1. 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

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

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 Overview Evolution in the CRM Data Landscape Developing a quantitative framework to assess value of data Future Trends and Innovation Opportunities Life Line Data Sourcing & Optimization Case Study 3

10. Evolution of the Marketing Landscape

11. 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

12. Consumers are More Connected Today than Ever Blog Search Email 27% actively read blogs 86% 87% 87% use email 1+ times per day 86% use search frequently 27% Social Display 63% use Facebook weekly 20% click on banner ads 63% Mobile IM 51% 20% 33% 33% use IM regularly 51% are active texters 6

13. Database Marketing Landscape is Evolving DbM 1.0 DbM 2.0 Direct/Identified Model New Entrants Domestic US and International Solutions Single Campaign/ Media Targeting Integrated Media Optimization Cost Pressure Increased Cost Pressure Offline focus Digitalization Key Trends 7

14. Data Explosion! Today, the codified information base of the world is believed to double every 11 hours 15 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 Opportunity Emergence Challenges Objectives Solution New Channels & Media Cost Pressures Improve ROI Customer Centricity Increased Complexity Focus on The Customer Analytic Data Sourcing & Optimization Accountability & Measurement Integrated Approach Increased Message Volume Technology 9 9

16. 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 2010 Costs 2011 Costs Savings 10

17. CRM Data Landscape

18. CRM Data Provider Landscape 12

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

22. Analytical Data Sourcing and Optimization 14

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 Lab 16

28. Evaluating Value of Data Sources - Example Key Dimensions for Evaluation Predictive Power Descriptive Power Example Composite Score Source Quality Universe Coverage 17

29. Analytical Data Sourcing & Optimization Analytical Data Sourcing Traditional Data Sourcing Incentive Incented to increase list performance and reduce list costs Incented to increase list volume Fully aligned with Client’s cost efficiency and growth goals Not fully aligned with Client’s business goals Alignment Analytically Driven Optimization Approach Recommendations driven by Experience and Relationship Recommendations Team Dedicated Team focused on Driving performance Driven to increase commissions Analytics World Class Analytics Team with dataoptimization experience No real analytics or science 18

30. List Optimization Dynamics The purpose of the list optimization process is to balance cost and value 19

31. 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” lists Merkle’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 ROI ROI N lists # of Lists 20

32. Optimized Source Mix Illustration The ratio of the Base File names increases in the optimized source mix scenario 21

33. Optimization Performed At Multiple Levels LEVEL 1 LEVEL 2 LEVEL 3 Today’s Focus 22

34. Vertical Data Compiled Data Credit Data Partner Data Customer Data Life Event Triggers Optimization Lab – Data Sourcing and Integration Process Data Sourcing Source Optimization Source Integration Source Effectiveness SourceOptimization Derived Data Development Campaign 1 Performance Optimization Campaign Optimization Enhanced messaging & segmentation Audience Optimization Defined Universe Campaign 2 Campaign ROI Source Effectiveness Campaign 3 Deploy Campaign Level Analytics Create the best Marketable Universe Source Evaluation 23

35. Trends and Innovation Opportunities

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 Engagement Phase I - Evaluation (Months 0 – 3) Phase 2 - Implementation (Months 3+) Establish KPI’s List Optimization Illustrative Simulation/Optimization on Historical Campaigns Refine Optimization Models Evaluation of New Compiled & Vertical Sources Early Harvest Execute Test Campaign Eliminate list sources with high duplication rates Develop list optimization tool Rollout Optimized list sourcing for Highlights (incl. brokerage services) Strategic data research and analysis 26 26 26

40. List Optimization Engine Automates the Process 27

42. Unemployment rate and per capita personal income

43. Consumer pricing and sentiment index

44. Precipitation and temperature data

46. More predictive media mix optimization and allocation models

47. Ability to explain performance changes due to environmental factors28

48. Digital Data Innovation and Integration 29

49. 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

50. Data Sourcing & Optimization Case Study

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 globally32 32

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 Center Screening Scheduled Results are reviewed by a board certified physician 33

57. Life Line’s Global Expansion Strategy 34

58. Life Line Projected Global Presence 35

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 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 Segment Prospect and Customer level Insights Brief knowledge on the 50-75 years old target population Impact LTV & Profitability Tracking @ The Customer Level Integration of Sources Multi-Source Interaction Campaign Approach Creative & Source Testing Single level source campaign level measurement Phase I Phase II Phase III Low High Program Sophistication 37

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 PERFORMANCE 39

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 Approach Gen1.0 – Gen3.0 40

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 Selection Leveraging 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 customers Prospects are then separated between those who received an offer from Life Line in the past 12 months vs. those who did not Segment-specific response models are used to improve identification of prospects with prior and no prior contacts UK Models National Canvas 50-75 yr olds Customer Clone Model Priors Response Model No-Priors Response Model Optimization Algorithm To Combine The Predictive Models 42

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

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

90. Trish Mathe tmathe@llsa.com Ozgur Doganodogan@merkleinc.com