CVS Caremark utilized lift modeling and constraint-based optimization to improve their POS coupon program. They developed predictive models to identify customers most likely to purchase incrementally in response to offers. Testing found their initial models did not improve results, but refining the models using more data and a combined analytic and strategic approach significantly increased incremental sales and profit per customer. Lessons included focusing on the right success metrics, challenges of lift modeling, and the need for both analytic techniques and business knowledge.

1. 9/30/2011
What to Expect from This
Session
 Explore the fundamentals and benefits of Lift Modeling
and Constraint-Based Optimization Analytics
 Learn how CVS Caremark has utilized their POS
channel to deliver relevant offers and improve ROI
- Behind-the-scenes analytic engine that drives
differentiated offers to unique types of customers
- Program evolution
- Lessons learned
Introductions
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2. 9/30/2011
Who We Are
 Consulting firm specializing in information-based marketing
and customer intelligence
 Founded in 1999, with headquarters in Burlington, MA
 Business problem solvers, skilled in analytics
 Broad, practical experience across multiple industries
 Outcomes driven - strive to deliver work that produces
significant bottom line impact
What We Do
Customer
Intelligence
and Reporting
Support Business Decision
Multi-media
Making / Processes
Predictive
Marketing Analytics and
Effectiveness Scoring
Increase Business Growth
and Profitability
Campaign Customer
Planning and Acquisition and
Measurement Development
Optimize Marketing
Loyalty
Program Spending
Strategy
CVS
 What does CVS stand for?
a. Consumer Value Stores
b. Caroline, Vanessa, Sophie
c. Convenience, Value, Service
d. Computer Vision Syndrome
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3. 9/30/2011
CVS
 Nation’s largest retail pharmacy chain
 Recently opened 7,300th store
 CVS fills more than 1 out of every 7 prescriptions
in America
 ExtraCare is the largest and most successful
retail loyalty program in the country
ExtraCare Loyalty Program
(brief video)
Do you have your ExtraCare
Card?
 68 MM active ExtraCare cardholders
 70% of CVS transactions are on the card
 ExtraCare customers visit the store every month, which
is twice as often as the typical non-ExtraCare shopper
 Every second, 32 ExtraCare cards are scanned in the US
 The very next day, these transactions can be
transformed into customer insights
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4. 9/30/2011
Lift Modeling and
Optimization
Incremental Sales Potential
“Best” customers are not always the same ones who drive campaign profitability
Best Customers - Top Sales Promotion Sensitive,
High Incremental Sales
Limit or Exclude from New Population -
Discounting Strategy Test Offer Levels
Response Model Lift Model
Best
Offers
Lift Modeling
Focus directly on the consumer behavior that drives ROI
 Predicts incremental response rather than total response
 Model score provides a mechanism to target customer
with the highest likelihood to deliver incremental sales
 The challenge: incremental sales is an aggregate
measure, while modeling and targeting must be done at
the individual level
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5. 9/30/2011
Offer Optimization Engine
Objective:
Determine the optimal offer assignment for each customer given
certain goals and constraints
 Automated approach to offer assignment; allows quick evaluation
of numerous scenarios
 Optimization targets: maximum lift sales, program revenue, units,
ROI, margin impact; minimum incentive cost
 Optimization Constraints: budget, incentive cost, ROI hurdle,
total sales
 Flexibility is a key benefit
Optimization in Action
How do you do it?
Start by asking: are you leveraging everything you know about your customer?
Shopping
Behavior
Promotion Category Breadth
History / Channel
Preference
Basket Profiles
Digital Behavior
Tenure
Payment Type
Employees /
Demographics
Non-Employees
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6. 9/30/2011
POS Marketing
at CVS
POS Coupons - Overview
Sophisticated targeting engine drives delivery of POS Coupons
 Printed at Bottom of Cash Register Receipt
 Primary Objective: Drive return visits
 Customer data (what they buy, who they are) drives
offer assignment
 Offers: Product-Specific or “Open-Ended”
 Open-Ended Offers
- Discount on any non-Rx item in the store
- Automated trigger program
- Redeemable on next visit
- Broader offers drive most sales lift and ROI
- Largest CVS-funded incentive program
CVS Approach – Trigger Offers
Customized and Dynamic Targeted Offers
Illustrative
Customer 1: Customer 2: Customer 3:
Spends $12.50 Spends $22 per Spend $15 per
per trip, visits trip and, visits 4x trip and, visits 5x
3x Qtr Month Qtr
ExtraCare/coupon
John Smith
Offer Configuration and Cadence determined
quarterly by mining millions of data Points: Save $4 on any $20 purchase.
