, Vahlen, München, 1993.

M

[Maister 1993] Maister, D.: Managing Professional Service Firm, Free Press, New York, 1993.

[Malina and Selto 2003] Malina, M., Selto, F.: Causality in a Performance Measurement Model, Working Paper of the University of Colorado, 2003. available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=488144, accessed 7th of March 2007.

[McDonald et al. 1996] McDonald, M., Millman, T., Rogers, B.: Key Account Management – Learning from Supplier and Customer Perspectives, Cranfield School of Management, 1996.

[McLachland and Basford 1988] McLachland, G., Basford, K.: Mixture Models: Inference and Application to Clustering, Marcel Dekker, New York, 1988.

[Meffert 2000] Meffert, H.: Marketing: Grundlagen marktorientierter Unternehmensführung. 9. Auflage, Gabler, Wiesbaden, 2000.

[Meier 1997] Meier, A.: Datenbankmigration – Wege aus dem Datenchaos (Database Migration). In: Praxis der Wirtschaftsinformatik, Vol. 194, 1997, pp. 24-36.

[Meier 2001a] Meier, A. (Editor): Internet & Electronic Business, Orell Fuessli, Zürich, 2001.

[Meier 2001b] Meier, A.: Vom digitalen Produkt bis hin zum Customer Relationship Management, In: [Meier 2001a, pp. 11-26].

[Meier 2003a] Meier, A.: Relationale Datenbanken, fünfte Auflage, Springer, Berlin, 2003.

[Meier 2003b] Meier, A.: A Data Warehouse Approach to Customer Relationship Management, In: Proc. of the 2nd International Conference 'Management Education for the 21st Century',

Hanoi, Vietnam, September 10-23, 2003. available: http://diuf.unifr.ch/is/seminars/files/crm-SS2006/files/DWforCRM-Meier2005.pdf, accessed 7th of March

2007.

[Meier and Werro 2006] Meier A., Werro N.: Extending a Webshop with a Fuzzy Classification Model for Online Customers. In: Proc. of the IADIS International Conference on e-Society 2006, Trinity College, Dublin Ireland, July 13-16, 2006, pp. 305-312.

[Meier et al. 2001] Meier, A., Savary, C., Schindler, G., Veryha, Y.: Database Schema with Fuzzy Classification and Classification Query Language, In: Proc. of the International Congress on Computational Intelligence – Methods and Applications, CIMA 2001, University of Wales, Bangor U.K., June 19-22, 2001.

available: http://diuf.unifr.ch/is/research/fcql/files/fCQL-Bangor.pdf, accessed 7th of March 2007.

References

- 127 -

[Meier et al. 2002] Meier, A., Bühler, L., Frauchiger, D., Gremiger S., Häuschen H., Hüsemann, S., Lüthi, A., Meyer, K., Wismer, D., Wohlwender, P.: Internet & Customer Relationship

Management – Marktstudie 2002 in Schweizer Unternehmen und Organisationen. available: http://diuf.unifr.ch/is/research/itee/KurzberichtCRM-bw.pdf, accessed 7th of March 2007.

[Meier et al. 2003] Meier, A., Mezger, Ch., Werro N., Schindler, G.: Zur unscharfen Klassifikation von Datenbanken mit fCQL, In: Proc. of the GI-Workshop LLWA - Lehren, Lernen, Wissen, Ad-aptivität. Karlsruhe, 2003.

available: http://diuf.unifr.ch/is/research/fcql/files/AKKD-03_MeierEtAl.pdf, accessed 7th of March 2007..

[Meier et al. 2005] Meier, A., Werro, N., Albrecht, M., Sarakinos, M.: Using a Fuzzy Classification Query Language for Customer Relationship Management, In: Proc. of the 31st VLDB

Conference, Trondheim, 2005. available: http://diuf.unifr.ch/is/research/fcql/files/VLDB-05_MeierEtAl.pdf, accessed 7th of March 2007.

[Meyer et al. 2001] Meyer, M., Weingärtner, M., Döring, F.: Kundenmanagement in der Network Economy – Business Intelligence mit CRM und e-CRM, Vieweg, Wiesbaden, 2001.

[Milligan 1996] Milligan, G.: Clustering Validation: Results and Implications for Applied Analysis, In: [Arabie et al. 1996], pp. 341-375.

[Mittal and Kamakura 2001] Mittal, V., Kamakura, W.: Satisfaction, Repurchase Intent, and Repur-chase Behaviour: Investigating the Moderating Effect of Customer Characteristics,

In: Journal of Marketing Research, Vol. 38, February 2001, pp. 131-142.

[Mirkin 2005] Mirkin, B.: Clustering for Data Mining: A Data Recovery Approach, Chapman & Hall, London, 2005.

[Monroe 1990] Monroe, K.: Pricing – Making Profitable Decisions, McGraw-Hill, New York, 1990.

[Morlang 2005] Morlang, C.: mCRM – Customer Relationship Management im mobilen Internet, Tectum, Marburg, 2005.

[Müller-Stewens 1998] Müller-Stewens, G.: Performance Measurement im Lichte des Stakeholderansatzes, In: [Reinecke et al. 1998], pp. 34-43.

[Müller-Stewens and Lechner 2005] Müller-Stewens, G., Lechner, Ch.: Strategisches Management, 2. Auflage, Poeschl, Stuttgart, 2005.

[Muther 1998] Muther, A.: Electronic Customer Care (ECC) - IT in der Anbieter-Kunden-Beziehung, Dissertation der Universität St.Gallen, 1998.

[Muther 2002] Muther, A.: Customer Relationship Management: Customer Care in the New Economy, 1. Auflage, Springer, Berlin, 2002.

N

[Nançoz 2004] Nançoz, C.: mEdit – Membership Function Editor for fCQL-Based Architecture, Master Thesis, Department of Informatics, University of Fribourg, 2004.

available: http://diuf.unifr.ch/is/studentprojects/pdf/M-2004_Christian_Nancoz.pdf, accessed 7th of March 2007.

[Narayandas 1998] Narayandas, D.: Measuring and Managing the Benefit of Customer Retention: An Empirical Investigation, In: Journal of Service Research, Vol. 1, January 1998, pp. 1-10.

[Neckel and Knobloch 2005] Neckel, P., Knobloch, B.: Customer Relationship Analytics – Praktische Anwendung des Data Mining im CRM, dpunkt, Heidelberg, 2005.

[Nguyen et al. 2002] Nguyen, P.T., Cliquet, G., Borges, A., Leray, F.: L’opposition entre taille du marché et degré d’homogénité des segments: une approche par la logique floue, In: Décisions Marketing, Vol. 32, 2003, pp. 55-69.

[Nordman 2004] Nordman C.: Understanding Customer Loyalty and Disloyalty – The Effect of Loyalty-Supporting and -Repressing Factors, Swedish School of Economics and Business

Administration, Helsinki, 2004. available: http://brunnen.shh.fi/portals/pubmanager/pdf/125-951-555-820-4.pdf, accessed 7th of March 2007.

[Norton and Heygate 2004] Norton, D., Heygate, R.: Measuring and Managing Customer Performance. available: www.bscol.com/pdf/Measuring%20and%20Managing%20Customer%20Performance.pdf, accessed 7th of

March 2007.

References

- 128 -

O

[Olaru and Wehenkel 2003] Olaru, C., Wehenkel, L.: A complete fuzzy decision trees technique, In: Fuzzy Sets and Systems, Vol. 138, pp. 221-254. available: http://www.montefiore.ulg.ac.be/~olaru/article-sciencedirect.pdf, accessed 7th of March 2007.

[Oliver 1980] Oliver, R.: A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions, In: Journal of Marketing Research, Vol. 17, November 1980, pp. 460-469.

[Oliver 1996] Oliver, R.: Satisfaction. A Behavioral Perspective on the Consumer, McGraw-Hill, New York, 1996.

[Oxford 1989] Oxford English Dictionary, 2nd edition, Oxford University Press, 1989, p. 689.

P

[Palloks-Kahlen 2006] Palloks-Kahlen, M.: Kennzahlengestütztes Controlling im kundenwertorientierten Vertriebsmanagement. In: [Reinecke and Tomczak 2006], pp. 283-308.

[Parasuraman et al. 1988] Parasuraman, A., Zeithaml, V., Berry, L.: SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality, In: Journal of Retailing, Vol. 64, January 1988, pp. 12-40.

[Parasuraman et al. 1994] Parasuraman, A., Zeithaml, V., Berry, L.: Reassessment of Expectations as a Comparison Standard in Measuring Service Quality: Implications for Further Research,

In: Journal of Marketing, Vol. 58, January 1994, pp. 111-124.

[Patterson and Spreng 1997] Patterson, P., Spreng, R: Modelling the Relationship Between Perceived Value, Satisfaction and Repurchase Intentions in a Business-to-Business, Services Context: and Empirical Examination, In: International Journal of Service Industry Management, Vol. 8, No. 5, 1997, pp. 414-434.

