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Marketing Strategies for Retail Customers

Based on

Predictive Behavior Models

Glenn HofmannHSBC

Salford Systems Data Mining 2005

New York, March 28 – 30

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Objectives

• Inform about effective approach to direct marketing in retail:

– Creation of single activity score from comprehensive data.

– Effect on top line sales and ROI.

– Ease of customer targeting and strategy development.

• Get feedback and ideas for improvements.

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Agenda

1. Background

2. Goals and vision

3. Data preparation

4. CART Modeling

5. Marketing strategies

6. Marketing results

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Background: HSBC

• Second largest financial services organization in the world.

• 232,000 employees in 76 countries.

• Over 110 million customers worldwide.

• US$1,154 billion in assets (June 2004)

• Top 10 credit card issuer in U.S.

www.hsbc.com

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Background: Retail and store credit cards

• Retailers employ credit cards to

– Increase sales (buy now – pay later).

– Collect customer information.

– Provide additional revenue stream.

• 20 – 70% penetration (sales on store-branded cards).

• About 50% convenience users, 50% revolvers.

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Background: Marketing challenges

• Increase sales at major events

(holidays, back-to-school, end-of season-sales).

• Decrease attrition

– Rich initial offer causes many customers to use card only once.

– Large turn-over due to competition.

• Reactivate inactive customers.

• Keep high spenders loyal.

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Goals

Desired characteristics for analytic tools:

• Enable accurate targeting of relevant segments (high lift).

• Ease of use for marketers at all levels.

• High ROI from direct mail campaigns.

å Effective marketing strategy and execution.

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Vision: Score idea

• Financial + credit card related account and transaction data

• Demographic data

Credit risk:

Direct Marketing:

Single number per customer

FICO score

• Account information• Transactions • Demographic data

Single number per customer

AIAscore

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Data preparation: Customer-level information

Account

- Opening date- Payment history- Payment problems- Credit limits- Blocks- Name, Address

Transactions

Item-level purchases and returns with- Date, Store- SKU/UPC, Amount- Department

Demographics

(3rd party append)- Age- Gender- Income- Family status- Children

Cluster code

(3rd party append) E.g.,- Personicx- Cohorts- Prism- MOSAIC

Summarize on customer level

Transform, prepare, combine, create new variables

Hundreds of customer-level variables

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Data preparation: Criteria for variables

• Predictive value

– Make key variables more predictive through adjustments

(e.g. gender, activity criterion).

– Continuous better than discrete.

• Robustness w.r.t. monthly and seasonal changes

– Data quality: Classification not based on missing/nonmissing

(e.g. demographic story) or other volatile information (e.g. account balance).

– Standardize seasonal data, examples:

Purchases in last 3 months (Dec. vs. March)

Purchases since 1/1/2002

å Standardize by values for all customers

(but not other variables: e.g. number of visits in lifetime of account).

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Data preparation: Additional predictors

Individual customer-level variables

Summarize by store Summarize by zip Summarize by cluster

Store-based :- Square footage- FTE- Sales

Zip-level census:- Mean income- # households- Pop. density

Cluster-level:- Mean income- Net worth- # children distr.

Reattach categorized information to each customer

Deviations of individuals from category summaries

Final set of predictor variables (several thousand)

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Data preparation: Summaries by category

Categories: store, zip code, household cluster

Continuous variables å Mean, Median, Standard Deviation(spend, #visits, age)

Discrete variables å Absolute and relative frequencies(gender, activity level, for each possible valuerewards level) (infrequent values grouped in “other”)

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CART modeling: General tips

• Work on random samples (100,000 customers).

• Set categorical variable penalties.

• Reduce default depth level and number of nodes.

• Use random subset for validation.

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CART modeling: Implementation

1. Model subsets of predictors (customer, store, zip, cluster).

å Find relevant variables (importance).

ĻPut all relevant variables in single set.

2. Create final model (final list of variables).å Make set with needed variables only (smaller file).

3. Use bagging to create grove file.

4. Score entire population (average predicted probabilities from multiple trees).

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CART modeling: Issues and paradoxes

• Categorical variables can cause trouble if in top nodes.

å Play with removal and penalties.

• Joining subset of predictors å unexpected behavior (limitation of CART)

Store summariesIndividual demographics

30% error 30% error

Individual demographics and store summaries

35% error

“Solution”: Play with removal of variables

Other ideas?

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CART modeling: Results

Score expresses likelihood of customer to use the card within the next 12 months.

Scores distribution Classification matrix(using .35 score cut-off point)

Predicted

TrueActive Inactive

Active 90% 10%

Inactive 14% 86%

Gains chart (for identifying future active customers)

Important predictor variables?

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Vision: Score idea

• Financial + credit card related account and transaction data

• Demographic data

Credit risk:

Direct Marketing:

Single number per customer

FICO score

• Account information• Transactions • Demographic data

Single number per customer

AIAscore

Proxy for many response models

• More accurate than RFM• Easier to use than RFM

Effective driver of

marketing strategy

and selection

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Vision: characteristics

Advantages: One score instead of multitude of scores for different products (campaigns)

- People with higher credit score are less risky.- People with higher AIA score are more active.

Challenges:

- People with high credit score are not always most profitable.- People with high active score do not always give largest

incremental in marketing campaigns.

Scope:

- FICO for all credit products- AIA limited only by availability of transaction data

• Currently: merchant and tender (PLCC) specific• Potential: product segment specific (apparel, power sport, etc.)

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Marketing: Single score implementation

Recuperating inactive customers (x+ months without purchase): å High all-information activity scores.

Special events: Detection of customers with high incremental

spend potentialå Highest AIA scores (strong correlation with campaign response).

Detection of low-incremental customers (marketing exclusion):å Low scores.

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Marketing: Special strategies (with further information)

Detection of high-potential customers among inactives: å High demographic-potential activity scores, lower

all-information activity scores.

Attrition prevention (early detection): å Active customers with low or declining AIA score, but

reasonable past purchase behavior or demographic potential.

Early loyalty enrolment (Gold card + soft benefits):å High loyalty score (propensity to meet rewards threshold in near

future).

Conversion of high spenders from competition or other tender:å Profitable customers with less frequent store visits / card use than

indicated by demographic potential.

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Results: Seasonal sales event

Incremental contribution of direct mail

Only implementation difference:

Which set of 300,000 customers is targeted by mail piece.

Confidentiality note: Dollar values are fixed multiples of actual values.

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Results: Reactivation mailing

Incremental contribution of direct mail

* Model selection from customers 8+ months inactive, using highest all-information active scores.

** Selection by straightforward inactivity criterion, selecting all customers 8, 10 and 11 months inactive.

Confidentiality note: Dollar values are fixed multiples of actual values.

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Results: Early rewards enrolment

Confidentiality note: Dollar values are fixed multiples of actual values.

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Results: Early rewards enrolment

Confidentiality note: Dollar values are fixed multiples of actual values.

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Summary

1. Predictive scores for customer behavior can

• Simplify effective selection.

• Enable powerful direct marketing strategies.

• Increase top line sales and ROI substantially.

• Potentially provide entire industry with powerful std. tool.

2. Comprehensive customer data ensures modeling success

• Reattached category summaries substantially improve model.

3. (Once developed,) scores are easy to use for marketers.

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Audience feedback

I welcome

• Ideas for improvements.

• Information on other approaches.

• Your experiences.

glennhofmann@yahoo.com