1. Cluster Analysis in Financial Services SESUG ’98 Satish Nargundkar/Tim Olzer

2. Basic Assumption One Portfolio

3. The Reality Many Different Portfolios

4. Segmentation Definition Description of a group of individuals Identification of similarities between members of one group Determination of similarities and differences among and between groups

5. Goals Of Segmentation Identify the various sub-populations Analyze or manage segments separately based on general characteristic attributes

6. Types of Segmentation Judgmental Segmentation Bivariate Segmentation Predictive Segmentation – CART, ChAID, etc. Non-parametric Segmentation – Cluster, Factor Analysis, etc.

7. Cluster Analysis Agenda l Introduction l Preliminary Analysis l The SAS Program l Cluster Analysis Results/Interpretation l Validation/Implementation l Case Study: Bankcard Targeting

8. Cluster Analysis Introduction Definition: The identification and grouping of consumers that share similar characteristics Yields: better understanding of prospects/customers Translates into: improved business results through revised strategies

9. Cluster Analysis Preliminary Analysis Data Selection Missing Values Standardization Removal of Outliers Cluster Analysis Considerations

10. Only want a small subset of variables for clustering Weed out undesirable variables – Can use PROC FACTOR, PROC CORR – Can use expert system Consideration for observations, weighting Cluster Analysis Preliminary Analysis: Data Selection

11. Probably done with factor analysis If not, then two options – Set Missing to Mean of data – Set Missing to Value of Equivalent Performance No right or wrong answer Might do both - depending on variables Cluster Analysis Preliminary Analysis: Missing Values

12. PROC STANDARD (m=0,s=1) - Why? Two options for outliers – Cap at a given value – Remove observations No right or wrong answer Advatages/Disadvantage to both Cluster Analysis Preliminary Analysis: Standardizing & Removing Outliers

13. Types of Clustering Cautions – Sensitive to Correlation – Heuristic not Statistic Cluster Analysis Preliminary Analysis: Cluster Analysis Considerations

14. l Bank Credit Card Environment l Objective: create an “external” prospect view to better target product offers l Cluster Analysis employed to create homogeneous sub-populations within prospect base l The resulting cluster profiles used to assist in product design and targeting Cluster Analysis Case Study: Bankcard Targeting

15. Prospect Base Prospect Base Young Families Young Families Country Club Set Up and Coming Properous Revolvers Properous Revolvers New to Credit New to Credit Other Shuffle Board Set Cluster Analysis Case Study: Bankcard Targeting

16. Cluster Analysis Case Study: Bankcard - Attribute Means

17. Cluster Analysis Case Study: Bankcard - Descriptions l A - Credit Dependent l B - Shuffle Board Set l C - Country Club Set l D - Prosperous Revolvers l E - Prosperous Transactors

18. Cluster Analysis Case Study: Bankcard - Performance

19. Cluster Analysis Case Study: Bankcard - Integrating Models with Profiling Vertical or Compiled Lists Data Prospect Universe Apply Basic Exclusions Create Prospect Profiles Cluster 1 Cluster 2 Cluster N …..

20. Cluster 1 ------------ Calculate Scores (Risk, Response, Utilization) Overlay Profitability Estimate Evaluate Risk-Return Tradeoff (by Offer and by Cluster) Make Final Selections Product/Offer 1Product/Offer 2Product/Offer N -------- LowRETURNHigh Low RISK High Mail No-Mail Cluster Analysis Case Study: Bankcard - Integrating Models with Profiling