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

Basic Assumption One Portfolio

The Reality Many Different Portfolios

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

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

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

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

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

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

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

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

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

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

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

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

Cluster Analysis Case Study: Bankcard - Attribute Means

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

Cluster Analysis Case Study: Bankcard - Performance

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 …..

Cluster 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