Data Driven Strategies

1. Data Driven Strategies Integrating Quantitative & Qualitative

2. Objectives • Share some new marketing strategies that have been successful within the Financial Services arena in 2007. • Get you thinking about breaking down traditional barriers that have existed within our space. • Explore three examples from 2007: • Advancements in ITA marketing • Segmentation Development and Execution • Qualitative and Quantitative Integration • Take learnings and apply to your business.

3. Why Merkle? • Merkle works with over 50 different financial services clients around driving a better marketing result • Our passion is to continue to push the DBM envelope and find new areas that can change the marketing landscape • Merkle spends over a $1 MM in R&D around DBM best practices and thought leadership • We will share with you two emerging opportunities that we believe can drive big gains in your marketing results!

4. Breaking through Barriers to Growth • Mature organizations face barriers to sustained revenue growth. • They key is to understand how to respond to these challenges. • Innovation: • New Audiences • New Channels • New Offers. Revenue Doing “more of the same” and playing with incremental wins to maintain.

5. Biggest Opportunities in DBM • Marketing Measurement • Media Mix Optimization • Enterprise Segmentation • Brand Engagement Measurement • Data Sourcing, Evaluation and Integration • “Whole Brain” Marketing • Integrating Quantitative Planning with Creative Execution

6. “Whole Brain” Marketing • Lots of press and emphasis around “Left Brain” Marketing • Forrester, “What Sticks”, “Competing on Analytics”, etc. • Less talk about the power of research and consumer insights - the “why” • Our belief is that it is not one or the other…it is both working together - “Whole Brain” Marketing! • Traditionally, marketers have approached, managed and hired these two competencies separate from one another • This has led to under-optimized marketing performance

7. Current Landscape In general, we have observed four different levels integrating the right and left brain capabilities. Level 4 “One Agency” Both capabilities exist in one team The team works daily with each other Neither capability Dominates Marketing strategy is in a unified fashion Accountability and the Big Idea working together! Level 3 “Virtual Agency” Both capabilities exist through separate groups Groups come together periodically to share info Strategies are debated and decided upon in this virtual agency forum “Turf war” tends to exist • Level 2 • “Agency and DBM” • Both capabilities exist • through separate groups • Groups work independent • from one another • Both offer up strategies • with marketer left to • determine best approach • Level 1 • “Right Brain Only” • Creative and Big Idea • people lead marketing • Little quantitative skills • Experience and intuition • are key drivers

8. Research and Analytics • Data Analytics (often called Quantitative) and Primary Research (often called Qualitative) are typically disjointed from each other. • Example – DBM company and Market Research Company. Very rarely do you go to one company to get these. • Yet both are dependent on each other, both tell a story and both are data driven. • Separating them can and will cause a loss of knowledge and create inferior results

9. Research & Analytics DESCRIPTIVE Research Focuses on the “why” and the “how” of audience behavior – identifying and interpreting the meaning behind consumer actions, decisions, beliefs and values; and understanding their decision making process Qualitative Insights PREDICTIVE Analytics Focuses on the “who’ and “what” components of direct marketing – utilizing data, facts, information and knowledge to identify and create statistically valid measures that drive business decisions Quantitative Approach

10. The Combined Benefits • Accurately identifying customer and prospect universes and segments • Understanding purchase influencers • Gauging reaction to new concepts/products • Identifying key loyalty and attrition factors • Optimizing channel expenditures • Increasing the ability to predict campaign performance • Moving beyond “Beat the Control” in creative • Tangible and actionable insights to improve your value proposition. • Ultimately...improved ROI Qualitative Insights Quantitative Approach

11. Process Overview Creative & Production Analytics & Test Design Behavioral & Attitudinal Data Applicable Data Quantitative Models Objectives & Strategy Qualitative Insights Content Solutions Why? What? Who? How? Combined approach answers the root questions

12. “Whole Brain” Marketing & Its Impact on ITA Marketing

13. ITA Marketing • Over the years, ITA marketing (especially within the credit card space) has struggled to find a firm spot within the marketing plan • Major advances in technology, data and analytics have the ability to boost the performance of ITA programs • We are observing that many credit cards companies are not capitalizing on this and are running sub-optimal ITA programs

