Notes Version: Driving Results through Strategic Data Sourcing and Optimization Life Line Global Case Study


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Notes Version: Driving Results through Strategic Data Sourcing and Optimization Life Line Global Case Study

  1. 1. 9/30/2011 October 5th, 2011 Driving Results through Strategic  Data Sourcing and Optimization:  Life Line Global Case Study Trish Mathe – Vice President of Database  Marketing, Life Line Screening Ozgur Dogan – General Manager, Data  Solutions Group, Merkle Presenter Backgrounds• Trish Mathe • Vice President of Database Marketing at Life Line Screening • Over 10 years of database marketing experience both in financial services  and healthcare industries • Areas of expertise include: building and maintaining marketing  infrastructure and automation, prospect and customer database  management, campaign management and measurement • Experienced in marketing to the fifty plus crowd, healthcare professionals,  and several other specialty market segments• Ozgur Dogan • General Manager of Data Solutions Group at Merkle • Oversees the delivery of analytical data sourcing and optimization solutions  for Merkle’s clients across all industry verticals  • Spent 7 years at Merkle and has 15 years of industry experience in building,  implementing and integrating database marketing solutions • Technical MBA Degree from the University of Georgia 2 Session Overview1. Evolution in the CRM Data Landscape2. Developing a quantitative framework to assess value of data3. Future Trends and Innovation Opportunities4. Life Line Data Sourcing & Optimization Case Study 3 1
  2. 2. 9/30/2011 Evolution of the Marketing Landscape Global Market Trends• Fundamental changes in the consumer decision making and  buying process• Advancing and evolving technology use• Expanding fragmentation – media and channels• Data explosion driven by emergence of digital media• Clutter and confusion in the data landscape• Increased Accountability and Measurement Ultimately, these influencers are changing the way marketers will create  competitive advantage in the future. 5 Consumers are More Connected Today than Ever Blog Email Search 27%  actively  read blogs 87% use email  87% 27% 86% 86% use search  1+ times per day frequently Social Display 63% use  20% click on  Facebook  63% banner ads weekly IM Mobile 20% 33% use IM  51% are active  regularly 51% texters 33% 6 2
  3. 3. 9/30/2011 Database Marketing Landscape is Evolving DbM 1.0 DbM 2.0 Single Campaign/ Media Targeting Integrated Media Optimization Direct/Identified Model New Entrants Key Trends Domestic US and International Solutions Offline focus Digitalization Cost Pressure Increased Cost Pressure 7 Data Explosion! Today, the codified information base of the world  is believed to double every 11 hours15 out of 17 sectors in the United States have more data stored  per company than the US library of Congress“We create as much information in two days now as we did from  the dawn of man through 2003.”  Eric Schmidt, Google CEO  “Organizations are overwhelmed with the amount of data they  have and struggle to understand how to use it to drive business  results.”  (2010 MIT Sloan/IBM Study) 8 Major Factors Driving Opportunity Emergence Challenges Objectives Solution New Channels & Media Cost Pressures Improve Customer ROI Centricity Increased  Analytic Focus on Complexity Data Sourcing The Customer Accountability & Optimization & Measurement Integrated Approach Increased Message Technology Volume 9 9 3
  4. 4. 9/30/2011 Business Impact of Analytical Data Sourcing Leading direct marketer saved $2 MM in list sourcing cost in it first four 4 months  through analytical data sourcing optimization without negatively impacting  response Total List Spends and Savings $4,000,000 $3,500,000 $3,000,000 $2,500,000 $2,035,459 $2,000,000 $1,500,000 $820,040 $1,000,000 $490,515 $456,425 $500,000 $268,479 $0 Jun Jul Aug Sep Total 2010 Costs 2011 Costs Savings 10 CRM Data Landscape CRM Data Provider Landscape COMPANY TYPES SEGMENTATION TOOL  SYNDICATED  COMPILERS LIST MANAGEMENT SPECIALTY COMPILERS CREDIT DATA DIGITAL DATA PROVIDERS RESEARCH Aggregators, Lifestyle/Behavioral, Generic Clusters - utilizing Panel data representingDemographics & Credit Scores, Owners, Response Data Realty, Transactional, attitudinal, demographics, or consumer attitudes & Firmographics Credit Attributes Audience, Life Events credit information behaviors Analytics 12 4