[Payne and Rapp 2003] Payne, A., Rapp, R.: Handbuch Relationship Marketing – Konzeption und erfolgreiche Umsetzung, zweite Auflage, Vahlen, München, 2003.

[Payne 2006] Payne, A.: Handbook of CRM: Achieving Excellence in Customer Management, Elsevier, Oxford, 2006.

[Peck et al. 1999], Peck, H., Payne, A., Christopher, M. and Clark, M. (Eds.): Relationship Marketing. Strategy and Implementation, Butterworth-Heinemann, Oxford, 1999.

[Peelen 2005] Peelen, A.: Customer Relationship Management, Prentice Hall, Harlow, 2005.

[Peppers and Rogers 1997] Peppers, D., Rogers, M.: Enterprise One-to-one: Tools for Building Unbreakable Customer Relationships in the Interactive Age, Piatkus Books, London, 1997.

[Peppers and Rogers 1999] Peppers, D., Rogers, M.: Enterprise One to One: Tools for Competing in the Interactive Age, Doubleday, New York, 1999.

[Peppers and Rogers 2005] Peppers, D., Rogers, M.: Return on Customer – Creating Maximum Value from your Scarcest Resource , Doubleday, New York, 2005.

[Petry 1996] Petry, F.E.: Fuzzy Databases – Principles and Applications, Kluwer Academic Publishers, London, 1996.

[Plaster and Alderman 2006] Plaster, G., Alderman, J.: Customer Value Creation: A Platform for Profitable Growth, Charter Consulting, 2006. available: www.techsol.com/casestudies/charter/Customer_Value_Creation.pdf, accessed 7th of March 2007.

[Plinke 1997] Plinke, W.: Bedeutende Kunden, In: [Kleinaltenkamp and Plinke 1997], pp.113-158.

[Pons et al. 2000] Pons O., Vila M.A., Kacprzyk, J. (Eds.): Knowledge Management in Fuzzy Databases. Physica, Heidelberg, 2000.

[Probst 2006] Probst, H.-J.: Kennzahlen leicht gemacht, Redline Wirtschaft, Heidelberg, 2006.

[Probst 2007] Probst, H.-J.: Controlling leicht gemacht, 2. Auflage, Überreuter Wirtschaft, Wien, 2007.

R

[Raab and Werner 2005] Raab, G., Werner, N.: Customer Relationship Management, Aufbau dauer-hafter und profitabler Kundenbeziehungen. 2. Auflage, Recht & Wirtschaft, Frankfurt, 2005.

References

- 129 -

[Rapp 1995] Rapp, R.: Kundenzufriedenheit durch Servicequalität: Konzeption – Messung – Umsetzung, Gabler, Wiesbaden, 1995.

[Rapp 2000] Rapp, R.: Customer Relationship Management – Das neue Konzept zur Revolutionierung der Kundenbeziehung, 2., überarbeitete Auflage, Campus, Frankfurt am Main, 2000.

[Rapp 2005] Rapp, R.: Customer Relationship Management – Das neue Konzept zur Revolutionierung der Kundenbeziehung, 3., überarbeitete Auflage, Campus, Frankfurt am Main, 2005.

[Ravald and Grönroos 1996] Ravald, A., Grönroos, C: The Value Concept and Relationship Marketing, In: European Journal of Marketing, Vol. 58, No. 2, pp. 19-30.

[Reichheld 1993] Reichheld, F.: Loyalty-based management, In: Harvard Business Review, Vol. 71, March/April 1993, pp. 64-69.

[Reichheld 1996] Reichheld, F.: Learning from Customer Defections, In: Harvard Business Review, Vol. 74, March/April 1996, pp. 59-60.

[Reichheld and Sasser 1990] Reicheld, F., Sasser, L.: Zero Defections: Quality Comes to Services, In: Harvard Business Review, Vol. 68, No. 5, 1990, pp. 105-111.

[Reichheld and Sasser 1996] Reicheld, F., Sasser, L.: The Loyalty Effect, Harvard Business School Press, Boston, 1996.

[Reichold 2006] Reichold, A.: Prozesse des Analytischen CRM. Fallbeispiele aus der Finanzdienstleistungsbranche, Architekturvorschlag und Methodenelemente, Dissertation

der Universität St. Gallen, 2006.

[Reinecke 2004] Reinecke, S.: Marketing Performance Management, Deutscher Universitäts-Verlag, Wiesbaden, 2004.

[Reinecke 2006] Reinecke, S.: Stand des Marketingcontrollings in der Praxis, In: [Reinecke and Tomczak 2006], pp. 81-95.

[Reinecke and Herzog 2006] Reinecke, S., Herzog, W.: Stand des Marketingcontrollings in der Praxis, In: [Reinecke and Tomczak 2006], pp. 81-95.

[Reinecke and Keller 2006] Reinecke, S., Keller, J.: Strategisches Kundenwertcontrolling, In: [Reinecke and Tomczak 2006], pp. 253-282.

[Reinecke and Köhler 2002] Reinecke, S., Köhler, S.: Performance Measurment des Customer Relationship Management im Internet, In: [Schögel and Schmidt 2002], pp. 203-242.

[Reinecke and Tomczak 2006] Reinecke, S., Tomczak, T. (Eds.): Handbuch Marketingcontrolling, 2. Auflage, Gabler, Wiesbaden, 2006.

[Reinecke et al. 1998] Reinecke, S., Tomczak, T., Dittrich, S. (Eds.): Marketingcontrolling, Kohlhammer, St. Gallen, 1998.

[Rolstadas 1998] Rolstadas, A.: Enterprise performance measurement. In: International Journal of Operations & Production Management, Vol. 18, September 1998, pp. 989-999.

[Romaniak and Hall 1992] Romaniak, S., Hall, L.: Decision making on credit-worthiness, using a fuzzy connectionist model, In: Journal of Fuzzy Sets and Systems, Vol. 48, 1992, pp. 15-22.

[Rudolf-Sipötz 2001] Rudolf-Sipötz, E.: Kundenwert: Konzeption – Determinanten – Management, Thexis, St. Gallen, 2001.

[Rust et al. 1995] Rust, R., Keiningham, T.: Return on Quality (ROQ) : Making Service Quality Finan-cially Accountable, In: Journal of Marketing, Vol. 59, April 1995, pp. 58-70.

[Rust et al. 2000] Rust, R., Zeithaml, V., Lemon, K.: Driving Customer Equity, Free Press, NY, 2000.

[Rust et al. 2004] Rust, R., Lemon, K., Zeithaml, V.: Return on Marketing: Using Customer Equity to Focus Marketing Strategy, In: Journal of Marketing, Vol. 68, January 2004, pp. 109-127.

available: www.rhsmith.umd.edu/marketing/pdfs_docs/Article%20Rust%20JM%200104.pdf, accessed 7th of March 2007.

[Rust et al. 2005] Rust, R., Lemon, K., Narayandas, D.: Customer Equity Management, Prentice Hall, Harlow, 2005.

References

- 130 -

S

[Sapojnikova 2004] Sapojnikova, E.: ART-based Fuzzy Classifiers: ART Fuzzy Networks for Automatic Classification, Cuvillier, Göttingen, 2004.

[Sárfi et al. 1996] Sárfi, R., Salama, M., Chikhani, A.: Applications of fuzzy sets theory in power systems planning and operation: a critical review to assist in implementation, In: Electric Power Systems Research, Vol. 32, February 1996, pp. 89-101.

[Schawel and Billing 2004] Schawel, C., Billing, F.: Top 100 Management Tools – Das wichtigste Buch eines Managers, Gabler, Wiesbaden, 2004.

[Schindler 1998a] Schindler, G.: Fuzzy Datenanalyse durch Kontextbasierte Datenbankanfragen, Deutscher Universitäts-Verlag, Wiesbaden, 1998.

[Schindler 1998b] Schindler, G.: Unscharfe Klassifikation durch kontextbasierte Datenbankabfrage, Discussion Paper at the Applied University of Solothurn, Dezember, 1998.

available: http://www.fhso.ch/pdf/publikationen/dp98-03.pdf, accessed 7th of March 2007.

[Schindler 1999] Schindler, G.: Unscharfe Klassifikation durch kontextbasierte Datenbankabfrage, In: Informatik/Informatique, Vol. 3, June 1999, pp. 18-22. available: http://www.svifsi.ch/revue/pages/issues/n993/in993.pdf, accessed 7th of March 2007.

[Schögel and Schmidt 2002] Schögel, M., Schmidt, I: E-CRM – mit Informationstechnologien Kundenpotenziale nutzen, Symposion, Düsseldorf, 2002.

[Schumacher and Meyer 2004] Schumacher, J., Meyer, M.: Customer Relationship Management strukturiert dargestellt, Springer, Berlin, 2004.

[Schwarze and Rosenhagen 1993] Schwarze, J., Rosenhagen, K.: Expertensysteme in der Kreditwürdigkeitsprüfung, In: Wirtschaftliches Studium, Nr. 6, Juni, 1993, S. 291-295.

