Life Line Global is a leading provider of preventative health screenings that screens over 1 million people annually. They were looking to rapidly grow their customer base in the US and globally. Merkle developed an analytical data sourcing and optimization solution for Life Line to improve targeting and performance. For the US, Merkle analyzed contact history, developed predictive models, and tested new targeting methods, increasing response rates by 38%. For the UK expansion, Merkle built separate predictive models for those with and without prior contact, yielding up to 62% improved performance. Merkle continues working with Life Line to further optimize targeting globally through advanced analytical solutions.

1. October 5th, 2011Driving Results through Strategic Data Sourcing and Optimization: Life Line Global Case StudyTrish Mathe – Vice President of Database Marketing, Life Line ScreeningOzgur Dogan – General Manager, Data Solutions Group, Merkle

2. Presenter BackgroundsTrish MatheVice President of Database Marketing at Life Line Screening

3. Over 10 years of database marketing experience both in financial services and healthcare industries

4. Areas of expertise include: building and maintaining marketing infrastructure and automation, prospect and customer database management, campaign management and measurement

5. Experienced in marketing to the fifty plus crowd, healthcare professionals, and several other specialty market segmentsOzgur DoganGeneral Manager of Data Solutions Group at Merkle

6. Oversees the delivery of analytical data sourcing and optimization solutions for Merkle’s clients across all industry verticals

7. Spent 7 years at Merkle and has 15 years of industry experience in building, implementing and integrating database marketing solutions

8. Technical MBA Degree from the University of Georgia2

9. Session OverviewEvolution in the CRM Data LandscapeDeveloping a quantitative framework to assess value of dataFuture Trends and Innovation OpportunitiesLife Line Data Sourcing & Optimization Case Study3

10. Evolution of the Marketing Landscape

11. Global Market TrendsFundamental changes in the consumer decision making and buying processAdvancing and evolving technology useExpanding fragmentation – media and channelsData explosion driven by emergence of digital mediaClutter and confusion in the data landscapeIncreased Accountability and MeasurementUltimately, these influencers are changing the way marketers will create competitive advantage in the future.5

12. Consumers are More Connected Today than EverBlogSearchEmail27% actively read blogs86%87%87% use email 1+ times per day86% use search frequently27%SocialDisplay63% use Facebook weekly20% click on banner ads63%MobileIM51%20%33%33% use IM regularly51% are active texters6

13. Database Marketing Landscape is EvolvingDbM 1.0DbM 2.0Direct/Identified ModelNew Entrants DomesticUS and International SolutionsSingle Campaign/ Media TargetingIntegrated Media OptimizationCost PressureIncreased Cost PressureOffline focusDigitalizationKey Trends7

14. 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

15. Major Factors Driving OpportunityEmergenceChallengesObjectivesSolutionNew Channels& MediaCostPressuresImproveROICustomerCentricityIncreased ComplexityFocus onThe CustomerAnalyticData Sourcing& OptimizationAccountability&MeasurementIntegratedApproachIncreasedMessageVolumeTechnology99

16. Business Impact of Analytical Data SourcingLeading direct marketer saved $2 MM in list sourcing cost in it first four 4 months through analytical data sourcing optimization without negatively impacting response2010 Costs 2011 Costs Savings10

17. CRM Data Landscape

18. CRM Data Provider Landscape12

19. Common Data Types and ConstraintsType of DataExamplesCommon ConstraintsCompiled & Aggregated DataExperian INSOURCE, Epsilon TotalSource, Data SourceCan only afford one source

20. It is difficult to determine unique value so only purchase single sourceSyndicated ResearchMRI, ScarboroughUnable to implement beyond basic messaging and product designVertical ListsNew parents, magazine subscribersToo many choices on the market, hard to evaluate

21. Selection limited to a small number of data card attributes13

22. Analytical Data Sourcing and Optimization14

23. How to Assess the Value of DataFrameworkPredictive PowerDescriptive Power Composite ScoreSource QualityUniverse CoverageKey Dimensions for Evaluation:Predictive Power: Does the source add incremental lift to my predictions?

