Cluster Analysis in Financial Services

SESUG ’98Satish Nargundkar/Tim Olzer

Basic Assumption

One PortfolioOne Portfolio

The Reality

Many Different PortfoliosMany 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 AnalysisAgenda

Introduction Preliminary Analysis The SAS Program Cluster Analysis

Results/Interpretation Validation/Implementation Case Study: Bankcard Targeting

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

Cluster AnalysisPreliminary 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 AnalysisPreliminary 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 AnalysisPreliminary 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 AnalysisPreliminary Analysis: Standardizing & Removing Outliers

• Types of Clustering• Cautions

– Sensitive to Correlation– Heuristic not Statistic

Cluster AnalysisPreliminary Analysis: Cluster Analysis Considerations

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

Cluster AnalysisCase Study: Bankcard Targeting

ProspectBase

ProspectBase

YoungFamiliesYoung

Families

Country Club SetCountry Club Set

Up and ComingUp and Coming

ProperousRevolversProperousRevolvers

New toCredit

New toCredit

OtherOther

Shuffle

Board Set

Shuffle

Board Set

Cluster AnalysisCase Study: Bankcard Targeting

Cluster AnalysisCase Study: Bankcard - Attribute Means

Attribute Cluster

Name A B C D E (ALL)

Age of Head ofHousehold 38 62 48 44 52 43

Length ofResidence 9 12 9 6 9 9

HouseholdIncome (,000) 48 45 88 73 71 62

No. RevolvingAccounts 24 2 10 15 8 7

No. Bankcardswith Bal. > 0 13 1 3 6 2 3

BankcardUtilization % 69 6 29 51 7 30

Mos. NewestBankcard open 11 75 21 15 32 35

Cluster AnalysisCase Study: Bankcard - Descriptions

A - Credit Dependent B - Shuffle Board Set C - Country Club Set D - Prosperous Revolvers E - Prosperous Transactors

Cluster AnalysisCase Study: Bankcard - Performance

Cluster AnalysisCase 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 1 Cluster 1 Cluster 1------------

Calculate Scores

(Risk, Response, Utilization)

Overlay Profitability Estimate

Evaluate Risk-Return Tradeoff (by Offer and by

Cluster)

Make Final Selections

Product/Offer 1 Product/Offer 2 Product/Offer N--------

LowRETURNHigh

Low

RISK

High

Mail

No-Mail

Cluster AnalysisCase Study: Bankcard - Integrating Models with Profiling