[Setnes and Keymak 2000] Setnes, M., Kaymak, U.: Fuzzy Modeling of Client Preference in Data-rich Marketing Environments, Research Paper ERS; ERS-2000-49-LIS, Erasmus Research Institute of Management (ERIM), RSM Erasmus University, 2000.

[Shenoi et al. 1992] Shenoi, S., Melton, A., Fan L.T.: Functional Dependencies and Normal Forms in the Fuzzy Relational Database, In: Information Sciences, Vol. 50, 1992, pp. 1-28.

[Shenoi 1995] Shenoi, S.: Fuzzy Sets, Information Clouding and Database Security. In: [Bosc and Kacprzyk 1995], pp. 207-228.

[Shin and Sohn 2004] Shin, H., Sohn, S.: Segmentation of Stock Trading Customers according to Potential Value, In: Expertsystems with Applications, Vol. 27, No. 1, 2004 pp. 27-33.

[Sieben 2001] Sieben, F.: Customer Relationship Management als Schlüssel zur Kundenzufriedenheit, In: [Homburg 2001, pp. 295-314.]

[Simons 1999] Simons, R.: Performance Measurement and Control Systems, Prentice Hall, NY, 1999.

[Srivastava et al. 1998] Srivastava, R., Shervani, T., Fahey, L.: Market-Based Assets and Shareholder Value: A Frame for Analysis, In: Journal of Marketing, Vol. 62, January 1998, pp. 2-18.

[Storbacka 1994] Storbacka, K.: The Nature of Customer Relationship Profitability. Analysis of Relationships and Customer Bases in Retail Banking, Swedish School of Economics and

Business Administration, Helsinki, 1994.

[Stormer et al. 2006] Stormer, H., Werro, N., Risch, D.: Recommending Products by the mean of a Fuzzy Classification, Proceedings of the European Conference on Collaborative Electronic Commerce Technology and Research, CollECTeR 2006, Basel, Switzerland, June 2006.

available: http://diuf.unifr.ch/is/research/fcql/files/CollECTeR-06_StormerEtAl.pdf, accessed 7th of March 2007.

T

[Takahashi 1995] Takahashi, Y.: A Fuzzy Query Language for Relational Databases, In: [Bosc and Kacprzyk 1995, pp. 365-384].

[Timm 2002] Timm, H.: Fuzzy-Clusteranalyse: Methoden zur Exploration von Daten mit fehlenden Wer-ten sowie klassifizierten Daten, Dissertation, Otto-v.-Guericke-Universität Magdeburg, 2002.

available: http://fuzzy.cs.uni-magdeburg.de/~htimm/data/dissertation.pdf, accessed 7th of March 2007.

References

- 131 -

V

[Varki et al. 2000] Varki, S., Cooil, B., Rust, R.T.: Modeling Fuzzy Data in Qualitative Marketing Research, In: Journal of Marketing Research, Vol. 38, November, 2000, pp. 480-489.

[Vasant et al. 2004] Vasant, P., Nagarajan, R., Yaacob, S.: Decision making in industrial production planning using fuzzy linear programming, In: IMA Journal of Management Mathematics,

Vol. 15, January 2004, pp. 53-65.

[Voges and Pope 2006] Voges, K., Pope, N. (Eds): Business Applications and Computational Intelligence, Idea Group, London, 2006.

[Vollmuth 2006] Vollmuth, H.: Kennzahlen, 4. Auflage, Haufe, Planegg/München, 2006.

W

[Waibel and Käppeli 2006] Waibel, R., Käppeli, M.: Betriebswirtschaft für Führungskräfte. Die Erfolgslogik des unternehmerischen Denkens und Handelns, Versus, Zürich, 2006.

[Weber 1996] Weber, R.: Customer Segmentation for Banks and Insurance Groups with Fuzzy Clustering Techniques, In: [Baldwin 1996], pp. 187-196.

[Weber and Sandt 2001] Weber, J., Sandt, J.: Erfolg durch Kennzahlen – Neue empirischen Erkenntnisse, Reihe Advanced Controlling, Band 21, Wiley, Vallendar, 2001.

[Weber and Lissautzki 2004] Weber, J., Lissautzki, M.: Kundenwert-Controlling, Reihe Advanced Controlling, Band 41, Wiley, Vallendar, 2004.

[Weber and Schäffer 2006] Weber, J., Schäffer, U.: Einführung in das Controlling, 11., vollständig ü-berarbeitete Auflage, Schäffer-Poeschel, Stuttgart, 2006.

[Wedel and Steenkamp 1991] Wedel, M., Steenkamp, H.-B.: A Clusterwise Regression Method for Simultaneous Fuzzy Market Structuring and Benefit Segmentation, In: Journal of Marketing Research, Vol. 28., November 1991, pp. 385-396.

[Weiber 2002] Weiber, R.: Handbuch Electronic Business, Gabler, Wiesbaden, 2002.

[Weiber and Weber 2002] Weiber, R., Weber, M.: Customer Lifetime Value im Electronic Business, In: [Weiber 2002, pp. 609-643].

[Werro 2005] Werro, N.: Fuzzy Classification vs. Sharp Classification within the CRM Perspective, Internal working paper, Department of Informatics, University of Fribourg, 2005.

[Werro 2007] Werro, N.: Fuzzy Classification of Online Customers, Dissertation, University of Fribourg, Forthcoming.

[Werro et al. 2005a] Werro, N., Meier, A., Mezger, Ch., Schindler, G.: Concept and Implementation of a Fuzzy Classification Query Language, In: Proc. of the International Conference on Data

Mining (DMIN’05), World Congress in Applied Computing, Las Vegas, USA, June 2005.

available: http://diuf.unifr.ch/is/research/fcql/files/DMIN-05_WerroEtAl.pdf, accessed 7th of March 2007.

[Werro et al. 2005b] Werro, N., Stormer, H., Meier, A.: Personalized Discount – A Fuzzy Logic Approach, In: Proc. of the 5th IFIP International Conference on eBusiness, eCommerce and

eGovernment, I3E 2005 Conference, Poznan, Poland, October 2005. available: http://diuf.unifr.ch/is/research/fcql/files/IFIP-05_WerroEtAl.pdf, accessed 7th of March 2007.

[Werro et al. 2006] Werro, N., Stormer, H., Meier, A.: A Hierarchical Fuzzy Classification of Online Customers, In: Proc. of the 8th International Conference on e-Commerce Technology,

Shanghai, China, October 2006.

[Wettstein 2002] Wettstein, T.: Gesamtheitliches Performance Management – Vorgehensmodell und informationstechnische Ausgestaltung, Dissertation der Universität Freiburg, 2002.

available: http://ethesis.unifr.ch/theses/WettsteinT.pdf, accessed 7th of March 2007.

[Williams 2006] Williams, G.: Data Mining. Theory, Methodology, Techniques and Applications, Springer, Berlin, 2006.

[Wilson and Jantrania 1996] Wilson, D., Jantrania, S.: Understanding the Value of a Relationships, In: Asia-Australia Marketing Journal, Vol. 1, No. 1, 1996, pp. 67-101.

[Witten and Frank 2005] Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, San Francisco, 2005.

References

- 132 -

X

[Xiao 2004] Xiao, F: Success Measurement for Cross-selling Mechanism in Customer Relationship Management, Master Thesis, Technische Universität Hamburg-Harburg (TUHH), 2004.

available: http://www.sts.tu-harburg.de/pw-and-m-theses/2004/xiao04.pdf, accessed 7th of March 2007.

Z

[Zadeh 1965] Zadeh, L.: Fuzzy Sets, In: Information and Control, Vol. 8, 1965, pp. 338-353.

[Zadeh 1978] Zadeh, L.: A Meaning Representation Language for Natural Languages. In: International Journal of Man-Machine Studies, Vol. 10, 1978, pp. 395-460.

[Zeithaml 1988] Zeithaml, V.: Consumer Perception of Price, Quality and Value: A Means-End Model and Synthesis of Evidence, In: Journal of Marketing, Vol. 52, March 1988, pp. 2-22.

[Zeithaml 2000] Zeithaml, V.: Sercice Quality, Profitability, and the Economic Worth of Customers: What We Know and What We Need to Learn, In: Journal of the Academy Marketing Science, Vol. 28, Winter 2000, pp. 67-68.

[Zeithaml et al. 1990] Zeithaml, V., Parasuraman, A., Berry, L.: Delivering Quality Service, Free Press, New York, 1990.

[Zimmermann 1987] Zimmerman, H.-J.: Fuzzy Sets, Decision Making and Expert Systems, Kluwer Academic, London, 1987.

[Zimmermann 1992] Zimmermann, H.-J.: Fuzzy Set Theory – and its Applications. 2nd edition, Kluwer Academic Publishers, London, 1992.

[Zimmermann 1993] Zimmermann, H.-J. (Ed.): Fuzzy Technologien – Prinzipien, Werkzeuge, Potentiale, VDI, Düsseldorf, 1993.

[Zimmermann 1995] Zimmermann, H.-J. (Ed.): Neuro + Fuzzy, Technologien – Anwendungen. VDI, Düsseldorf, 1995.