  5. 5. 9/30/2011 Common Data Types and Constraints Type of Data Examples Common ConstraintsCompiled &  Experian INSOURCE,  ‐ Can only afford one sourceAggregated Data Epsilon TotalSource,  ‐ It is difficult to determine unique value  Data Source so only purchase single sourceSyndicated Research MRI, Scarborough ‐ Unable to implement beyond basic  messaging and product designVertical Lists New parents,  ‐ Too many choices on the market, hard  magazine subscribers to evaluate ‐ Selection limited to a small number of  data card attributes 13 Analytical Data Sourcing and Optimization 14 How to Assess the Value of Data Framework Predictive Power Descriptive Power  Composite Score Source Quality Universe Coverage Key Dimensions for Evaluation: – Predictive Power: Does the source add incremental lift to my predictions? – Descriptive Power: Does the new source provide the ability to better  segment my target audience or lend new insights? – Universe Coverage: Does the source provide access to new and unique  prospects (or overlay to existing customers)? – Source Quality: Does the source provide accurate and high quality data?  15 5
  6. 6. 9/30/2011 Data Optimization Lab 16 Evaluating Value of Data Sources ‐ Example Key Dimensions for Evaluation Predictive Power Descriptive Power  Example Composite Ranking Composite Score Vendor 1 Vendor 2 Vendor 3 Vendor 4 Vendor 5 Vendor 6 Vendor 7 Merkle Composite Score 2.50 6.90 4.60 5.85 4.85 3.90 6.40 1.00 Score Rank 2 8 4 6 5 3 7 1 Source Quality Universe Coverage Module Ranking Vendor 1 Vendor 2 Vendor 3 Vendor 4 Vendor 5 Vendor 6 Vendor 7 Merkle Score 0.2% 0.2% 2.1% 2.5% 4.4% 2.5% 0.2% 0.1% Source Rank 2 4 5 7 8 6 3 1 Quality Rating High High Medium Medium Low Medium High High Score 76.7% 62.6% 68.2% 66.1% 81.6% 83.2% 69.3% 94.0% Universe Predictive Power By Expert Model Rank 4 8 6 7 3 2 5 1 Coverage Rating Medium Low Medium Medium High High Medium High Score 150 138 144 150 145 149 134 151 Overall Model X Model Y Model Z Predictive Rank 2 7 6 3 5 4 8 1 Power Vendor A     Rating High Low Medium High High High Low High Vendor B     Score 95% 31% 53% 81% 80% 63% 45% 100% Descriptive Vendor C     Power Rank 2 8 6 3 4 5 7 1 Rating High Low Medium High High Medium Low High Vendor D      Low  Medium  High 17 Analytical Data Sourcing & Optimization Traditional Data Analytical Data Sourcing Sourcing Incented to increase list Incented to increase list Incentive volume performance and reduce list costs Not fully aligned with Client’s Fully aligned with Client’s Alignment business goals cost efficiency and growth goals Recommendations driven by Analytically Driven OptimizationRecommendations Experience and Relationship Approach Dedicated Team focused on Team Driven to increase commissions Driving performance World Class Analytics Team with Analytics No real analytics or science data optimization experience 18 6
  7. 7. 9/30/2011 List Optimization Dynamics The purpose of the list optimization process is to balance cost and value Maximize List  Value Increase Performance Expand Universe Minimize List Cost  Reduce List Costs Reduce Run Charges Reduce Duplication 19 Analytic  Approach to List Universe Optimization Existing Universe Lists Future Universe Lists List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   List   “N” listsMerkle’s approach is to inform the source /list pool and universe optimization process with analytics to  ROIdefine the right mix and number of lists that maximize ROI N lists # of Lists 20 Optimized Source Mix Illustration The ratio of the Base File names increases in the optimized source mix scenario 21 7
  8. 8. 9/30/2011 Optimization Performed At Multiple Levels LEVEL 1   Source Optimization Identify lists with high performance and lower Expand Universe Through New Lists costs LEVEL 2 Universe Optimization Replace lists with low performance and/or high overlap LEVEL 3 Campaign Optimization Model Scoring Segmentation Today’s  Focus HIGHER PERFORMANCE LOWER COSTS HIGHER VISIBILITY 22Optimization Lab – Data Sourcing and Integration Process Data  Source Source Source  Sourcing Optimization Integration Effectiveness Source Optimization Derived Data  Life Event  Development Triggers Vertical Data Campaign 1 PerformanceCompiled Data Audience Defined Campaign Optimization Optimization Campaign 2 Optimization Universe Campaign ROI Enhanced  messaging &  Credit Data segmentation Source  Effectiveness Campaign 3 Partner DataCustomer Data Deploy Campaign Level  Create the best  Analytics Marketable Universe Source Evaluation 23 Trends and Innovation Opportunities 8
