#DM201

1. 10:00 -11:00 AM – Modeling – Lists – Email Match – Coop Speaker: Angela Newsom, Client Services Director, Wiland #DM201

2. What We’ll Cover • How a co-op works • Collection of data • Modeling • Types of models • Scores • Segmentation • What’s trending • Why do co-ops work #DM201

3. What is a Co-op Database? • A cooperative database is the compilation of individual transactions from thousands of participants used to create a bigger picture of the consumer. • Participants must contribute their own data in order to benefit from others’ data (pay to play). • Transactions are summarized at various database levels: • Industry (Non-Profit) • Market (Religion) • Theme (Catholic - Humanitarian Relief, Charities, Schools, etc) #DM201

4. What is a Co-op Database? • Co-ops build predictive models using a variety of statistical techniques and thousands of variables to identify prospects or housefile names whose key transactional and demographic characteristics are most similar to the clients’ direct responders or best donors. #DM201

5. Common Objection to Joining a Co-op • WE DON’T WANT TO SHARE OUR DATA! • Our donors are unique to us • Our donors will be targeted • Our donors will receive additional mail #DM201

6. What is a Co-op Database? Client JKL 9.23.17 $100 Client GHI 5.25.18 $40 Client ABC 1.2.17 $50 Client DEF 5.8.18 $25 Client DEF 8.15.19 $20 Client ABC 4.26.18 $50 New Client 5.15.19 $75 Client DEF 7.5.18 $55 Client PQR 12.10.18 $500 Client ZAB 12.2.18 $60 Client PQR 10.12.17 $75 Client MNO 6.7.17 $100 Client VWX 5.25.18 $35 Client CDE 7.8.18 $85 Client VWX 2.10.18 $115 Client GHI 8.8.18 $200 Client JKL 4.4.19 $65 Client DEF 5.5.17 $25 Client STU 11.25.17 $10 Client MNO 9.3.18 $99 #DM201

7. What is a Co-op Database? Fundraiser’s View of Jane Doe Gave $10 to fundraiser’s DM appeal 13 months ago Gave a total of $30 to fundraiser Gave 3x in 3 years Average gift is $10 Co-op’s View of Jane Doe Made 10 donations in the last 12 months, $2,400 total Gave to animal welfare, child welfare, and disaster relief organizations 60% of donations are DM; 30% online; 10% TM Has active subscriptions to news, fashion, and pet magazines Has made one $1,000+ donation to an animal welfare agency Has 3 children under 18 Spent $692 over the phone buying an ottoman from an upscale merchant Recently moved Is 35 years old; HH income is $200k+ Gave $50 to a child welfare agency 15 days ago #DM201

8. Six Major Players • Different modeling techniques • Find different audiences • Three co-ops serve more than just the NP industry #DM201

9. Modeling - Defined • A statistical model is a mathematical tool used to predict a desired outcome using a pool of sample data. (Models are tools, not lists!) • Models analyze past behavior in order to predict future behavior. • Models are not completely unique of each other and can be geared towards different objectives: • Cost to Acquire • Average Donation • Revenue per Piece • Response Rate • Lifetime Value #DM201

10. Modeling - Defined • Models are made up of variables of importance as determined by the sample data provided #DM201

11. Modeling - Defined Ax + By + Cz = Score* Scores are sorted from top to bottom x, y, z are the variables A, B, C are the weights for each variable Score = the sum of the weighted variables *Each record gets a different score based upon its variable characteristics Segment 1 Segment 2 Segment 3 Segment 4 Segment X Segments are created #DM201

12. Types of Models Profile/Clone Models vs. Response Models Mail File #1 Mail File #2 Mail File #3 Descriptive or Look-a-Like variables Differentiating Variables 2,000 responders per mailing is desirable Response #3 Response #1 Response #2 #DM201

13. What Makes a Great Model • 1-3 modeling samples (mail files and response files) with 1,000+ acquisition responders per mail sample • Modeling from campaigns with similar results and appeals • Offsetting universes – responders vs non-responders • OR 5,000+ 0-24 mo NTF donors (profile model) • Breadth and depth of modeling data • Modeling software – multiple platforms • Data Science expertise – powerful analytics #DM201