[Zimmermann 2000] Zimmermann, H.-J. (Ed.): Practical Applications of Fuzzy Technologies, Kluwer, Academic Publishers, London, 2000.

[Zimmermann and Zysno 1980] Zimmermann, H.-J., Zysno, P.: Latent Connectives in Human Decision Making, In: Fuzzy Sets and Systems, Vol. 4, 1980, pp. 37-51.

[Zimmermann and Zysno 1982] Zimmermann, H.-J., Zysno, P.: Ein hierarchisches Bewertungssystem für die Kreditwürdigkeit im Konsumentenkreditgeschäft, In: Die Betriebswirtschaft, Vol.42, 1982, pp. 403-17.

[Zimmermann and Zysno 1983] Zimmermann, H.-J., Zysno, P.: Decision and evaluations by hierarchical aggregation of information, In: Fuzzy Sets and Systems, Vol. 10, 1983, pp. 243-266.

[Zimmermann et al. 1984] Zimmermann, H.-J., Zadeh, L., Gaines, B. (Eds.): Fuzzy Sets and Decision, North-Holland, Amsterdam, 1984.

[Zopounidis et al. 2001] Zopounidis, C., Pardalos, P., Baourakis, G. (Eds): Fuzzy Sets in Management, Economy & Marketing, World Scientific Publishing, Singapore, 2001.

[Zumstein 2005] Zumstein, D.: Customer Relationship Management Balanced Scorecard (CRM BSC), Term Paper, Seminar Online-CRM, University of Fribourg, Summer term, 2005.

available: www.dzumstein.ch/pdf/Paper_CRM_BSC_Darius_Zumstein.pdf, accessed 7th of March 2007.

[Zumstein 2006] Zumstein, D.: ICT-Management Tools, Term Paper, iimt, University of Fribourg, 2006. available: www.dzumstein.ch/pdf/Seminararbeit_ICT_Management_Tools.pdf, accessed 7th of March 2007.

[Zumstein et al. 2007] Zumstein, D., Werro, N., Meier, A.: Fuzzy Portfolio Analysis for Strategic Cus-tomer Relationship Management, Internal Working Paper, University of Fribourg, 2007.

[Zysno 1980] Zysno, P.: Kreditwürdigkeit: Empirische Überprüfung einer im Rahmen der Theorie der "Fuzzy Sets" modellierten Hierarchie gewichteter Kriterien, DFG-Forschungsbericht, 1980.

- 133 -

Appendix

Appendix 1: Excel File for the Calculation of the Membership Degrees

The absolute and normalised (relative) membership degrees of the classified objects to each class of a fuzzy classification with two attributes (Screenshot 1) or three attributes (Screenshot 2) can be calculated with the Microsoft® Excel file “GammaOperator”. The file is available under: www.dzumstein.ch/pdf/GammaOperator.xls (or on the CD-ROM enclosed on the back)

Screenshot 1 of GammaOperator for the Calculation of Smith’s Membership Degrees in Table 3 Screenshot 2 of GammaOperator for the Calculation of Smith’s Membership Degrees in Table 4

Enter value of the classified element to attribute 1 (vertical y-axis)

Enter value of the classified element to attribute 2 (horizontal x-axis)

Enter the value of gamma (γ) of the γ-operator (see page 12)

Result: absolute Membership Degrees (MD) M(Oi│Ck)

Result: normal-ised Membership Degrees (MD) Mnorm(Oi│Ck)

Enter Grade: invest-ment (in Section 3.2), personalised account, interest rate, …

Result: invest-ment, personal-ised account or interest rate, …

Enter value of the classified element to attribute 1 (vertical y-axis)

Enter value of the classified element to attribute 2 (horizontal x-axis)

Enter value of the classified element to attribute 3 (z-axis)

Results: e.g. calculation of RFM points (in Section 3.5)

Appendix

134

Appendix 2: Checklist for Performing – Fuzzy Strengths/Weaknesses Analysis

Marketing Performance Importance 1) Company reputation 2) Market share 3) Customer satisfaction 4) Customer retention 5) Product quality 6) Service quality 7) Pricing effectiveness 8) Distribution effectiveness 9) Promotion effectiveness 10) Sales force effectiveness 11) Innovation effectiveness 12) Geographical covarage Finance 13) Cost or availability of capital 14) Cash flow 15) Financial stability Manufacturing 16) Facilities 17) Economics of scale 18) Capacity 19) Able, dedicated workforce 20) Ability to produce on time 21) Technical manufactering skill Organisation 22) Visionary, capable leadership 23) Dedicated employees 24) Entrepreneurial orientation 25) Flexible or responsive

Source: adapted from [Kotler and Keller 2005, p. 55]

Major strengths

μ low performance μ high performance

0

1

Minor strengths

μ low importance μ high importance

0

1

NeutralMinor

weakness Major

weaknessVery low

Low Medium

High

Very High

Appendix

135

Appendix 3: Value Factors from the Customer Perspective Value Factor Literature Reference fC

Price, monetary sacrifices [Zeithaml 1988], [Parasuraman et al. 1988, 1994], [Monroe 1990], [Claycomb and Franckwick 1997], [Rust et al. 2000]

Cost reduction, decreased relationship costs [Wilson and Jantrania 1996]

References, image, status, prestige [Claycomb and Franckwick 1997] Relationship investments (time, ressources) [Wilson and Jantrania 1996], [Claycomb and Franckwick 1997]

Time [Parasuraman et al. 1988, 1994], [Rust et al. 2000] Effort, search effort [Parasuraman et al. 1994], [Claycomb and Franckwick 1997] Energy [Parasuraman et al. 1988, 1994] Psychological costs, physic costs [Grönroos 1998], [Claycomb and Franckwick 1997] Indirect costs [Grönroos 1992] Service provider’s reliability [Parasuraman et al. 1988, 1994] Service provider’s responsiveness, quick response time [Parasuraman et al. 1988, 1994], [Maister 1993]

Accuracy [Grönroos 1992] Flexibility [Grönroos 1992] Efficency [Grönroos 1992] Service delivery [Zeithaml et al. 1990], [Rust et al. 2000] Methodology used [Patterson and Spreng 1997] Problem identification [Patterson and Spreng 1997] Assurance [Parasuraman et al. 1988, 1994] Empathy [Parasuraman et al. 1988, 1994] Seller’s / Service provider’s competence, core competencies [Patterson and Spreng 1997], [Wilson and Jantrania 1996]

Service provider’s network (firm/personal) [Patterson and Spreng 1997], [Hansen et al. 1999] Service provider’s innovation, creativity [Maister 1993] Service provider’s knowledge strategy [Hansen et al. 1999] Service provider’s expertise, knowledge [Maister 1993] Service provider’s experience [Maister 1993] Safety [Ravald and Grönroos 1996] Credibility [Ravald and Grönroos 1996] Security [Ravald and Grönroos 1996] Trust [Ravald and Grönroos 1996], [Wilson and Jantrania 1996] Stability [Grönroos 1992] Continuity [Grönroos 1992] Goals, goal compatibility [Wilson and Jantrania 1996] Bonds (structural, social, economic) [Wilson and Jantrania 1996] Brand [Rust et al. 2000]

Source: adapted from [Leino 2004, pp. 45f]

: Fuzzy classification not adequate : Fuzzy classification with discrete or : continuous membership functions possible

Appendix

136

Appendix 4: 170+ Customer Performance Indicators

Legend measuring unit: # = Number/count, € = in euro, % = Percentage/share, Ø = Average (per customer), I = Index, R = Rating, t = time/period, ∑* = Aggregate value (at enterprise level), * = Indicator at a customer level and at enterprise level

Indicators for: customer investment; customer attitude; customer behavioural intentions; customer behaviour; customer results Indicators in bold: important indicators often discussed in literature on marketing or accounting


I # Indicator Definition or operationalisation Unit Purpose fc

1) Customer Performance Indicators for Revenue and Profitability (CPIP)

I 1 Number of prospective customers Number of interested non-buyers, pro-spective customers for the company

#,%,∑*

Indicator for market potential and the company’s potential growth

I 2 Conversion rate Number of new customers relative to prospective customers %,* Indicator for customer acquisition &

marketing/communication success

I 3 Poached customers from competitors Number of customers, who are gained of from the competitors in a period

#,%,t, ∑*

Indicator competitive position and for relative market share (growth)

I 4 Number of new customers Number of new customers in a period, nominal or relative to custom. portfolio

#,%,t, ∑*

Indicator for customer acquisition &marketing success; future revenue

I 5 Relative number of new customers Number of new customer in a period in comparison with main competitors

#,%,t, ∑*

Benchmark for competitive position& for customer acquisition success

I 6 Number of first-time buyers Number of first-time customers of a product (nominal/relative to cu. base)

#,%,t, ∑*

Over time sales should rely not on trial but on repeat buyers

I 7 Number of customers Number of customers who bought from the company in a defined period

#,%,t, ∑*

Indicator for loyalty and retention, market share, competitive position

I 8 Number of repeat buyers Number of repeat buyers of a product (nominal/relative to customer base)