24. Descriptive Power: Does the new source provide the ability to better segment my target audience or lend new insights?

25. Universe Coverage: Does the source provide access to new and unique prospects (or overlay to existing customers)?

26. Source Quality: Does the source provide accurate and high quality data? 15

27. Data Optimization Lab16

28. Evaluating Value of Data Sources - ExampleKey Dimensions for EvaluationPredictive PowerDescriptive Power ExampleComposite ScoreSource QualityUniverse Coverage17

29. Analytical Data Sourcing & OptimizationAnalytical Data SourcingTraditional Data SourcingIncentiveIncented to increase list performance and reduce list costsIncented to increase listvolumeFully aligned with Client’s cost efficiency and growth goalsNot fully aligned with Client’s business goalsAlignmentAnalytically Driven OptimizationApproachRecommendations driven byExperience and RelationshipRecommendationsTeamDedicated Team focused on Driving performanceDriven to increase commissionsAnalyticsWorld Class Analytics Team with dataoptimization experienceNo real analytics or science 18

30. List Optimization DynamicsThe purpose of the list optimization process is to balance cost and value19

31. Analytic Approach to List Universe OptimizationExisting Universe ListsFuture Universe ListsList 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 define the right mix and number of lists that maximize ROIROIN lists# of Lists20

32. Optimized Source Mix IllustrationThe ratio of the Base File names increases in the optimized source mix scenario21

33. Optimization Performed At Multiple LevelsLEVEL 1 LEVEL 2LEVEL 3Today’s Focus22

34. Vertical DataCompiled DataCredit DataPartner DataCustomer DataLife Event TriggersOptimization Lab – Data Sourcing and Integration ProcessData SourcingSourceOptimizationSourceIntegrationSource EffectivenessSourceOptimizationDerived Data DevelopmentCampaign 1PerformanceOptimizationCampaignOptimizationEnhanced messaging & segmentationAudienceOptimizationDefinedUniverseCampaign 2Campaign ROISource EffectivenessCampaign 3Deploy Campaign Level AnalyticsCreate the best Marketable UniverseSource Evaluation23

35. Trends and Innovation Opportunities

36. Data Sourcing and Optimization As Enabler of Customer CentricityEffective 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.

37. Understanding the optimal mix of data, both third party and customer enables optimal analytics.

38. 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

39. Data Sourcing As Strategic EngagementPhase I - Evaluation(Months 0 – 3)Phase 2 - Implementation(Months 3+)Establish KPI’sList OptimizationIllustrativeSimulation/Optimization on Historical CampaignsRefine Optimization ModelsEvaluation of New Compiled & Vertical SourcesEarly HarvestExecute Test CampaignEliminate list sources with high duplication ratesDevelop list optimization toolRolloutOptimized list sourcing for Highlights (incl. brokerage services) Strategic data research and analysis 262626

40. List Optimization Engine Automates the Process27

41. Economic and Environmental Data IntegrationEconomic and Environmental DataExamplesNew house starts and vacancy rates

42. Unemployment rate and per capita personal income

43. Consumer pricing and sentiment index

44. Precipitation and temperature data

45. Disaster areasBusiness ImpactBetter targeting of products and services that are sensitive to environmental factors

46. More predictive media mix optimization and allocation models

47. Ability to explain performance changes due to environmental factors28

48. Digital Data Innovation and Integration29

49. Key Take AwaysCRM data landscape is changing rapidly due to digital media emergency and data explosionInnovative optimization approach delivers ROI by reducing data costs and increasing marketing performanceIt’s important to cut through the clutter and identify the most valuable data assets in the market place including newly emerging sources like digitalIntegrating analytics expertise with data market knowledge is necessary to gain access to best and most comprehensive marketable universe30

50. Data Sourcing & Optimization Case Study

51. Life Line Screening OverviewLeading provider of community-based preventive health screenings and employs approximately 1000 employees in the U.S. and abroad