(UP TO $4.00 VALUE)
Category Past Profit Rx
Shopped
Demos
Offers Level
Promos
Freq EXPIRES 11/4/2011
ExtraCare® Card #*******8047
*EXTRACARE CARD MUST BE PRESENTED TO GET THESE
Customer 2:
Customer 1 Customer 3: SAVINGS. EXCLUDES TOBACCO, ALCOHOL, AND PRESCRIPTIONS. NO
Matched up to CASH BACK. TAX CHARGED ON PRE-COUPON PRICE WHERE REQUIRED.
Matched up to Matched up to LIMIT ONE PER CUSTOMER.
$10 off $50 3363805547
$3 off $15 offer $4 off $20 offer
offer
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7. 9/30/2011
Evolution of POS Program
Ongoing innovation and refinement has ensured lasting program success
Step 5

Step 4 Optimize
Optimization
Step 3
Innovate System
Step 2 Refine Lift Modeling
Response,
Step 1 Target and Modeling,
Differentiation
Execute
Build Fact R, F, M
Base
Test and Learn We were here
This could not be achieved without a commitment to test and learn
The Catalyst: Declining ROI
Response model targeting was beginning to be unprofitable – need for
investigating drivers and potential solutions
Campaign ROI
20%
15%
10%
5%
0%
Campaign Incremental Discount / Net Net
Sales Sales (Lift) Coupons Incremental Incremental
-5% Sales Margin
-10%
-15%
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Key Question: Who drives incremental sales?
2009 2010
Modeling Approach
 Explore a wide range of variables to capture key customer
behaviors
- Promotion sensitivity and history, brand preferences, loyalty, demos
 Create multiple predictive models for each step in the
coupon redemption process
Print Redeem Spend
visit, purchase visit, purchase basket size
 Estimate a wide range of success metrics
 Optimize offer assignment, unique to each customer, given
predicted behavior in response to each offer scenario
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8. 9/30/2011
Program Structure
The final solution involves multiple models that capture the profit dynamics
Econometric Business
Data Inputs Optimization
Modeling Metrics
Shopping Sales
Behavior /
No Offer
Loyalty
Price &
Promotional
Sales Lift $3 off $10
Sensitivity
Predictive
Coupon Usage Models Cost $4 off $20
(14)
Category Margin
$5 off $25
Patterns Lift
 Qualifier (1)
 Redemption (8)
Customer  Sales (5)
Demographics ROI $10 off $50
Recommended Target Changes
Significant changes to targeting and offer assignment were recommended
Old Target Drop Keep Add New Target
 Higher incremental
sales
Don’t Target
 Stronger ROI
The Test
A test was implemented to compare the new approach to the old
Eligible Customers
New Strategy - Lift
Existing Strategy -
Modeling and
Response Model Based
Optimization Based
Control Group Control Group
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9. 9/30/2011
Test Results
It didn’t work!
Incremental Sales and Profit Per Customer
Incremental Sales Incremental Profit
Existing Model New Model
 $3 and $10 coupon worked well
 $4 coupon drove negative results
 Identified disconnect in success metrics
If At First You Don’t Succeed…
 Granted another opportunity to “split” the eligible
population in a head-to-head test
 Refined and implemented models using more recent data
 Tested alternative approaches
- Model Based Targeting and Offer Assignment
- “Strategy-Based” approach – art plus science
Art + Science Wins!
Incremental Sales and Profit Per Customer
Incremental Sales Incremental Profit
Existing Model New - Model Based New - Strategy Based
ROI 4% 15% 16%
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10. 9/30/2011
Lessons Learned
 Lift Modeling is not straight forward – be open to testing
- Most challenging: predicting incremental sales dollars and ROI
- There are several alternative modeling approaches
 Focus on the right metrics, and be consistent with
corporate KPIs
 Optimization is likely to require human intervention – solid
business understanding and good intuition
In Conclusion
 Leverage as much data and customer insight as you can
to maximize
 The next generation of marketing analytics is moving
beyond traditional response modeling
- Understand your success metrics and use analytic tools that
focus directly on those metrics
- Be aware of the challenges
 The best solution requires a balance of technique and
business knowledge
Getting Started
 Set up randomized testing with appropriate treated /
control quantities
 Capture historical campaign data
- Customer characteristics at time of execution
- Response results
 Ensure operational system infrastructure is in place
 Identify resource to develop solution
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11. 9/30/2011
Thank You!
Marcy Riordan Michael Parduhn
ANALYTIC VISION. BUSINESS IMPACT.
For more information contact: mriordan@iknowtion.com (781) 494-9989
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