#,%,t, ∑*

Indicator for customer satisfaction, loyalty/retention, stability of a sales

I 9 Number of regular buyers Number of regual buyers (patrons) of a product (nominal/relative to base)

#,%,t, ∑*

Indicator for loyalty/retention & for the stability of sales and revenues

I 10 Number of migrated customers Number of migrated or lost customers (nominal or relative to customer base)

#,%,t, ∑*

Indicator for customer dissatisfac-tion, disloyalty or switching barriers

I 11 Number of recovered customers Number or rate of recovered customers (nominal or relative to cu. base)

#,%,∑*

Indicator for customer recovery or churn management success

I 12 Number of profitable recovered cu. Number of very profitable recovered customers (nominal or relative)

#,%,€,t, ∑*

Indicator for customer recovery success and efficiency

I 13 Number of bookings or reservations Number of customer’s bookings or re-servation by the company in a period

#,%,€,t,*

Leading-indicator for customer turnover, contr. margins and profits

I 14 Number of requests Number of customer’s requests, tenders or inquiries in a period

#,%,€,t,*

Indicator for customer’s interest for a product and purchase intention

I 15 Perfect response Share or number of responses to customer requests in a defined period

#,%,€,t,*

Indicator for customer orientation and the capacity to serve customer

I 16 Number of offers Number of company’s offers, bids or quotations to a customer in a period

#,%,€,t,*

Indicator for company’s marketing and sales effort

I 17 Number of orders Number of customer’s orders or ap- pointments by the company in a period

#,%,€,t,*

Leading-indicator for customer turnover, contr. margins and profits

I 18 Perfect orders Number of correct, complete and punctual deliveries to a customers

#,%,€,t,*

Indicator for the company’s capac-ity to serve customers

I 19 Order quote (hit rate) Number of customer’s orders (I 17) relative to number of requests (I 14) %,* Indicator for customers satisfaction

with the offer & offering efficiency

I 20 Purchased products or services Purchased products/services (nom- nal/relative) of a customer in a period

#,€,Ø,%,t,*

Indicator for marketing and sales success and actual revenues

I 21 Purchased volume Customer’s purchased volume of a product or service per purchase/total

#,€,Ø,%,t

Indicator for purchase behaviour, marketing success, actual revenue

I 22 Heavy usage index Customer purchases of a product com-pared a Ø customer in the category

#,€,Ø,I,*

Indicator for relative intensity of consumption (purchase behaviour)

I 23 Average order value Average order value of a customer per purchase in a defined period

€,Ø,t,*

Indicator for customer purchase behaviour; turnover, potential, CLV

I 24 Purchase dates Dates (dd.mm.yyyy) of a customer’s purchases of company’s products Ø,t,* Indicator for customer purchase

behaviour

I 25 Purchase rhythm Rhythm or regularities of customers purchases in a defined period Ø,t,* Indicator of customer usage and

purchase behaviour

I 26 Purchase intensity Customer’s intensity, e.g. number, vol-ume or value of purchases in a period

I,R,t,*

Indicator for customer usage and purchase behaviour

I 27 Recency The length of time since a customer’s last purchase (RFM method) t,Ø,* Indicator for purchase behaviour &

for number of active customers

I 28 Frequency Frequency of a customers purchases in defined period (RFM method) t,Ø,* Indicator for customer behaviour

for customer loyalty or retention

I 29 Monetary value Monetary value of a customer’s total purchases in a period (RFM method)

€,Ø, R,*

Indicator for customer turnover, profitability and CLV

I 30 Cash flow Operiational (free) cash flow received from a customer in a defined period €,t,* Indicator for customer payment

behaviour

I 31 Customer turnover (or sales) Customer’s total turnover (here synon-ymously: sales/revenues) in a period

€,%, Ø,t,*

Indicator for company’s ability to fulfil customer needs, create value

Appendix

137

I 32 Turnover of first-time buyers Amount of turnover of a first-time buyer (of a new customer) in a period

€,Ø,%,t,*

Indicator for customer potential and attractiveness

I 33 Turnover of repeat/regular buyers Amount of turnover of a regular customers in a defined period

€,Ø,%,t,*

Indicator for customer potential, attractiveness and profitability

I 34 Cumulative turnover (or sales) Total, cumulative turnover (or sales) of a customer since his first purchase

€,Ø,%,t,*

Indicator for loyalty and customer lifetime value or customer equity

I 35 Growth of turnover (or sales) Customers sales growth (nominal or relative to total sales growth)

%,Ø,*

Indicator for customer potential and attractiveness

I 36 Sales premiums Sales premiums or promotional ince-tives given to cu. for buying a product

#,€,Ø,%,t,*

Customer investment indicator to influence purchase behaviour

I 37 Customer or new customer discounts Discounts, reductions or gifts given to (new) customers dependent on sales

#,€,Ø,%,t,*

Customer investment indicator to acquire new customers

I 38 Customer penetration Share of demand of all products a customer buys from the company %,* Indicator for commitment (I 131) &

customer (add-on selling) potential

I 39 Share of wallet Share of customer’s fulfilment by the company rel. to demand of a product

#,€,Ø,%,*

Indicator for commitment (I 131) and (repurchase) potential

I 40 Share of wallet of new customers Share of a new customer’s fulfilment of demand relative to his total demand

#,€,Ø,%,*

Indicator for usage behaviour, customer turnover and potential

I 41 Relative share of wallet Customer’s share of wallet relative to company’s main competitor(s)

%,€,*

Benchmark for the company’s competitive position

I 42 Market share of customer Share of customer’s volume at the total (revenue/unit) market share

#,€,%,*

Indicator for the dependency of the company on a customer

I 43 Revenue/unit market share Sales revenue or unit sales as a percentage of market sales revenue

#,€,%,∑*

Indicator for market strength and competitive position

I 44 Relative market share Company’s market share divided by largest competitor’s market share

#,€,%,∑*

Indicator for market strength and competitive position

I 45 Growth of market share Growth of revenue/unit (I 43) or relative market share (I 44) in a period

#,€,Ø,%,t, ∑*

Indicator for marketing success and industry attractiveness

I 46 Market growth Nominal or relative growth of the total market demand of a product/category

#,€,Ø,%,t, ∑*

Indicator for industry attractivenessand market potential

I 47 Market demand Describes the total demand of all consumers for a product in a period

#,€,Ø,%,t, ∑*

Indicator for customer need and wishes (industry attractiveness)

I 48 Market penetration Purchasers of a product category as a percentage of total population %,∑* Indicator for (product/service) ca-

tegory acceptance by consumers

I 49 Brand/product penetration Market share of a product in compar- son to total market share of a product %,∑* Indicator for the brand/product

acceptance by consumers

I 50 Down-selling (rate) Number of additional but less ex-pensive products sold to a customer

#,€,Ø,%,t,*

Indicator for customer satisfaction, loyalty or retention, and turnover

I 51 Cross-selling (rate) Number of customer’s purchases of different products from the company

#,€,Ø,%,t,*


I 52 Cross-selling potential Probability of selling additional, other products to an existing customer

%,€, R,t,*

Indicator for customer’s potential, future turnover and profits

I 53

A

dd-o

n-se

lling

Up-selling (rate) Number of additional, upgrade, more expensive products sold to a customer

#,€,Ø,%,t,*


I 54 Cross-buying intention Expressed intention of customer to purchase different products

#,€,Ø,%,t,*

Leading-indicator for customer’s future turnover and profits

I 55 Repurchase intentions Expressed intention of a prospect or customer to (re)purchase a product %,* Leading-indicator for customer

satisfaction, loyalty and retention

I 56 Repurchases (or repurchase rate) Number of customer’s repurchases (or repurchase rate) of a product

€,%, Ø,t,*

Indicator for customer value, satis-faction, loyalty and/or retention

I 57 Probability of repurchases Probability of intention of existing customers to repurchase a product %,* Indicator for customer repurchases

(satisfaction, loyalty or retention)

I 58 Customers product mix at company Customer purchased product mix by the company or at the competitor #,€,* Indicator for customer needs,

wishes and purchase behaviour

I 59 Average product portfolio of customer Customer’s average number/volume of purchases of products in a period

#,€,Ø,t,*

Indicator for customer needs, wishes and purchase behaviour

I 60 Price sensitivity Customers sensitivity to notice and react to changes in prices

Ø,I, €,R,*

Indicator for probability to switch or repurchase and for loyalty

I 61 Price premium The additional charge a satisfied cust-omer pays for a product or service

€, %,*

Indicator for product quality, customer value, satisfaction & loyalty

I 62 Method of payment Method or medium, how a customer pays, e.g. cash, credit card, cash card -,* Indicator for purchase or payment

behaviour and for creditworthiness

I 63 Punctuality of payment Number of days, weeks or months of a customer’s delay in payment

#,Ø, t,*

Indicator for customer creditwor-thiness and solvency

I 64 Number or sum of outstanding bills A customer’s total number and sum of outstanding bills or accounts