14. The Credit “Crutch” • Many companies have become too reliant on credit data! • Past ITA performance poor • Data, technology, analytics, creative and campaigns are built around pre-approved programs • Difficult to change • Assumed to minimized risk and loss • Hard to get commitment from Sr. Management

15. Disadvantages of Credit Data • Many disadvantages exist around utilizing credit data in 2007 • Increasing Legislation and privacy • Expensive • Limited audience • Diluting offers and messaging to be conservative

16. ITA - Yesteryear & Today ITA Today Many Major Advancements: Better Data - Predictive Advanced Analytics Segmentation Data Lab vs. Vertical Lists New Channels ITA Yesteryear Faster Learning Cycles

17. Making ITA Work - Approach • Create an integrated ITA Marketing Team • Get access to External Data • Make use of powerful Internal Data • Segment the Audience • Powerful analytics • Insight • Offer, messaging and creative testing • Measure, Learn and Improve

18. Integrated ITA Team • Optimizing your ITA program requires a team of highly skilled people working together on a daily basis • Requires both right and left brain marketers • Marketing ITA leader • Analytics lead • Data Expert • Insight specialist • Creative Lead • Program manager • Campaign manager

19. External ITA Data • Data is extremely important in building a high performing ITA program! • In the last several years, the availability of external third party data has increased dramatically: • Summarized Credit Data • Auto Data • Mortgage, Homeowner Data • Transactional Co-op Data • Demographic Data • Wealth Data and Models • Lifestyle Data • Life Event Data, Etc.

20. ITA Data Matrix Summarized Credit Statistics High Auto Data Mortgage Data Universe Demographic Data Real Estate Data Wealth Indicators ITA Impact low high Econometrics Transactional Lifestyle Data Segmentation Data Life Event Data Low Vertical Data

21. Internal ITA Data • This is data that is available within your existing infrastructure • Some of the most descriptive and predictive data come from your “internal” data: • Promotional history • Summarized cardholder data • Response history • Derived Data • Etc.

22. Derived Variable – SPENDING VELOCITY A modeled field estimating how frequently a household is likely to spend over a period of time. Higher values indicate high spending velocity. Value and Ranges:1 to 20 • TOP VARIABLES • Avg loan amount & all open revolving trades • Avg Spending Velocity Index (CR level) • HOH age & Household size & Car owner • LM Finance Card Index • Avg outstanding balance on all open bank card trades • Avg number of revolving trades 30 or more days delinquent or derogatory • Length of residence • Percent of profile consisting of open retail accounts ITA Impact Score HIGH LOW

23. Derived Variable – INFERRED CARDHOLDER A modeled field that estimates the probability of the household having bank and/or credit cards. The higher the ranking, the more likely it is that the household has a credit card. Value and Ranges: 1-10 • TOP VARIABLES • Marital status – married • Number of sources • Merkle Donor Rating • Avg Auto1 open date • Percent population inside urbanized area • Polk flag • Merkle Wealth Rating • Age range in HH • Responders (Group Variables) • Avg student high credit ITA Impact Score HIGH LOW

24. Prospect Segmentation Experian I III II ITA Universe Credit Universe Equifax TU Segmented Targeting Opportunities Segment I – Universe Expansion Segment II – Leverage non-credit data to optimize the performance Segment III – Maximize the Pre-screen Program

25. “Audience to Offer” Expands Universe Climbers Urban Singles Gen Y Difficult Underserved “Seekers” Market Penetration Index Poor Credit Easy Low $ High $ Affluent Families

26. Behavioral Segmentation Descriptive Profiling Characteristics ITA Segments Spending Habits Lifestyle Travel • Cash transactions • Credit transactions • Lifetime Average Spend • Lifetime Transactions • Lifetime Quantity per Transaction • Trailing 12 mo Spend • Trailing 12 mo Transactions • Cars • Electronics • Home & Garden • Babies & Kids • Apparel & Jewelry • Health • Sports & Outdoors • Entertainment • Foreign Travel • Domestic Travel • Premier Foreign Travel • Premier Domestic Travel Gen Y Climbers Seekers Urban Singles Affluent Families Poor Credit

