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Life is Never Random … How to Make the Most of Your Data Strategy

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Life is Never Random … How to Make the Most of Your Data Strategy

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Presentation from this year's Bridge Conference that covers how nonprofit marketers can make the most of their data strategy to drive donor acquisitions.

Presentation from this year's Bridge Conference that covers how nonprofit marketers can make the most of their data strategy to drive donor acquisitions.

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Life is Never Random … How to Make the Most of Your Data Strategy

  1. 1. #BRIDGE17 LIFE IS NEVER RANDOM … HOW TO MAKE THE MOST OF YOUR DATA STRATEGY DENIS MCSWEENEY: AARP- DIRECTOR, DIRECT MAIL CHANNEL MARYANN BUONCRISTIANO: MERKLE- VP DATA SOLUTIONS JENNIFER HONADEL: EPSILON- MANAGING DIRECTOR
  2. 2. #BRIDGE17 Elements of a data strategy How to stay ahead of the changes Key elements to success Learning Objectives #BRIDGE17
  3. 3. #BRIDGE17 Elements of Data Strategy Solid Strategy will be Aligned with Marketer’s Business Objectives and Budget Long term value New donors/members Average Gift/Spend Channel preference Mailing efficiencies Messaging Creative/ Offer #BRIDGE17#BRIDGE17 Key Components
  4. 4. #BRIDGE17 Optimizing Data Strategy There are proven methodologies that we can employ to help organizations improve their data sourcing strategy to positively impact results: • Utilizing data for multichannel people based marketing • Leveraging analytics to drive data evaluations • Enhancing data sourcing pre-campaign • Improving data performance through predictive analytics #BRIDGE17
  5. 5. #BRIDGE17 All-Channel Planning, Activation & Measurement Personally Identifiable Information (PII) Direct Digital Broadcast All-Channel Data for People-Based Marketing Read the blogpost about the conference at merkleinc.com
  6. 6. Data Evaluation Process to Drive Performance #BRIDGE17 Coverage Maximize unique reach and avoid duplication across data providers Descriptive power Quantify descriptive power of data sets based on granularity of segmentation Predictive power Benchmark predictive power of data in live client models Accuracy Identify the most accurate data based on consensus models and distribution analysis Cost Optimize cost by minimizing duplication across data providers Read the blogpost about the conference at merkleinc.com
  7. 7. #BRIDGE17 Enhancing Data Sourcing Pre-Campaign Leverage Historic Information to: • Reduce list sourcing costs (Typical Reduction Range = 20%-50% reduction in list costs per campaign) • Maintain/Improve Campaign Performance • No impact to current campaign processing 7 AARP historical list sourcing AARP current list sourcing 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
  8. 8. #BRIDGE17 Enhancing Data Sourcing Pre-Campaign 8 Response is assigned to each of the lists on which the individual exists Response is randomly assigned to a single list, typically the list that got paid. Remaining lists do not get the credit hence resulting in incomplete attribution Un-biased (appeared-on) response attribution Traditional response attribution Response attribution analysis: List 1 List 2 List 3 List 4 List 3 List 1 List 2 List 3 List 4 List 1 List 2 List 3 List 4
  9. 9. #BRIDGE17 • List Cost Per Piece - reduced the overall LCPP significantly over the last 5 years through removal of higher cost, high overlap rentals and ongoing price negotiations. • Annual LCPP is over 60%+ lower than prior to this methodology. $0.0256 $0.0238 $0.0161 $0.0135 $0.0120 $0.0093 $0.0098 $- $0.0050 $0.0100 $0.0150 $0.0200 $0.0250 $0.0300 1/11-5/11 6/11-12/11 2012 2013 2014 2015 2016 LCPP Success AARP has Achieved Read the blogpost about the conference at merkleinc.com
  10. 10. #BRIDGE17 AARP: Data Strategy Challenge • Nonprofit, nonpartisan, social welfare organization • Mission: Enhance quality of life for all as we age – not just AARP members • Membership: 38 million • Target audience: age 50+ Gen X 1965-1984 (ages 50-52) #BRIDGE17 Boomers 1946 -1964 (ages 53-71) Silent Gen 1925 -1945 (ages 72+)
  11. 11. Data Strategy Challenge: Part 1 54 years old Different needs, interests, concerns 76 years old #BRIDGE17 Read the blogpost about the conference at merkleinc.com