  9. 9. 9/30/2011 Data Sourcing and Optimization As Enabler of  Customer Centricity • Effective ICM™ demands a broad  set of core competencies in order  to be effective.  Data  plays a  central role in delivering on the  vision of ICM. • Understanding the optimal mix  of data, both third party and  customer enables optimal  analytics.   • Analytics informed effectively  through data enables  segmentation, customer  optimization, marketing mix,  media targeting, and predictive  modeling in support of the four  functional areas within ICM. 25 Data Sourcing As Strategic Engagement Phase I ‐ Evaluation Phase 2 ‐ Implementation (Months 0 – 3) (Months 3+) Establish KPI’s Illustrative List  Simulation/Optimization on Optimization Historical Campaigns Refine Optimization Models Evaluation of New Compiled  &  Vertical Sources Execute Test CampaignEarly Harvest Eliminate list sources with  high duplication rates Develop list optimization tool Optimized list sourcing for  Rollout Highlights (incl. brokerage services)  26 Strategic data research and analysis  2626 List Optimization Engine Automates the Process 27 9
  10. 10. 9/30/2011Economic and Environmental Data Integration Economic and Environmental Data Examples  New house starts and vacancy rates  Unemployment rate and per capita personal  income  Consumer pricing and sentiment index  Precipitation and temperature data  Disaster areas Business Impact  Better targeting of products and services that are sensitive to environmental factors  More predictive media mix optimization and allocation models  Ability to explain performance changes due to environmental factors 2 Digital Data Innovation and Integration Online Data  • Place scripts on publisher sites to collect data about interests and in  Aggregators market activity (travel, auto, etc) at a cookie level Anonymous (cookie) • Use the data to optimize online communications like Display Ads audience targeting Online Data  • Collect data across publisher, portal sites on in market activity, user profiles Aggregators • Includes “in market” data and IP‐email connected to postal address PII Targeting  Offline to Online • Providers that own offline data assets match specific offline customer or  prospect audiences to online anonymous IDs Audience Targeting • Several partner with Yahoo!, MSN, AOL for match • Collect online data focused on specific niche areas – B2B, video, semantic  Niche Providers context, network provider, etc. • Online panels evaluate user activity across sites, profiling companies tag  Online Panels sites to profile visitors • The Rapleaf model of providing customer emails to determine social  Social behavior and identify influencers was shut down.  • No clear path to licensing data – most usage is in display  29 Key Take Aways• CRM data landscape is changing rapidly due to digital  media emergency and data explosion• Innovative optimization approach delivers ROI by  reducing data costs and increasing marketing  performance• It’s important to cut through the clutter and identify the  most valuable data assets in the market place including  newly emerging sources like digital• Integrating analytics expertise with data market  knowledge is necessary to gain access to best and most  comprehensive marketable universe 30 10
  11. 11. 9/30/2011 Data Sourcing & Optimization Case Study Life Line Screening Overview• Leading provider of community‐based preventive  health screenings and employs approximately 1000  employees in the U.S. and abroad• Mission is to make people aware of the existence of   undetected health problems and guide them to seek  follow‐up care with their personal physician• Since their inception in 1993, Life Line has screened  over 6 million people, and currently screens 1  million people each year at 20,000 screening events  globally 32 32 Screening Process: Participant’s Experience • “Results Letter”  mailed within 3  Participant Screened At  weeks.Screening  Local Venue: Church,  • Advised to share Scheduled Club, Community Center with physician for  appropriate  follow‐up. • If anything critical  participant is  provided a  Results are reviewed  “Doctor’s Review  by a board certified  Kit” immediately  physician  and advised to go  to a physician or  emergency room  within 24 hours. 33 11
  12. 12. 9/30/2011 Life Line’s Global Expansion Strategy What? Where? Why? Copy & paste model British Commonwealth • English speaking • Cultural similarities • Low regulatory barriers Proof of concept #1: India • English speaking Grass root marketing • Market potential partnership • DM challenging Proof of concept #2: Continental Europe • Non-English speaking Franchise operations • Fragmented regulatory landscape • Good customer response 34 Life Line Projected Global Presence 35 Life Line Business Challenge• Interested in rapidly growing the customer base in US  and across the globe• Using multiple compiled lists provides support to the  large‐scale Direct Mail acquisition program• Limited universe and heavy mailing volume causing  contact fatigue• Applying the learnings generated in US to support the  global expansion strategy with UK as the first pilot  market 36 12