27. AUDIENCE OFFER 20 to 1 10 to 1 ITA Success = “Audience to Offer” vs. traditional “Offer to Audience” “Whole Brain” Marketing Profit Drivers CREATIVE CONTACT MEDIA CHANNEL MESSAGE Database Marketing Profit Drivers 6 to 1 5 to 1 3 to 1 3 to 1 2 to 1 Marketing Focus Changes from YOU to THEM (Audience) Expands Prospect Universe Maximizes and Optimizes Revenue Establishes More Relevancy

28. CREATIVE CONTACT MEDIA CHANNEL MESSAGE 6 to 1 5 to 1 3 to 1 3 to 1 2 to 1 Building & Executing the Plan “Whole Brain” Marketing Profit Drivers AUDIENCE OFFER Database Marketing Profit Drivers 20 to 1 10 to 1 • Need to build communication strategic roadmap that optimizes the: • Media (Channel) preference, • Frequency, • Channel of interaction, • Messaging (value proposition) and finally…… • The creative that brings all of the above to life!

29. Strategy vs. Creative • Marketing campaigns conceived by blue-sky ‘creative’ thinking are 4Xmore likely to fail than succeed. • Campaigns based on insights are 15Xmore likely to succeed than fail.” A statistic quoted by Michael Moon at a DMA Symposium in Amsterdam

30. The Proof: Background • Client was 100% Credit Data • Losing share and needed universe expansion • Tired creative with “vanilla” offerings Result • Increased marketable universe by over 25% via new data, • Lower Cost, Greater Response, Same Risk via segmentation scheme, modeling • New Segment-specific Offers & Messaging with comprehensive communications plan • Through aggressive testing strategies, developed optimal communication plan for select segments • Program deemed roll-out in 3 Months

31. Enterprise Segmentation Development & Execution

32. Enterprise Segmentation • That market is cluttered with segmentation schemes and solutions • Some are robust and effective but most are vaporware with little impact • Merkle believes that, at the highest levels, segmentation can and should be a strategic asset for a company

33. Segmentation Why Segmentation Disappoints* • Excessive interest in consumer identities rather than focusing on product features that matter most to consumers • Too little emphasis on actual consumer behavior, which reveal attitudes and predict business outcomes • Undue absorption in the technical details of devising the segmentation • Creation of a solution that focuses on insights but fails to address actionability • Lack of vision regarding methods and tactics enabling maximization of the segmentation solution * Source: First three (3) reasons for disappointment provided by the following article: Rediscovering Market Segmentation, by Daniel Yankelovich & David Meer, Harvard Business Review, February 2006

34. Segmentation - Our View • There is no single best approach to segmentation • The right approach is one that will satisfy the overall objectives and leverage the right information • Merkle believes that leveraging research-based, customer and marketable universe data is a best practice • The solution must be both relevant to the marketing executive and actionable to the marketing manager

35. 3 Major Types of Information Research Customer • Primary Research • Secondary Research • Syndicated Panels • Behavioral • Product Mix • Transactions • Usage • Revenue / Value ? • Most directly tiedto customer value • Strongest predictor of future purchase activity • Product mix provides overall depth of relationship • Provides insightsinto consumer needs and intentions • Overall market share and total behaviors (not just with your brand) • Self reported information, typically very simple to understand • Get media consumption and awareness / consideration metrics ? ? Universe • Compiled • Credit • Verticals • Co-ops • Only source available on total marketable universe • Other two buckets are a subset of this universe • Bureau and co-op sources can provide some behavioral insight