  12. 12. #BRIDGE17 Data Strategy Challenge: Part 1 • Acquisition Mail’s response rate is highest among prospects turning 50: 'pent-up' demand’. • The 50-59 age group is strategically important (and large), but does not view AARP as relevant to their lives. 188 81 110 117 94 3.4% 41.6% 34.5% 13.0% 7.5% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 0 20 40 60 80 100 120 140 160 180 200 49 50-59 60-69 70-79 80+ AARP Response Rate Index by Age Share of Mail Quantity by Age Read the blogpost about the conference at merkleinc.com
  13. 13. #BRIDGE17 Data Strategy Challenge: Part 1 How can AARP be more relevant to the 50-59 age group? • Special messaging for prospects turning ‘the big five-0’. • Provide the option to respond online via a coupon code. • Different copy (skip Medicare supplemental insurance). • Premiums (for joining) that skew younger… like a Bluetooth speaker. Read the blogpost about the conference at merkleinc.com
  14. 14. #BRIDGE17 Data Strategy Challenge: Part 1 Coupon code audience: • Ages 50-69 with $40k+ HH income. • Tested among a broad age range, and then used analytics to identify the ‘optimal’ segment. • Optimal = Maximizing online’s share of responses without lowering overall response. Read the blogpost about the conference at merkleinc.com
  15. 15. #BRIDGE17 Data Strategy Challenge: Part 2 The quest for the ‘holy grail’: • Goal: Segment the prospect universe based on propensity to respond (transact) online • Step 1: Test the use of an Epsilon TotalSource Plus variable, Channel Preference Ratio – Online • Postcards vs. letter packages • Higher vs. lower online channel preference • Step 2: To be decided… #BRIDGE17
  16. 16. #BRIDGE17 Data Strategy Challenge: Part 3 Multicultural: • Hispanic and AA/B segments are an important part of each Acquisition Mail campaign. • Prospects are classified as Hispanic or AA/B based on an internal model (data variables, Census, zip/last name) and/or list owner classification. Key questions: Are there sub-segments that will respond better to differentiated messaging? Can these segments be modeled using variables on the prospect database?
  17. 17. #BRIDGE17 Utilize analytics to determine the optimal list mix for each campaign. (List rental can get out of control: AARP was paying more than 2x what it should have been!) Utilize modeling to rank and select names for mailing. Update the model annually. Mail random samples of names in each campaign to enable update of the model and measurement of model performance. Target special offers based on promotion history and data variables (e.g., month of birth for a birthday offer). Data Strategy: Best Practices 1 2 3 4
  18. 18. #BRIDGE17 Data Strategy as Growth Engine Data Assets Matched to AARP Analytics Isolate target audience Insights - Strategy Understand wants, needs concerns Creative & Messaging Align to audience Technology Ensure accuracy and consistency Delivery Data-driven inputs Multi-channel decision Reach Activation & Performance Reach audience in all channels Data and Insights Drive the Organization Read the blogpost about the conference at merkleinc.com
  19. 19. #BRIDGE17 19 Isolate and Profile the Target Audience Gift Size/Membership Term 1 Lifetime Value 2 Season4 5 New Donors/Members Channel 3 Match & profile Survey Machine learning Read the blogpost about the conference at merkleinc.com
  20. 20. #BRIDGE17 Know Them Better Predictive modeling/segmentation Attitudinal Data Why you join • Relationship to cause/org • Engagement Demographic Data Who you are • Demographics and Financials • Lifestyles and hobbies • Digital activity • Media consumption Purchase Data What you buy • Consumer transaction data across brands / categories • All channels • Charitable categories • Size of gift • Frequency of giving • Ratio giving to spending Donation/Member Data What you give
  21. 21. #BRIDGE17 Reach in All Channels • Direct Mail • Email • Online • Social • Mobile • Television Read the blogpost about the conference at merkleinc.com
  22. 22. #BRIDGE17#BRIDGE17#BRIDGE17 Its Smart to Use the Same Data Across All Channels Suppose you need income information for online targeting Multi-sourced profile data “12 different offline sources agree Household Income is $100- 120k. User has checking account and a value score of A2” Online behavioral data “Visited Forbes.com, where average visitor has income of $180k” IP/ Geographic data “Uses an IP address that corresponds to a DMA where average income is $70k” Read the blogpost about the conference at merkleinc.com
  23. 23. #BRIDGE17 Read about the conference at merkleinc.com! Thank You Mary Ann Buoncristiano – mbuoncristiano@merkleinc.com Denis McSweeney – dmcsweeney@AARP.org Jennifer Honadel – jennifer.Honadel@epsilon.com

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