Notes Version: The Paradigm From Sales To Profits Using Optimization Analytics

Notes Version: The Paradigm From Sales To Profits Using Optimization Analytics

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  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 1
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  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 2
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  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 3
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  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 4
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  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 5
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  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 6
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  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 7
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  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 8
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  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% 9
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  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 10
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  9/30/2011 Thank You! Marcy Riordan Michael Parduhn ANALYTIC VISION. BUSINESS IMPACT. For more information contact: mriordan@iknowtion.com (781) 494-9989 11