52. 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

53. Since their inception in 1993, Life Line has screened over 6million people, and currently screens 1 million people each year at 20,000 screening events globally3232

54. Screening Process: Participant’s Experience“Results Letter” mailed within 3 weeks.

55. Advised to share with physician for appropriate follow-up.

56. If anything critical participant is provided a “Doctor’s Review Kit” immediately and advised to go to a physician or emergency room within 24 hours.Participant Screened At Local Venue: Church, Club, Community CenterScreening ScheduledResults are reviewed by a board certified physician 33

57. Life Line’s Global Expansion Strategy34

58. Life Line Projected Global Presence35

59. Life Line Business ChallengeInterested in rapidly growing the customer base in US and across the globe

60. Using multiple compiled lists provides support to the large-scale Direct Mail acquisition program

61. Limited universe and heavy mailing volume causing contact fatigue

62. Applying the learnings generated in US to support the global expansion strategy with UK as the first pilot market36

63. CRM Solution RoadmapHighTargetingInsightProgram DevelopmentMeasurementSource Incremental P&L and HierarchyIntegration of Promotion History Prospect Segmentation“Silo” SourcesMarcom Contact Strategy per SegmentProspect and Customer level InsightsBrief knowledge on the 50-75 years old target population ImpactLTV & Profitability Tracking @ The Customer LevelIntegration of SourcesMulti-Source Interaction Campaign ApproachCreative & Source TestingSingle level source campaign level measurementPhase IPhase IIPhase IIILowHighProgram Sophistication37

64. Analytics and Targeting Solution for USStarted with an in-depth analysis of Life Line’s historical campaign data and quantified the impact of contact history on campaign performance

65. 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

66. A new targeting methodology was developed and tested against the current compiled data vendors in a head to head test

67. Segmented modeling solution increased response rate by 38% and generated 62K incremental customers given the same mailing quantity38

68. Analytics Solution Framework STEP 1 – PERFORM CONTACT HISTORY ANALYSIS STEP 2 – DEVELOP A PREDICTIVE MODELING SYSTEM STEP 3 – DEVELOP OPTIMIZATION ALGORITHM TO MAXIMIZE DIRECT MAIL CAMPAIGN PERFORMANCE39

69. Targeting Evolution – Gen3.0LLS models continue to be redeveloped to keep current and the approach refined to gain incremental lift.

70. 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.

71. 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 ApproachGen1.0 – Gen3.040

72. UK Predictive Modeling SolutionWe developed a Modeling System consisting of multiple Customer Clone and Response Models to support Life Line’s UK business

73. 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

74. All of the models performed well and will provide a steady stream of high performing target prospects going forward41

75. UK Modeling and SelectionLeveraging the learning's from the US:A customer clone model is used to eliminate 50-75 year olds who do not look like current Life Line customer customersProspects are then separated between those who received an offer from Life Line in the past 12 months vs. those who did notSegment-specific response models are used to improve identification of prospects with prior and no prior contactsUK ModelsNational Canvas50-75 yr oldsCustomer Clone ModelPriors Response ModelNo-Priors Response ModelOptimization Algorithm To Combine The Predictive Models42

76. UK Segmented Model – SummaryModeling process identified the characteristics among each segment that best defined the responders

77. Predictors of response for households without prior contact:

78. Have a shorter length of residence

79. Pay higher property tax

80. Shorter distance to the screening location

81. Reside in areas of higher concentration of existing Life Line UK customers

82. Predictors of response for households with prior contact:

83. Number of individual promotions received over previous 12 months(the fewer the better)

84. Reside in an area where others have responded to a past campaign

85. Households that place orders by mail and the amount of the order

86. Donate to charity

87. Have a shorter length of residence43

88. UK ResultsUK ResultsProspects identified through the Segmented Models yielded up to 62% improvement in performance relative to campaign average

89. Merkle and Life Line Teams are working on the next generation segmented models to further increase the response performance44

90. Trish Mathetmathe@llsa.comOzgur Doganodogan@merkleinc.com