#,€, Ø,t,*


I 65 Amount or share of bad debt losses Amount or share of customer’s or new customer's with bad dept losses

#,€,% Ø,t,*


I 66 Payment history Customer’s payment history or punctuality of payment in a period

Ø,t,I,R,*


I 67 Payment history of new customers A new customer’s payment history or punctuality of payment in a period

Ø,R,t,*

Indicator for customer creditwor-thiness, solvency

I 68 Creditworthiness Customer’s creditworthiness or credit rating; customer solvency

Ø,I,R,*

Indicator for the ability, intention and financial capability to pay bills

Appendix

138

I 69 Gross margin (contribution margin) Customer’s total turnover (or sales) minus total variable costs in period

€,% Ø,t,*

Indicator for customer turnover, fix costs and profit

I 70 Customer contribution margin I Customer’s turnover (net sales) minus costs of goods/services sold in period

€,% Ø,t,*

Indicator for customer turnover and profit

I 71 Customer contribution margin II Contribution margin I (I 70) minus marketing costs (I 101)

€,% Ø,t,*

Indicator for customer turnover, marketing costs and profit

I 72 Customer contribution margin III Contribution margin II (I 71) minus customer-driven sales costs (I 95)

€,% Ø,t,*

Indicator for customer turnover, marketing costs and profit

I 73 Customer contribution margin IV Margin III (I 72) minus customer driventransport & service costs (I 96, I 97)

€,% Ø,t,*

Indicator for customer turnover, transport/sales costs and profit

I 74 Contribution margin of new customer Contribution margin (I-IV) of a new customer in a defined period

€,% Ø,t,*

Indicator for customer turnover, customer lifetime value, potential

I 75 Customer gross profit Customer’s total turnover (I 31) minus total costs (I 102) in a period

€,Ø,t,*

Indicator for customer and com-pany performance and success

I 76 Customer net profit Gross profit (I 75) minus taxes, int-erests, depreciation & other expenses

€,Ø,t,*


I 77 Growth of customer profit Realised (or expected) positive or negative growth of gross or net profit

€,Ø,t,*

Leading indicator for potential and company performance or success

I 78 Customer profitability Difference between total turnover and costs associates with a CR in a period

€,%,Øt,R,*


I 79 Customer equity The total of the discounted lifetime values of all customers of a company

€,%,Øt,R,*


I 80 Customer Lifetime Value (CLV) The present, discounted value of all cash flows over the length of the CR

€,%,Øt,R,*


I 81 Prospect Lifetime Value (PLV) The present value of all cash flows over the length of the prospect’s CR

€,%,Øt,R,*

Indicator for prospect potential and future customer lifetime value

I 82 Customer attractiveness The degree of overall attractiveness of a customer for a company I,R,* Indicator for customer & company

performance (for segmentation)

I 83 Customer potential The customer’s future potential, e.g. for sales, contribution or profit

€,I,R,t,*

Indicator for future customer and company performance or success

I 84 Potential of new customers A new customer’s future potential, e.g. volume, sales, contribution or profit

€,I,Ø,R,t,*


I 85 Development of customer’s potential The change of customer’s potential in a defined past or future period

€,I,R,t,*


I 86 Development of customer’s industry The change of customer’s potential in a defined past or future period

€,I,R,t,*


2) Customer Investment Indicators (CII)

I 87 Acquisition costs All costs incurred to acquire a new customer (e.g. advertising, marketing)

€,%, Ø,t,*

Indicator for the company’s effort to acquire or gain new customers

I 88 Acquisition efficiency Ratio of customer acquisition costs (I 87) to turnover of first buyer (I 32)

I,R, ∑*

Indicator for the ability to acquire efficiently new customers.

I 89 Retention costs All costs incurred to bind a customer (e.g. retention programs, offers)

€,%, Ø,t,*

Indicator for the company’s effort to bind and obtain customers

I 90 Retention efficiency Ratio of customer acquisition costs (I 89) to sales of customers (I 34) I,R,* Indicator for the ability to bind

efficiently current customers.

I 91 Recovery costs All costs incurred to recover a cus-tomer (e.g. recovery actions, offers)

€,%, Ø,*

Indicator for the company’s effort to recover or reactivate old customers

I 92 Recovery efficiency Ratio of customer recovery costs (I 91) to sales of recovered customers I,R,* Indicator for the ability to recover

efficiently migrated customers.

I 93 Administration costs Net costs of administration (wages of administrative staff, materials, etc.)

€,%, Ø,t,*

Indicator for customer orientation and for customer efficiency (I 108)

I 94 Transaction costs All costs incurred in making an exchange of products or services

€,%, Ø,*

Indicator for selling effort/efficiency & customer contribution margin III

I 95 Sales costs All costs incurred to sale a product or service to a customer

€,%,t,*

Indicator for selling effort/efficiency & customer contribution margin III

I 96 Logistic costs All costs associated with logistics (e.g.order, inventory, handling & delivery)

€,%, Ø,t,*

Indicator of supply chain mmgt., & for customer contribution margin IV

I 97 Service costs All costs associated with customer service (e.g. call center, consulting)

€,%, Ø,t,*

Indicator for customer orientation & customer contribution margin IV

I 98 After sales costs All costs incurred after the sale (e.g. warranty, helpdesk, complaints, etc.)

€,%, Ø,t,*

Indicator for customer orientation & customer contribution margin IV

I 99 Communication costs All costs associated with customer communication (e.g. ad, mail, phone)

€,%, Ø,t,*

Indicator for the company’s market-ing effort and efficiency

I 100 Contact costs All costs incurred to contact a custom-er (e.g. phone calls, travel expenses)

€,%, Ø,t,*

Indicator for customer orientation and for customer efficiency (I 108)

I 101 Marketing costs Total expenditures on marketing activities in a defined period

€,%, Ø,t

Indicator for the marketing effort & customer contribution margin II

I 102 Total customer’s costs All customer-related costs in a defined period

€,%, Ø,t,*

Indicator for customer profitability and customer efficiency

I 103 Return on Sales (ROS) ROS is calculated by dividing net profits by sales (I 31: I 76)

%,R, ∑*

Indicator for the company’s ability to generate profits from sales

I 104 Return on Customer (ROC; ROCI) ROC is calculated by dividing cu. net profits by total costs (I 76: I 102)

%,R,*

Indicator for the company’s ability to generate profits from customers

Appendix

139

I 105 Return on Relationship (ROR) ROR is calculated by dividing cu. net profits by total costs (I 76: I 102)

%,R,*

Indicator for company’s ability to generate profits from relationships

I 106 Return on Customer Satisfaction Net profits of customer satisfaction divided by cu. satisfaction mmgt costs

%,R,*

Indicator for the company’s ability/ effectiveness to satisfy customers

I 107 Return on Customer Retention Net profits of customer retention divided by cu. retention costs ()

%,R,*

Indicator for the company’s ability & effectiveness to bind customers

I 108 Customer efficiency Customer contribution margin relative to capacity shortage/costs

%,R,*

The company’s ability to optimally serve customers at low costs

I 109 Return on Marketing (ROM; ROMI) Total revenues of marketing divided by total marketing costs

%,R,*

Indicator for marketing efficiency and marketing success

I 110 Return on Investment (ROI) Total revenues divided by total costs or investment

%,R, ∑*

Indicator for efficiency and success of investments

3) Customer Relationship Indicators (CRI)

I 111 Awareness (brand awareness) Share of target customers which is aware of a brand (recall/recognition)

%,€,ØI,R,*

Indicator for advertising effects and communication success

I 112 Ad awareness Percentage of population/prospectives that is aware of a brand’s advertising

%,Ø,I,R,*

Indicator for advertising effects and communication success

I 113 Brand/product knowledge Percentage of customers who demo- strate product knowledge or beliefs R,* Indicator of the familiarity with a

product beyond name recognition

I 114 Top of mind First brand comes to customers mind asking about a product of a category R,* Indicator for the salience of a

product or service

I 115 Corporate image Indicates, how a company is perceived by a (prospective) customer R,* Indicator for customer satisfaction,

repurchases and recommendation

I 116 Product or service image Indicates, how a company’s product is perceived by a (prospective) customer R,* Indicator for customer satisfaction,

repurchases and recommendation

I 117 Customer orientation The a company’s focus on meeting the needs and wants of it customers R,* Indicator for a company’s ability to

identify market opportunities

I 118 Perceived product quality A customer’s rating of the perceived quality of a given product. R,* Indicator for customer expectations

or satisfaction and product quality

I 119 Relative perceived product quality A customer’s rating of a product com-pared to other products in the market R,* Benchmark for customer expecta-

tions or satisfaction, product quality

I 120 Perceived service quality Customers perceived quality of a provided service R,* Indicator for customer expectations

or satisfaction and service quality

I 121 Perceived cheapness/expensiveness Customers perceived cheapness or expensiveness of a offer R,* Indicator for the company’s price

competitiveness (and satisfaction)

I 122 Perceived price-performance ratio Customer perceived price-perform-ance ratio or value for money.

