real world advanced customer intelligence...renewals = x new orders = y # orders = z sum quantity =...

COPYRIGHT © 2010 BY PHIL WINTERS. ALL RIGHTS PROTECTED AND RESERVED.

Real‐World Advanced Customer Intelligence Phil WintersStrategic Advisor – Peppers & Rogers Group

COPYRIGHT © 2010 BY PHIL WINTERS. ALL RIGHTS PROTECTED AND RESERVED. 2

Who IS this 007 guy ??????

1976JCLAssemblerFortranPLIPascalCobalSPSSMathlabChemshareSimsci………SAS


Agenda

►Predictive Analytics and Data Mining

►Customer Intelligence

►KNIME used in Anger: The Economist

►Conclusions and Directions.


the collection, examination, summarization, manipulation, and interpretation

of quantitative data to discover its

underlying causes, patterns, relationships, and trends.

Definition:

Predictive Analytics and Data Mining

Intelligence


New fact‐based insight created from the analytic process

Intelligence….

Chemical

Sciences

Pharmaceutical

Intelligence


Agenda






Cus∙tom∙er (kŭs'tə‐mər) [kuhs‐tuh‐mer] Oxford English Dictionary

• noun1. An individual who buys

(or interacts for) goods or services

1.2. A group of individuals

with some commonality

• Consumer• Citizen• Business contact• Parishioner• Doctor• Car dealer• Etc.

• Households• Communities• Constituancies• A Businesses• "B to B"• Etc.


An approach for differentiating and acting based on Customer Intelligence

Customer Intelligence: IDIC

Customers as Unique Addressable

Individuals

• How “addressable” is the customer base?

• What proportion of them are identified/identifiable?

• Can a customer be positively identified and tracked across multiple channels – and purchases?

• What about households and social networks?

By Value, Behaviour and

Needs

• Which customers are most valuable, and why?

• How do customers’ needs differ, and what are the major categories of needs?

• Business clients: How do these questions get answered for the individuals within the customer organisations?

Establish Learning

Relationships

• For what proportion of customers do we have email addresses, and how do we get more of them?

• How do we link individual customers to specific segments? Do we have the right “Golden Questions”?

• Can we track customer interactions through time?

Products, Offering &

Communications

• Based on our customer insights, how do we tailor an offer or treatment to optimise around each customer?

• What is the most efficient way to modularise this?

• How do we measure results for different customers?


The process of creating new, fact‐based information from datathat can be used to identify and differentiate customers into segments.

Customer Intelligence

Which needs are addressed by our products and services such that the benefits are clear and relevant to the customer?

Dreams and wishes are satisfied by perceived value of offerings…

Needs

How does the customer decide for and use our products and services?

Which touchpoints are used where and how often?

Touchpoint priority, decision cycle, product utilisation, channel usage, tenure…

Behaviour

Drive

Disney has identified “Valuable” customers

What specific and measured value does the customer bring to the organisation?

Revenues, cost to serve, potential…

Value

Generates


The focus on needs is particularly relevant, since so many customers’ decisions are social and emotional in nature – not just utilitarian.

Needs

Needs

Utility

Convenient

Time Investment

AccessiblePhysically CompatiblePleasant to the Senses

Economy

Economical Usage

Economical Purchase

PerformingEfficient

Safe

Durable

Social Significance

Prestige

Gains / Saves Face

ImpressesFulfills a Role

Identify

Sense of Belonging

Traditional / Non‐Traditional

Emotional

Pleasure

AffectionateFunExpand Knowledge

Sentiment

Memorable

Spiritual

MoralSacredLucky

80%*

* In saturated markets, 80% of all decisions are based on social and emotional needs.


The Data Analysis Process

• CRISP‐DM• SEMMA• KDD‐Process


Extended Value

Needs Behaviour

Integrated Segments(for Segment Strategy Development)

Micro-level Segments(Behaviour, Needs, Extended Value)

....

....

