Marketing Strategies for Retail Customers
Based on
Predictive Behavior Models
Glenn Hofmann
HSBC
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
Credit risk:
• Financial + credit card
related account and
transaction data
• Demographic data
Single number
per customer
FICO
score
Direct
Marketing:
• Account information
• Transactions
• Demographic data
Single number
per customer
AIA
score
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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
Store-based :
- Square footage
- FTE
- Sales
Summarize by zip
Zip-level census:
- Mean income
- # households
- Pop. density
Summarize by cluster
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
(spend, #visits, age)
å
Mean, Median, Standard Deviation
Discrete variables
(gender, activity level,
rewards level)
å
Absolute and relative frequencies
for each possible value
(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).
Put 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 summaries
Individual 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
True
Active
Inactive
Active
90%
10%
Inactive
14%
86%
Gains chart
(for identifying future active customers)
Important predictor variables?
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Vision: Score idea
Credit risk:
• Financial + credit card
related account and
transaction data
• Demographic data
Single number
per customer
FICO
score
Direct
Marketing:
• Account information
• Transactions
• Demographic data
Single number
per customer
AIA
score
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 potential
å 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.
[email protected]
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