  13. 13. 9/30/2011 CRM Solution Roadmap High Targeting Insight Program Development Measurement Source Incremental P&L and  Hierarchy Integration of Promotion  History  Prospect Segmentation “Silo” Sources Marcom Contact Strategy per  Prospect and Customer level  Insights Segment Impact Brief knowledge on the 50‐75  LTV & Profitability Tracking  years old target population  Integration of Sources @ The Customer Level Multi‐Source Interaction  Creative & Source Testing Campaign  Approach Single level source campaign  level measurement Phase  I Phase  II Phase  III Low High Program Sophistication 37 Analytics and Targeting Solution for US• Started with an in‐depth analysis of Life Line’s historical campaign  data and quantified the impact of contact history on campaign  performance• Learnings from the analysis were used to develop a segmented modeling strategy based on prior contact history that drove the  selection of best prospect names• A new targeting methodology was developed and tested against  the current compiled data vendors in a head to head test• Segmented modeling solution increased response rate by 38%  and generated 62K incremental customers given the same mailing  quantity 38 Analytics Solution Framework STEP 2 – DEVELOP A  STEP 1 – PERFORM CONTACT  PREDICTIVE MODELING  HISTORY ANALYSIS SYSTEM Base Universe Selection Model Universal M odel #3 Segmented Segmented Model #1 Model #2 Global STEP 3 – DEVELOP  Optimal Solution OPTIMIZATION  Local Maximum Local ALGORITHM TO  Maximum MAXIMIZE DIRECT  MAIL CAMPAIGN  PERFORMANCE 39 13
  14. 14. 9/30/2011 Targeting Evolution – Gen3.0• LLS models continue to be redeveloped to keep current and the  approach  refined to gain incremental lift.• Gen3.0 segments out prior contacts from non‐prior and also  urbanicity.  Promotion history as a predictor is removed and  used outside of the model to remove bias that comes from  having it in the model.• In head to head testing Gen3.0 is winning over Gen2.0 in 5 out  of 7 campaigns and driving an incremental 6% improvement on  average over an already strong Gen2.0 model. Modeling Approach Gen1.0 – Gen3.0 40 UK Predictive Modeling Solution • We developed a Modeling System consisting of multiple  Customer Clone and Response Models to support Life  Line’s UK business • Detailed analysis of the promotion history revealed that  two separate response models were needed (Prior and No  Prior) given the large performance differences between  the two contact strategy segments • All of the models performed well and will provide a steady  stream of high performing target prospects going forward 41UK Modeling and Selection Leveraging the learnings from the US: UK Models 1. A customer clone model is used to  eliminate 50‐75 year olds who do  National Canvas 50‐75 yr olds not look like current Life Line  customer customers Customer Clone Model 2. Prospects are then separated  between those who received an  offer from Life Line in the past 12  Priors  No‐Priors  months vs. those who did not Response  Response  Model Model 3. Segment‐specific response models  are used to improve identification  of prospects with prior and no  Optimization Algorithm To  prior contacts Combine The Predictive Models 42 14
  15. 15. 9/30/2011 UK Segmented Model – Summary • Modeling process identified the characteristics among each  segment that best defined the responders • Predictors of response for households without prior contact: • Have a shorter length of residence • Pay higher property tax • Shorter distance to the screening location • Reside in areas of higher concentration of existing Life Line UK customers • Predictors of response for households with prior contact: • Number of individual promotions received over previous 12 months (the fewer the better) • Reside in an area where others have responded to a past campaign • Households that place orders by mail and the amount of the order • Donate to charity • Have a shorter length of residence 43 UK Results UK Results• Prospects identified through the Segmented Models yielded up to 62%  improvement in performance relative to campaign average• Merkle and Life Line Teams are working on the next generation segmented  models to further increase the response performance 44 Trish Mathe Ozgur Dogan 15