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Optimizing Marketing Campaigns by the Use of Data Mining Methods

for the Hamburg-Mannheimer Insurance

Die Kaiser-Rente®

Glück ist planbarDie Kaiser-Rente®

Glück ist planbar

Thomas Rauscher - ITERGO Informationstechnologie GmbH

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Overview:

1. Commercial Goals: Why Data Mining ?

2. Setting up a Data Mining Project

3. Into the Mining Process: Statistical Challenges

4. Doing the Campaigns & Controlling of Results

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1. Commercial Goals: Why Data Mining?

2. Setting up a Data Mining Project

3. Into the Mining Process : Statistical Challenges

4. Doing the Campaigns & Controlling of Results

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Why does Hamburg-Mannheimer Insuranceuse Data-Mining-Methods?

Use valuable information from the customer database Better targeting of sales and backoffice activities Customer segmentation

The Projects: 1999/2000 cancelation reduction for life insurance 2001 campaign management for the Kaiser-Rente 2002 recruitment controlling for new agents for HMI

sales organisation from 2001 on: customer selection for several mailings

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The Basic Concept:The basic idea about the usage of data mining methods is the targeting of valuable customersIn this context ‚valuable‘ means that these customers are likely to respond to a particular offer or activity

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The Project „Kaiser-Rente® “„Riester-Rente“ = private pension with additional governmental funding (amount of funding based on income and number of kids) „Kaiser-Rente®“ = name of the product offered by the Hamburg-Mannheimer

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The Target Group for the „Riester-Rente“Governmental funding would be availble for all employees paying social security fees: 30 Million German inhabitants 2,7 Million Hamburg-Mannheimer customers

Doubling the market share in the new market 4% existing market share for classical like insurance 8% expected market share as target for ‚Riester-Rente‘

The Commercial Goal

The Slogan: „Glück ist planbar“ „Luck can be planned“

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Optimization of Marketing Campaigns for the Kaiser-Rente®

Question:Which customers are most likely to sign a contract for the Kaiser-Rente?

Action: Selection of those customers who must be first contacted for the whole sales organisation (mandatory!) directly after product launch of the Kaiser-Rente Tracking of results, selection of customers for follow-up campaigns

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1. Commercial Goals: Why Data Mining?

2. Setting up a Data Mining Project

3. Into the Mining Process : Statistical Challenges

4. Doing the Campaigns & Controlling of Results

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4 Major Campaigns July 2001: 1. Campaign (with product launch) October 2001: 2. Campaign (after product launch) March 2002: 3. Campaign January 2003: 4. Campaign

Each Campaign should cover ~ 300.000 - 400.000 customer contacts

The Big Challenge Whole project was started in February 2001, product launch and the first campaign were targeted to 1. of July 2001.

Campaigns for the Kaiser-Rente®

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Project organization: Who was involved?1 Marketing Expert (Hamburg-Mannheimer)

Modeling and quality control 2 external Programmers

Data management and sampling1 Data-Mining-Expert (ITERGO)

Data mining and scoring1 Programmer (ITERGO)

Customer selection and printing 1 Sponsor (Hamburg-Mannheimer)

• Basis conception and coordination of sales activities

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Amount of campaign activities (in days)

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0

0

25

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35

15

50

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0 10 20 30 40 50 60

Campaign

Mining

Data Management

Modeling

1.Campaign

2.Campaign

3.Campaign

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Model 1: First Campaign (with product launch)

One big Problem: No experience, no historical data !The solution: Two particular groups of customers:

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9.000 Contracts with ‚Anpassungsgarantie‘: Option to change from a classical private pension to the Kaiser-Rente in July 2002 after Certification

2.000 Customers who responded to a mailing with information about the Kaiser-Rente

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Analysis of first Contracts for the Kaiser-Rente®

from July and August 2001

Process (same as first campaign)

Contract for a Kaiser-Rente

No Contract

30.6. 2001:

Collection of potential predictors from the customer database (sample of total population)

31.8. 2001: Collection of target variable, (Contract Kaiser-Rente) and Sampling

1.9. - 15.10.2001: Data Mining Process

15.10 2001: Scoring for the complete customer database,

Customer Selection for the campaign

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Model 2: Second Campaign (after product launch)

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1. Commercial Goals: Why Data Mining?