%,I, R,*

Indicator for the company’s price competitiveness (and satisfaction)

I 123 Perceived value or utility The (subjective) customer rating about the perceived value of product R,* Indicator for created utility & (basic

/added) value of a marketing offer

I 124 Customer value The total value that a customer receives from a offer R,* Indicator for the created utility and

value of a marketing offer

I 125 Fulfilment of customers’ expectations Degree of fulfilment of customers’ expectations/requirements to a product

Ø,I,R,*

Indicator for customer needs and expectations, satisfaction & loyalty

I 126 Customer satisfaction The overall customer satisfaction with the offer of a company

Ø,I,R,*

Indicator for customer value and likelihood of repurchases (loyalty)

I 127 Relative customer satisfaction Customer satisfaction compared with competitors/national satisfaction index

Ø,I,R,*

Benchmark of the company’s ability to satisfy customers

I 128 Customer satisfaction with product The degree of customer satisfaction with a certain product or product

Ø,I,R,*

Indicator for product quality and likelihood of repurchases (loyalty)

I 129 Customer satisfaction with service The degree of customer satisfaction with a service

Ø,I,R,*

Indicator for service quality and likelihood of repurchases (loyalty)

I 130 Customer involvement Activation level in purchase situations or emotional proximity to a product R,* Indicator for relationship quality &

likelihood of repurchases (loyalty)

I 131 Customer commitment A customer’s internal intention to continue a valuable relationship R,* Indicator for likelihood of repurch-

ases (loyalty) & recommendations

I 132 Customer attachment Feelings of affection of a customer for the company and its products/services R,* Indicator for likelihood of repurch-

ases (loyalty) & recommendations

I 133 Customer trust Degree of customers trust or confidence in the company and products R,* Indicator for relationship quality &

likelihood of repurchases (loyalty)

I 134 Customer loyalty Committed customer who repurchase by the company and recommend it

Ø,I,R,*

Indicator for satisfaction, attach-ment , retention & future revenues

I 135 Customer perceived dependence Customer’s perceived dependency on the company or on its products I,R,* Indicator for the company’s switch-

ing barriers and customer retention

I 136 Dependency on a customer Company’s financial, informational or cooperative dependency on customer I,R Indicator for number of customers

and market share of customer

I 137 Customer retention (retention rate) The number of customers retained (relative to the customer base)

%,Ø,I,R,*

Indicator for satisfaction, loyalty or ability of the firm to bind customers

I 138 Adjusted customer retention rate Retention rate in consideration of non-influence able migration (e.g. deaths) I,R,* Indicator for satisfaction, loyalty or

ability of the firm to bind customers

I 139 Weighted customer retention rate Retention rate (I 137) weighted e.g. by customers turnover or profit I,R,* Indicator for the ability of the firm

to bind profitable customers

I 140 Willingness to switch Customers basic willingness to switch from the company to a competitor I,R,* Leading-indicator for customer

loyalty, retention and lifetime value

Appendix

140

I 141 Intention to switch Customers intention to switch over from the company to a competitor I,R,* Leading-indicator for customer


I 142 Switching probability Customer’s future probability of switching to a competitor %,* Leading-indicator for customer


I 143 Customer half-life Period, after which the half of all acquired customers have left again

I,R, ∑*

Indicator for number of migrated customer and loyalty or retention

I 144 Churn rate (migration rate) Number of migrated customers relative to customer base

%,I,R, ∑*

Indicator for customer loyalty and retention (ability to bind customers)

I 145 Switching costs (switching barriers) Height of the company’s different switching barriers (or switching costs) I,R,* Indicator for customer retention &

customer lifetime value or equity

I 146 Technical/contractual switching costs Height of technical/contractual switch-ing barriers (others barriers see p. 74) I,R,* Indicator for customer retention &

customer lifetime value or equity

I 147 Duration of customer relationship Length of time (weeks, months) of a company’s relationship with customer

Ø,t, R,*

Indicator for customer satisfaction,loyalty, retention and lifetime value

I 148 Intensity of customer relationship Intensity or strength of the company’s relationship with a customer R,* Indicator for relationship quality,

and customer attachment & loyalty

I 149 Perceived relationship quality Customer’s perceived (subjective) relationship quality with the company I,R,* Indicator for service/product quality,

satisfaction, loyalty and retention

I 150 Position in customer lifetime cycle Cust. lifetime cycle: entrance, growth, maturation, saturation and dissolution R,* Indicator for cross-/up-selling

potential & customer lifetime value

4) Customer Recommendation Indicators (CReI)

I 151 Intention to recommend Customers’ intention or plan to recom-mend products to friends, relatives,... R,* Indicator for customer satisfaction,

commitment and loyalty

I 152 Number of recommendations Number of successful recommend- tions of a customer in a given period

#, R,*

Indicator for customer satisfaction, loyalty and acquisition efficiency

I 153 Recommendation intensity Intensity of customer’s recommen-dation to friends, relatives,… R,* Indicator for customer satisfaction,

commitment and loyalty

I 154 Recommendation frequency Frequency and rhythm of a customer recommendation to friends, relatives…

#, R,*

Indicator for customer satisfaction, commitment and loyalty

I 155 Potential reference recipients Number and importance of potential of a customer

#, R,*

Indicator for customers social position and role as opinion leader

I 156 Role as opinion leader Customer’s social, medial or public position; influence to other consumers R,* Indicator for product or corporate

image, brand/company awareness

5) Customer Information Indicators (CInfI)

I 157 General intention to dialog Customers general intention/ willingness to have a dialog or conversation. R,* Indicator for involvement, relation-

ship quality & response behaviour

I 158 Consulting or helpdesk intensity Consulting intensity (in hours or €) of a consultant or employee in a period

#,€,R,t,*

Indicator for customer orientation, relationship intensity and quality

I 159 Customer initiated contacts Number of customer initiated contacts to the company in a defined period

#,€,R,t,*

Indicator for different customer behaviour (sales, satisfaction, etc.)

I 160 Company initiated customer contacts Number of customer initiated contacts to the company in a defined period

#,€,R,t,*

Indicator for customer orientation and intensity of relationship

I 161 Contact intensity with new customers Number of companies contacts with a new customers in a defined period

#,€,R,t,*


I 162 Contact intensity with customers Number of contacts with a customers (regular buyer) in a defined period

#,€,R,t,*


I 163 Number/quality of suggestions Number, quality and importance of a customer’s suggestions in a period

#,R,t,*

Indicator for customer satisfaction and product expertise

I 164 Number/quality of complaints Number and quality of customer’s complaints in a defined period

#,Ø,R,t,*

Indicator for product/service qualityand customer satisfaction/loyalty

I 165 Complaint satisfaction Degree of customer’s satisfaction with company’s reaction to the complaint

R, Ø,*

Indicator for service quality and customer satisfaction and loyalty

I 166 Demands of technical services Customer’s demands (e.g. numbers, duration, costs) of technical services

#,€,Ø,R,t,*

Indicator for usage behaviour, satisfaction and product expertise

I 167 Number of returns (return rate) Customer’s number/value of returned or rejected products or services

#,€,R,t,* ∑*

Indicator for product/service qualityand cust. expectations/satisfaction

I 168 Product expertise Customers know-how or expertise concerning the company or products R,* Indicator for usage behaviour,

experience and involvement

I 169 Response rate or behaviour Percentage of customers respond to a communication or marketing action

%,I,R,*

Indicator for customer purchase be-haviour, value, purchase intention

I 170 Response probability Customer’s response probability to a future communication/marketing action

%,R,*

Indicator for response rate and communication success

6) Customer Cooperation Indicators (CCI)

I 171 Intention to cooperate Customer’s intention for a cooper- tion with the company (e.g. for R&D) R,t,* Leading-indicator for attachment,

relationship quality and intensity

I 172 Expertise for cooperation Customer’s know-how, knowledge, ex-pertise or information for cooperation R,* Indicator for purchase behaviour,


I 173 Potential cooperation topics Possible cooperation topics, fields, technologies, products/services, etc. R,* Indicator for purchase behaviour,


Source: partly adapted from [Reinecke 2004, pp.267ff], [Neckel and Knobloch 2005, pp.199], [Bauer et al. 2006, pp.106ff], [Farris et al. 2006, pp.12ff, 129f], [Davis 2006, pp.4ff]

Appendix

141

Appendix 5: Criteria for Fuzzy Market Segmentation Variables Typical breakdowns fC

Geographic units Nations, states, cantons, regions, cities, communes, neighbourhoods, streets Climate Northern, southern

Regions South-north, west-east (e.g. German speaking part of Switzerland, Romandy, Ticino, Grisons); mountains, midland

City or metro size <5000, 15’000-20’000, 20’001-50’000, 50’000-100’000, 100’001-200’000, 200’001-500’000, 500’000-1Mio., >1Mio, ...