Quantity + SpeedInformation Junky

Structural ViewDefines ownership

Strategic ViewSegments sizeable enough to receive

management focus

Tactical ViewSegments actionable and focused

enough to create value for marketing

Low Cheep &Cheerful

High

TheRocksMedium

VIP

Macro Segments

Creating relevant segments that can be applied to a range of business issues

12

Macro segments enable you to define and implement different treatment &

experience schemes for these customers directly at the touchpoints (i.e. always

serving high value customers with priority at all touchpoints)

With need & behaviour segments, you can execute segment-specific campaigns very quickly to increase customer profitability

Integrated segments involve certain behaviour and needs segments which are

in alignment, allowing you to manage these segments as a portfolio while

increasing the overall value generated by each one

Opportunity to X-sell premium service!


Agenda

►Analytics





The Economist


KNIME: Open Source, Highly Respected, Gartner Cool Vendor 2010

A Modern Data Mining Workplace


Data QualityEasy to explore and document Data Quality Issues. Created workflow that could also be run AGAIN to ensure corrected source data.


Transactions vs. Customer RecordsGoal in Customer Intelligence is to transform all related transactional and supplemental data into ONE customer record that represents everything about the customer


Time SeriesIs Length good? How do we compare long‐term and short‐term subscribers in terms of their value? What does "valuable" meanß

Total Relationship Value: $ 600Total Relationship Length: 14 Years"Average": $3 per month

Total Relationship Value: $ 600Total Relationship Length: 14 YearsActual Relationship Length: 10 Years"Average": $ 5 per month


Numeric Data ManipulationComplex Numeric transformations and calculations were straightforward


String and Nominal Data ManipulationTransforming character data into SENSIBLE Numerics for use in Data Mining is always required in Customer Intelligence.


Total Customer Worth (TCW)

TCW = Σcontract(URN) NETVALUEMONEY(contract)Valuable, but does not alone allow comparison of customers with different Tenure

Average Customer Worth (ACW)

Σcontract(URN) NETVALUEGMONEY(contract)ACW =

Σcontract(URN) # months(expiry date –start_date)

A relative measure of Money/month regardless of Tenure.

Additional Fields Created


Created with KNIME Report Designer. Provided by KNIME.com GmbH, Zurich,Switzerland

A:

B:

C:

D:

E:

Sample ACW Distribution Groups

ACW proposed classes


ExplorationWhere you need the experts and the expertise to INTERPRET, not to use !


Visualization and Data User Personas

Information Consumers

Power Users

Operational Consumers

– Sales– Marketing– Customer Service– Finance– Tech Support

IT Users

Business Analyst– No DBMS or programming– Strong Excel– Ad-hoc queries– OLAP– Create reports– Publish reports– Custom reports– Understands business metrics

C-level Execs– Annotation– Email

Middle Management

– Drill down– Manipulation– Annotation

IT Admin (Ahmed)

– User administration– Reporting administration– Software administration

Report Administrator

– Business View manager– Understands physical data model– SQL programmer

Consultant– Applications developer

DesignerCorp Comm Specialist

– Style– Format

– Report Builder– Schedule reports– Monitor queue

Data Modeler

– Some DBMS and programming– Strong Excel– Ad-hoc queries– Custom reports– Modeling– Analytics– Detail data– Understands business domain

Power User (Gloria)


"Rules from Decision Trees" can seem like Chinese to business users…Lifetime_Tenure ≤13 (very new customers)1. 5 < Activelife_Tenure ≤ 11 && avg_cover_pct ≤ 0.03 => very new A customers2. 5 < Activelife_Tenure ≤ 11 && avg_cover_pct > 0.03 => very new B customers3. Activelife_Tenure ≤ 5 => very new C,D customers4. Activelife_Tenure > 11 => very new D,E customers

Lifetime_Tenure > 13 (older customers)1. avg_cover_pct ≤ 0.07 && Activelife_Tenure >12 => new A customers2. avg_cover_pct ≤ 0.07 && Activelife_Tenure ≤ 12 => new D customers

3. 0.12 < avg_cover_pct ≤ 0.20 && Activelife_Tenure > 39 => somewhat new A customers4. 0.12 < avg_cover_pct ≤ 0.20 && Activelife_Tenure ≤ 39 => somewhat new C customers….