14. Model Ranking and Segmentation Ms. Orange donates $50 once a year Ms. Orange is identified by the lens of a revenue-focused modelas a Segment 1 donor.  Meanwhile, Prof. Plum is placed in Segment 6 by the same model. In this view, Ms. Orange is seen as having a greater likelihood of giving, and giving a larger gift, than Prof. Plum. In a response-focused model, Prof. Plum would likely be in segment 1 and Ms. Orange would be in segment 6. Prof. Plum donates 10 times a year at $10/donation #DM201

15. A Modeling Example – Acquisition-Focused DATA SET October 2015 450,837 pcs 2.08% RR $31 avg gift $.80 I/O RESULTS October 2018 116,383 pcs 4.18% RR $41 avg gift $1.33 I/O #DM201

16. Primary Co-op Solutions • #1 Prospect Audiences • #2 Lapsed Reactivation • #3 Telemarketing • #4 Other House File Solutions • Major and Mid-Level Donor Targeting • Sustainer Targeting • Overlay and Data Append #DM201

17. What’s Trending • Multiple Model Solutions • Balance/Backfill Models • Post-Merge Optimization • Marketing Budget Optimization • Drop & Replace • Machine Learning and AI Technology • Digital Marketing Options • Non-Co-op Options – Ultimate Data and Ultimate Audiences • Email #DM201

18. Multiple Model Solution • BENEFITS • Larger universe • More stable results • Less tracking . #DM201

19. Balance/Backfill Models • BENEFITS • Fill to meet quantity requirements for production • Pay on net basis since done post- merge Client’s Best Model Merge Net Output File Balance Names Kill/Suppress Files #DM201

20. Post-Merge Opto - Marketing Budget Optimization • Many marketers have significant waste in their marketing budget. • Some models are built to identify the bottom of an audience in order to eliminate the waste. #DM201

21. Post-Merge Opto - Marketing Budget Optimization • This model predicts a RR of 1.24% • Recommend dropping at least seg 20 #DM201

22. Post-Merge Opto - Marketing Budget Optimization CASE STUDY NO OPTIMIZATION COMPARISON WITH OPTIMIZATION $/Pc Change $/Pc $/Pc #DM201

23. Post-Merge Opto - Drop and Replace • Drop segments 37, 38, 39, 40 • Averaging $.10/pc • Replace with best available balance names • Averaging $.44+ rev/piece #DM201

24. Machine Learning and AI • Creation of data elements on the fly based on combination variables • More sophisticated variable interaction • Here’s the difference: • In a traditional regression model, points are assigned on a uni-variate basis – ie, points assigned for female, points assigned for age 30-35 • In our newest machine learning model, combination variables are created with points assigned based on the multi-variate combo variable – ie, points assigned for the combination of age 30-35 if female #DM201

25. Digital - Co-Targeting of Modeled Names • Co-Targeting • Integrated DM & Digital Display Ad Marketing • Provides additional lift to DM campaigns • Challenges • Permission • Allocation of Returns #DM201

26. Digital - Pre-Loaded Audiences #DM201

27. Email • Append and Reverse Append • Match rates: 50% to 70% • Requires “permission pass” opt-out email which allows the consumer to opt-out within a 10 day period • Costs vary depending upon volume (est $15-50/M) • Many e-append vendors • Uses: • Increase size of email file • Reverse append to retrieve postal address for DM • Email List Rental #DM201

28. Finally…Why do Co-ops Work? • Pure breadth and depth of data – billions of transactions • Often a lower cost than conventional list rental • Experienced Team of Data Scientists! • Large universes – scalability • Ability to tweak models that are under-performing • Constant flow of fresh data • Purchased demographic overlay data • Laser focus on a single task – finding responsive audiences for our clients! • Better results and higher LTV • Access to audiences not otherwise available through rental or exchange #DM201