Population density Rural, suburban, urban; Resident persons per square kilometre: <10,0; 10,0-24,9; 25-49.9; 50-99,9; 100-249,9; 245-499,9; 500-999,9; ≥1000) G

eogr

aphi

c

Employment Low, …, high (<250, 250-449, 450-549, 550-749; ≥750 employed persons/1000 habitants)Age Under 2, 2-6, 6-11, 11-15, 15-20, 20-26, … , 65+ Gender Male, female [no women/men, …, uni sexe, only women/men] Race Black, White, Asian, Hispanic, Arabic, … Nationality British, French, German, Swiss, Italian, Spanish, Turkish, Polish, Swedish, … Religion Catholic, Protestant, Jewish, Muslim, Hindu, Buddhist, other, none Generation Baby boomers; Generation Xers (68ers, 70ers, 80ers, 90ers), … Occupational group Worker, sales, clerical, officials & proprietors, professional & technical, managers,... Occupation In education (school, university), full-time/part-time work, housewife, unemployed,…

Family life cycle [Young, single], [young, married, no children], [young, married, youngest child under 6], [older, married, children], [older, married, adult children], [older, single], …

Family size 1, 2, 3, 4, 5 and more Education Grade school or less, high school, … , college graduate (low vs. high educated) Social class Lower lowers, upper lowers, working, middle, upper middle, lower uppers, upper uppers

Dem

ogra

phic

Income Household income, number of persons in the household with own income, household income per capita, personal net income, disposable income, ...

Psychographic lifestyle Culture-oriented, sports-oriented, outdoor-oriented, etc.

Personality Compulsive, gregarious, authoritarian, ambitious, etc. Sinus-Milieus® Social status

Lower, middle, higher social status

Basic values Traditional (duty, order), modern (Individualisation, self-realisation, hedonism, pleasure) and pro-active change (patchwork, virtual society, multi-options);

10 Milieus Frugal traditional, materialistic workers, traditional commoner, status oriented, well estab-lished, post-materialist, new middle class, escapists, experimentalists, modern performer

Euro-Socio-Styles® Reality vs. mirage

Being vs. possessions

Permanence/change Peace and personal security vs. living ones’ own emotions

Psyc

hogr

aphi

c

8 Styles Secure world, steady world, standing world, authentic world, new world, cosy tech world, crafty world, magic world

Behavioural occasions Regular occasion, special occasion Benefit Quality, service, economy or speed Buying behaviour, Price sensibility Very low, …, very high price sensibility or consciousness Service sensibility Very low, …, very high demand for service Brand awareness Very low, …, very high brand awareness or brand identity Demand for quality Very low, …, very high demand for quality Involvement Very low, …, very high involvement

Types of buying decisions Processes, e.g. Extensive High price, rarely purchased, careful evaluation and choice Limited Medium price, moderate information demand Routine Low price, frequently purchased, few alternatives Impulsive Response to an intensive stimuli, no evaluation of alternatives

User status Non user, ex-user, potential user, first time user, regular user Usage rate Very light user, light user, medium user, heavy user, very heavy user Readiness stage Unaware, aware, informed, interested, desirous, intending to buy Attitude Hostile, very negative, negative, indifferent, positive, very positive, enthusiastic Adoption time Innovators, early adopters, early majority, late majority, laggards

Beh

avio

ural

Loyalty status None, very low, medium, strong, very strong, absolute

Source: following [Kotler et al. 2005, p. 399]


Appendix

142

Appendix 6: Attributes, Classes, Terms, Domains and Contexts of Credit Rating

Attribute (Aijkl) & hierarchy level Li 1 2 3 4

Classes Membership functions (μ) Terms T(Aijkl)

Scores S(Aijkl) [scores],(%)

Context K(Aijkl)

C1) creditworthy μpers. creditworthy + μmat. creditworthy

C2) personally not creditworthy

μpers. not creditworthy + μmat. creditworthy

not creditworthy cont.: [0,99]; (0,49.9)

discrete: {very bad, bad, insufficient}

C3) materially not creditworthy

μpers. creditworthy + μmat. not creditworthy

C4) not creditworthy μpers. not creditworthy + μmat. nicht kw

creditworthy

continuousc: [0,200]; (0,100) discrete d: {very bad, bad, insufficient, sufficient, good, excellent}

cont.: [0,99]; (50100)

discrete: {sufficient, good, excellent}

C1-1) personally creditworthy

μfamily c. creditworthy + μjob c. creditworthy

C1-2) job circumstances not creditworthy

μfamily c. creditworthy + μjob c. not creditworthy

personally not creditworthy [0, 49]; (0, 24.9)

C1-3) family circumstances not creditworthy

μfamily c. not creditworthy + μjob c. creditworthy

C1-4) personally not creditworthy

μfamily c. not creditworthy + μjob c. not creditworthy

personally creditworthy

[0, 100]; (0,50)s/d

[50, 100]; (25, 50)

Family situation not creditworthy [0, 24]; (0, 12.49)

Family situation creditworthy

[0, 50]; (0, 25)s/d [25, 50]; (12.5, 25)

Age (A4111) 18, ..., 100 [0, 10]; (0, 5)d

Civil status (A4112) single, married, divorced, separated, widowed [0, 10]; (0, 5)s

Family status (A4113) single, single mother, concubinage, married [0, 10]; (0, 5)d

Number of children (A4114) 0, 1, 2, 4, >4 [0, 10]; (0, 5)s

Fam

ily s

ituat

ion

(A31

1)

Age of children (A4115) 0, ... , 30 years [0, 10]; (0, 5)d

Employment not creditworthy [0, 24]; (0, 12.49)

Employment creditworthy

[0, 50]; (0, 25)s/d [25, 50]; (12.5, 25)

Profession (A4121) - [0, 10]; (0, 5)d Qualification (A4122) [low, … , high qualified] [0, 10]; (0, 5)d Employer (A4123) - [0, 10]; (0, 5)d

Duration of employment(A4124) [labour contract, <1, >1, >3 years] [0, 10]; (0, 5)s/d

Pers

onal

cre

ditw

orth

ines

s (A

21)

Em

ploy

men

t (A

312)

other (A4125) - [0, 10]; (0, 5)s/d

C2-1) materially creditworthy

μearning c. creditworthy + μexpenses c. creditworthy

C2-2) Expenses circumstances not creditwort.

μearning c. creditworthy + μexpenses c.not creditworthy

Materially not creditworthy [0, 49]; (0, 24.9)

C2-3) Earning capacity not creditworthy

μearning c. not creditworthy + μexpenses c. creditworthy

C2-4) materially not creditworthy

μearning c. not creditworthy + μexpenses c. not creditworthy

Materially creditworthy

[0, 100]; (0, 50)s/d

[50, 100]; (25, 50)

Income not creditworthy [0, 24]; (0, 12.49)

Income creditworthy

[0, 50]; (0, 25)s/d [25, 50]; (12.5, 25)

Net income (A4211) [0, ≥ 10'000] [0, 10]; (0, 5)s/d Property (A4212) [0, ≥ 1'000'000] [0, 10]; (0, 5)s/d Loan securities (A4213) [very low, ... , very high] [0, 10]; (0, 5)s/d

Account information (A4214) [High, …, low negative; low, ..., very high] [0, 10]; (0, 5)s/d

Inco

me

(A32

1)

other (A4215) - [0, 10]; (0, 5)s/d

Outgoings not creditworthy [0, 24]; (0, 12.49)

Outgoings creditworthy

[0, 50]; (0, 25)d [25, 50]; (12.5, 25)

Rent, mortgage rate (A4221) [very low, … , very high] [0, 10]; (0, 5)s/d Alimonies (A4222) [very high, ... , none] [0, 10]; (0, 5)s/d Financial obligations (A4223) [very high, ... , none] [0, 10]; (0, 5)s/d Reason for credit (A4224) [un-, ... , important] [0, 10]; (0, 5)s/d

Ove

rall

cred

itwor

thin

ess

(A1)

M

ater

ial c

redi

twor

thin

ess

(A22

)

O

utgo

ings

(A32

2)

other (A4225) - [0, 10]; (0, 5)s/d

Legend: scores assigned to values of c: continuous ( ) or d: discrete membership functions ( ); s/d: both possible

Appendix

143

So eine Arbeit wird eigentlich nie fertig, man muss sie für fertig erklären,

wenn man nach Zeit und Umständen das Mögliche getan hat.

Johann Wolfgang von Goethe

- 144 -

Statement

FACULTE DES SCIENCES ECONOMIQUES ET SOCIALES / WIRTSCHAFTS- UND SOZIALWISSENSCHAFTLICHE FAKULTÄT DEKANAT BD. DE PÉROLLES 90 1700 FRIBOURG

E R K L Ä R U N G

Ich versichere, dass ich die vorstehende Arbeit selbständig angefertigt und entsprechend den Grundsätzen wissenschaftlicher Ehrlichkeit abgefasst habe.

Es ist mir bekannt, dass andernfalls die Abteilung gemäss dem Abteilungs-beschluss vom 28.11.1984 das Recht hat, den auf Grund dieser Arbeit verliehenen Titel zu entziehen.

Fribourg, den 7. März 2007

............................................

Darius Zumstein

customer performance measurement · using appropriate tools and methods of customer relationship...

Documents