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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