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

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 AnalysisAgenda • Introduction • Preliminary Analysis • The SAS Program • Cluster Analysis Results/Interpretation • Validation/Implementation • Case Study: Bankcard Targeting

8. Cluster AnalysisIntroduction • 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 AnalysisPreliminary Analysis • Data Selection • Missing Values • Standardization • Removal of Outliers • Cluster Analysis Considerations

10. Cluster AnalysisPreliminary Analysis: Data Selection • 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

11. Cluster AnalysisPreliminary Analysis: Missing Values • 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

12. Cluster AnalysisPreliminary Analysis: Standardizing & Removing Outliers • 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

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

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

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

16. Cluster AnalysisCase Study: Bankcard - Attribute Means

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

18. Cluster AnalysisCase Study: Bankcard - Performance

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

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