36. Option 1: Start with Attitudes • Benefit: Directly gets to customer intentions, needs and attitudes • Approach: • Drive the initial survey and segmentation – guide it to leverage more widely available data and steer the initial segmentation to potentially be more predictable • Build a unique methodology to map to DB and then re-profile and define the segments based on the post mapping procedure Customers Sample InitialResearch AdditionalResearch Map segments to Universe Marketable Universe Prospects Segmentation using Market Research Sample Panel Seg #1 Seg #2 Seg #3 … Seg #n Seg #1 Seg #2 Seg #3 … Seg #n ≠ Build Initial Profiles on Segments Re-Profile and Re-Define Final Segments based on Prospect, Customer and 2nd Research Stage Initial Segments(Sample Only) Final Segments(Marketable Universe)

37. Option 2: Start with Customer Behaviors • Benefit: Assume that in mature markets, companies with decent market share have some penetration into all underlying (unobserved) market segments • Given that, this approach can identify and describe the differences within these segments, especially between prospects and customers • Approach: • Leverage market research, conduct research specific to each segment and drive the descriptions and profiles with that insight information for the presentation layer • Starting with customer behaviors gives us a solid foundation on which to build a mapping process to the marketable universe Marketable Universe Customers Segmentation using Customer Behavioral Data Map Segments from Customers to Universe Seg #1 Seg #2 Seg #3 … Seg #n Seg #1 Seg #2 Seg #3 … Seg #n Build Profiles on Customer Behaviors Build Profiles on Customer & Prospect Data Customer Segments Final Segments Finalize Market Segments based on Learnings from Research – Use this information to “color” segments ConductResearch by Segment

38. Choosing the Approach Determining the right approach to strategic depends on the client and the situation • Consider the company’s business model and competitive landscape • The maturity of the company and the product play a factor – immature markets/company lack data and market share • Determine the primary and overall objectives – if there is a specific objective, perhaps a tactical approach would be better • Consider modifications or customizations to the two solutions presented to best fit the situation (hybrid approaches)

39. Choosing the Approach Mature / MarketLeader Option II Option I Industry / Company Option II Option II Immature/ New Non-Direct Direct toConsumer BusinessModel

40. Segmentation Execution

41. Making the Segments Actionable 2 3 4 5 1 6 Coming of Age Conservatives Savings Hunters Growing Families Upscale Singles Cosmopolitans Prospects % of all US Households 13.3% 14.3% 11.2% 27.7% 15.6% 17.8% % of Client Inquiries (funnel Index) 15.8% (119) 9.3% (65) Inquiries 20.8 (133) 12.4% (111) 29.8% (108) 11.9% (67) Population % of Client Customers (funnel Index) Customers 11.6% (125) 10.3% (83) 31.0% (104) 15.7% (75) 15.6% (99) 15.5% (133) % of Client Cancels (funnel Index) 15.7% (100) 9.2% (79) Cancels 21.5% (137) 13.0% (126) 30.2% (97) 10.3% (66) • 27%<30 yrs; 14%>60yrs • 24% with Bachelors or higher • 5.5 • 39% • 39% married • 21% • 2.9 Years • 17%<30 yrs; 21%>60yrs • 19% with Bachelors or higher • 6.7 • 38% • 50% married • 33% • 4.7 Years • 17%<30 yrs; 15%>60yrs • 29% with Bachelors or higher • 7.7 • 48% • 60% married • 47% • 5.6 Years • 15%<30 yrs; 18%>60yrs • 37% with Bachelors or higher • 7.6 • 38% • 58% married • 41% • 5.0 Years • 6%<30 yrs; 45%>60yrs • 44% with Bachelors or higher • 8.9 • 18% • 62% married • 56% • 7.0 Years • 7%<30 yrs; 40%>60yrs • 37% with Bachelors or higher • 8.4 • 22% • 62% married • 44% • 6.0 Years Age Education Wealth Rating Children in Household Married Homeowner Length of Residence Demographics • Quoted Premium • Premium per Driver • Premium per vehicle • Stated Previous Insurer: • Competitor 1 • Competitor 2 • Competitor 3 • Competitor 4 • Competitor 5 • Competitor 6 Inquiry Profile Premium Premium per Driver Premium per vehicle % New Customers Tenure % Internet Sale % With a new car % with claim Gender Customer Profile