Activelife_Tenure > 401. 42 < Activelife_Tenure ≤ 73 && avg_cover_pct ≤ 0.20 => A customers2. 42 < Activelife_Tenure ≤ 73 && avg_cover_pct > 0.20 => B,C customers

3. 73 < Activelife_Tenure ≤ 179 && Ratio > 1.17 && avg_cover_pct ≤ 0.4 => A customers4. 73 < Activelife_Tenure ≤ 179 && Ratio > 1.17 && avg_cover_pct > 0.4 => B customers

5. avg_cover_pct ≤ 0.7 && Activelife_Tenure > 179 => A customers

6. avg_cover_pct > 0.7 &&179 < Activelife_Tenure ≤ 214 => B customers7. avg_cover_pct > 0.7 && Activelife_Tenure > 214 => A customers


Productclustering: Make it Clear…


Make it Visual and Understandable

1 2 3 4 5 6 7 8 9 10 11 12

X

E:

# Y

1

2

3

4

5

6

D:

C: 3

B:

B

A

A:

0

13

C

D

B

D

Newer Customers who buyside products"New and Spicy"

Long term customers"Bread and Butter"

Long termcustomerswho also buyside products"All Spice"


E: D: C: B: A:

Make itVisualAndTell A Story


A:

Renewals = XNew Orders = Y# orders = ZSum quantity = AProd = CExtra prod = multi = Bime diff = D

Coverage = E

Cluster5

Cluster10

C:B:

D:

E:

Cluster14

Cluster12

Cluster18

Cluster13

ACW by product: Large Clusters

Old faithful customers


Coverage = E


Coverage = E


Coverage = E


Coverage = E


Coverage = E


Tell a Story, provide a "playground"Build a simple workflow to show major points, leave the users with something they can work with like statistics and hologram nodes


Executing Customer IntelligenceA process that even a non expert can follow !!!

31


Learning Curve"It took me 15 minutes….."


Agenda

►Analytics





I think not…..

A One‐Off Occasion for KNIME?

Travel to Play

SpoilMe !

Play ismost ImportantTotalExperience

Other


Approach: SAS vs. KNIMEThere is a "change of approach and philosophy" issue.A translation table will help!


• More for "Unsofisticated Users"– SQL* *I HATE SQL…… but others love it.– EXCEL Node directly callable (no CSV, etc.)

• Repetitive Tasks– A Callable Shareable Re‐instantiable Metanode (NOT copied!)– Project Documentation

• Character Manipulation– Row Oriented full String Manipulation?

• Analytic Extensions for Nominal Data– Kohonen SOM, LCA, Distance Routines, etc.

Wishes (or am I still learning?)

36


Bringing thought Leadership and management consultancyAcross all industries

Implementing Customer Strategy

Business Specialist CampaignSpecialist

Data Steward TechnicalAnalyst

Knowledge Capture+ Exchange

CICC Staff Skills

CICC Manager

Driver of Change

CICompetence

Centre

Best PracticeCapture +Exchange

Data Stewardship + Data Acqu.

CampaignSupport

Contact Rules Brand Real Estate

Driver of Change

Advanced Analytics

Training

External Resource

Coordination

CustomerStrategyProgramExecution

Drives

Unternehmen Prozesse Information Technologie


delighting customers through their entire decision‐making process across all their preferred touchpoints

Customer Intelligence Agenda

Ad


Definition:

Predictive Analysis

KNIME provides the platform for:

and KNIME

the collection, examination, summarization, manipulation, and interpretation

of quantitative data to discover its

underlying causes, patterns, relationships, and trends.

Intelligence

real world advanced customer intelligence...renewals = x new orders = y # orders = z sum quantity =...

Documents