1 Optimizing Marketing Campaigns by the Use of Data Mining Methods for the Hamburg-Mannheimer Insurance Die Kaiser-Rente ® Glück ist planbar Thomas Rauscher - ITERGO Informationstechnologie GmbH

2 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 Br

3  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 Br

4 Why does Hamburg-Mannheimer Insurance use 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 Br

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

6 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

7 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“ Br

8 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 Br

9  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 Br

10 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 ~ customer contacts The Big Challenge  Whole project was started in February 2001, product launch and the first campaign were targeted to 1. of July Campaigns for the Kaiser-Rente ® R

11 Project organization: Who was involved?  1 Marketing Expert (Hamburg-Mannheimer)  Modeling and quality control  2 external Programmers  Data management and sampling  1 Data-Mining-Expert (ITERGO)  Data mining and scoring  1 Programmer (ITERGO)  Customer selection and printing  1 Sponsor (Hamburg-Mannheimer) Basis conception and coordination of sales activities

12 Amount of campaign activities (in days)

13 Model 1: First Campaign (with product launch)  One big Problem: No experience, no historical data ! The solution: Two particular groups of customers: R  Contracts with ‚Anpassungsgarantie‘:  Option to change from a classical private pension to the Kaiser-Rente in July 2002 after Certification  Customers who responded to a mailing with information about the Kaiser-Rente

14  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 : Collection of potential predictors from the customer database (sample of total population) : Collection of target variable, (Contract Kaiser-Rente) and Sampling : Data Mining Process : Scoring for the complete customer database, Customer Selection for the campaign R Model 2: Second Campaign (after product launch)

15  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 Br

16 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 R

17 Example: Mining-Model (SAS Enterprise Miner) R

18 Quality of Data  most 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 R Statistical Challenges

19  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 R Influential Variables A selection of variables predicting the probabilty of signing a contract for the Kaiser-Rente:

20  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 Br

21 Product launch for the Kaiser-Rente ®  Customer selection for sales contact - Campaign 1: selected customers - Campaign 2: selected customers  defined contact forms printed for the sales agents Br

22 Contact report Br

23 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 Br

24 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 R

25 Campaign 1 (Response Rate by Score) Average Sales Org. A Average Sales Org. B

26 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 ? Br

27 The ‚Wave‘-Problem

28 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 Br

29 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? Br

30 Thanks for your attention ! Contact Thomas Rauscher Anwendungsentwicklung Data Warehouse ITERGO Informationstechnologie GmbH Überseering 35, D Hamburg Tel. (++49) (0) VG-QS/ITERGO November 2002