2. Setting up a Data Mining Project

3. Into the Mining Process:Statistical Challenges

4. Doing the Campaigns & Controlling of Results

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Technical Environment

Database: HM Customer Database (DB2).

Data Management Tool: SAS Data Selection from DB2 into SAS-Datasets Data Manipulation and Merging Download to a NT-Server for the Data Mining Process

Mining-Tool : SAS- Enterprise Miner automatically generates SAS-Code for scoring of the complete customer database

The complete Workflow was done using SAS-Software

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Example: Mining-Model (SAS Enterprise Miner)

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Quality of Datamost important issue (!) that can only be controlled properly by perfect knowledge or backtracing analysis of data sources

Choice of Method: Regression vs. Tree-Algorithm none of both is dominant in performance. Tree: Needs less variables, easier to interprete for non-statisticians, more robust to outliers Regression: easier to interprete for statisticians, better control about variable selection and multicollinearity For the Kaiser-Campaigns both decision trees and regression were used for different campaigns and subgroups

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Statistical Challenges

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Time since last contact to any agent Contacting Sales organization Classical life-insurance-contract (yes/no) Status of contacting sales agent Number of kids Type of Bank account Age

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Influential Variables

A selection of variables predicting the probabilty of signing a contract for the Kaiser-Rente:

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1. Commercial Goals: Why Data Mining ?

2. Setting up a Data Mining Project

3. Into the Mining Process : Statistical Challenges

4. Doing the Campaigns & Controlling of Results

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Product launch for the Kaiser-Rente®

Customer selection for sales contact - Campaign 1: 400.000 selected customers - Campaign 2: 290.000 selected customers defined contact forms printed for the sales agents

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Contact report

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Target and Control Groups

Campaign 1: 1/3 of customers as control group: random selection regardless of scoring value

Important: Control group of Campaign 1 came to be the base population needed for campaign 2 modeling !

Campaign 2 - 4: 1/5 of customers as control group

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Results of Campaign 1 (from control group):

Customers in the first percentile had a response rate which was 3.4 times higher than the response for the total population

Percentile of ‚best‘ customers

Ratio of response rate below percentile / total population

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Campaign 1 (Response Rate by Score)

0+ 20+ 40+ 60+ 80+ 100+ 150+ 200+ 250+ 300+ 350+ 400+ 450+ 500+ 550+ 600+ 650+ 700+

% o

f S

old

Co

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Sales Organisation A Sales Organisation B

Average Sales Org. A

Average Sales Org. B

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Consequences and Results for Campaign 2

The different behaviour of the two sales organization led to the development of different models for those organisations during the mining process for Campaign 2

Results: Again good seperation between high and low score intervals, but: much weaker lift in response rate between target and control group Why ?

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The ‚Wave‘-Problem

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

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0+ 50+ 100+ 150+ 200+ 250+ 300+ 350+ 400+ 450+ 500+ 550+ 600+ 650+ 700+

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Campaign I Campaign II Rest

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Consequences for Campaign 4

Following the original concept Campaign 4 should cover a seclection of those customers who had not been selected for Campaign 1 to 3

Change of Concept: Campaign 4 was focused on recontacting the highest-scored customers from campaign 1 to 3 who had not yet signed a contract for the Kaiser-Rente

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Conclusions

When using Data Mining in a commercial context, not the statistical quality of modeling and analysis is of primary interest, but three other issues:

Data Quality, good knowledge of data sources

Well defined target variable: What is the question that shall be answered by Data Mining methods?

Well defined actions: What shall actually be done with the results of the Data Mining process?

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Thanks for your attention !

Contact

Thomas RauscherAnwendungsentwicklung Data WarehouseITERGO Informationstechnologie GmbHÜberseering 35, D - 22297 HamburgTel. (++49) (0)40 6376-6613E-mail: thomas.rauscher@itergo.com

VG-QS/ITERGONovember 2002