42. Segment 3 The Funnel Framework 27.7% Segment 4 Segment 1 Segment 6 Segment 5 Segment 2 17.8% Entire Marketplace 15.6% 14.3% 13.3% 11.2% • Primary Framework for Clients • The two permanent segments: attitudinal segments (across the funnel & lifecycle (down the funnel) • The Funnel data can be selected based on time (most recent 1, 3, 6, 12 months) and geography (national, state, DMA) • Many different metrics can be displayed within the boxes (population % and indices shown to the left) Aware Engaged 108 133 Inquirer 119 111 67 65 26.1% 26.4% 25.3% 27.8% 25.2% 22.9% 104 Customers 133 75 99 125 83 97 Cancels 137 100 126 66 79

43. Identifying with Your Segments Some basic rules: • Telling them you know things about them with the copy or images does not win you their appreciation or drive results. • Remember creative should always keep an aspirational (younger, wealthier, action packed) vibe. • Listen to your segments and define them based on their needs, attitudes and behaviors. • Tailor your offers (if you can) and/or your value propositions within those offers as your primary means of executing segment specific creative approaches.

44. Implementation Focus 75% The Offer 50% Impact Value Prop/ Offer Message 25% Creative Design & Copy Focus

45. Recent Segment Specific Creatives Based on the new segmentation, Merkle recommended expanded testing to increase messaging strategies and “personalize” the marketing approach Client selected two segments of inquirers to start the testing process: Cosmopolitans Savings Seekers Merkle and Client’s internal creative team would each develop new kits to drive inquirer conversion and beat the longstanding 3 year+ control. The Result: ALL TESTS BEAT THE CONTROL

46. Valuable Messaging Insights Cosmopolitans Demographics: • Oldest Population (66% over 50yrs old) • High income group • Most educated (44% with Bachelors or higher) Media Consumption: • Very likely to read newspapers • Radio formats likely to listen to: news/talk, sports • Radio formats not likely to listen to: urban, CHR • Likely to watch: Documentary, evening news, golf, horse racing, skating, tennis • Not likely to watch: daytime comedies, primetime comedies, daytime drama, game shows, reality shows Insurance and Automotive: • Highest levels of auto insurance coverage Attitudes and Perceptions: • Buy based on quality not price • Loyal to brands they like • Low consumer confidence: feel they will be financially worse off within 12 months • Focused on eating healthy • Feel TV advertising is too loud and repeated too often • Most likely to trust newspaper, Least likely to trust TV • Likely to purchase items by phone • Much more likely to engage in foreign travel

47. Valuable Messaging Insights Savings Hunters Insurance and Automotive: • Want the cheap/easy to maintain vehicle Attitudes and Perceptions: • Tend to make impulse purchases • Will switch brands for small discount • Only save money for specific purposes • Like humorous TV advertising • Get least amount of sleep at night compared to other segments • Most likely to say holding true to religious faith and beliefs is important • Most likely to trust TV, least likely to trust internet Demographics: • Typically 30 – 55 • Low income group • Lowest education level (19% with Bachelors or higher) Media Consumption: • Likely to watch: Auto racing, daytime dramas, reality shows, primetime comedies, early morning news, game shows, primetime films • Not likely to watch: Golf, horse racing, tennis, news specials • Radio formats likely to listen to: country, urban, rock • Radio formats not likely to listen to: news, talk, oldies

48. Quantitative & QualitativeWorking Together

49. Quick Story CFM Direct Had Great Results • AOR of College Savings Product in the 529 Family. • Handled all aspects: Planning, Media, Creative, Production, Analytics • “Knew the product” - had the insights, control and skills. • Identified good stable of Vertical and some Compiled Lists • Had great lead volume from multiple channels: Schools, Pubs, Web & Mail • But…the product was not performing the way they had planned - it was “grow or die”

50. Quick Story CFM Direct Purchased by Merkle • Analytic horsepower and data options expanded exponentially • Knew we needed a more precision based marketing approach - a way to SHOW the client hot to drive results • Assembled a new Client team complete with Research, Creative, Analytics, Data and Search SMEs.