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1 Predictive Modeling The Basics Plus What You Need to Know Tweet this session: #NYNP2013 #NYNP2013models

2 Today s Speakers Joseph Coakley, Individual Giving Manager Food Bank For New York City Britt Fouks, Solution Consultant Target Analytics, a Division of Blackbaud Inc. Jeff Johnson, Account Director LW Robbins Michele Peys, Account Director LW Robbins

3 Models: Here s what we will cover today Demystify what they are and why use them? Share the science behind how they are built (but not so much that your eyes roll up into your head!) Case Studies of Success with strategies and results!

4 Why Use Models? Models : Isolate predictive response variables and apply to large datasets Reduce interpretation subjectivity Identify opportunities for optimizing universe (house-file and prospect); ensuring best return on investment

5 Modeling Camps Descriptive: Models that classifying people by like behaviors or characteristics Help you understand more about similar groups of people based on specific behaviors Predictive: Models that predict future behaviors based on past action

6 Data Here, Data There, Data, Data, EVERYWHERE! Behavioral/Transactional Attitudinal/Affinity Your own vs. External Demographic **The right data or cuts of data have more impact than the type of modeling technique**

7 Modeling Techniques- Descriptive Cluster Modeling: Grouping of similar behaviors, demographics, expressed interests, loyalties for the purpose of analyzing the relationships between those groups Descriptive, not predictive Example: clustering market segments to determine differences in purchasing behavior; leveraging these differences for product testing

8 Modeling Techniques- Predictive Regression: Compares responders vs. non-responders using specific variables & assesses strength of relationships between those variables Ex. Mail File Analysis

9 Modeling Techniques- Predictive Decision Tree/Chaid: Decision Tree Analyses map observations about data and carves large datasets down to variables used for modeling Detects interactions between variables Prunes away the least significant variables; leaving the most predictive and consistent variables for usage

10 Modeling Techniques- Predictive Decision Tree/Chaid:

11 Other Modeling Notables Scores- Output of the model built; often in numeric form Increase in complexity based on # of variables in the model Dependent vs. Independent Variables- Dependent Variables- variable to model Independent Variables- help explain the DV Correlation- the degree to which two variables are related Validation- refers to the dataset or process used to validate the model s predictive nature

12 Q/A: Common Questions Applications- where would I use a model? Prospecting- zip models, coop acquisition lists, merge and/or list optimizations Lapsed/Cultivation- warm prospect models, lapsed optimization, major and planned giving likelihood models Guidance Users/Clients have important jobs! Ask questions Provide intelligence Update how model will be evaluated

13 Q/A: Common Questions Data Prepping is important. Best input data yields best results Cultivation/Lapsed modeling should use heavy doses of your own transactional data Evaluation Questions to ask when evaluating results: Did the model do what was intended? If not, was it due to data being used? How did the model index against other results?

14 Case Studies

15 Two Basic Models Co-op List Models Built on a charities current donor pool Look-a-like to your current donorbase Potential donors have not given to your organization in the past but have given to similar groups or have exhibited purchasing behaviors which makes them a good target for an acquisition mailer. To access this market your organization will be required to share your list of donors and transactions.

16 Two Basic Models Lapsed Reactivation Models Focused on donors with no recent gift to a charity Definition varies by organization. In this example we will use 36+ months lapsed. For most organizations reactivating a lapsed donor is worth more in long term revenue than a new donor. Built to find donors with recent donation activity by affinity and RFM to other organizations in the coop.

17 Concerns Organization wants to protect donors from being mailed by other groups! This concern is invalid. On average an individual direct mail donor supports 12.74* organizations. Your donors are actively supporting almost 13 charities other than yours and if one, two or ten of those groups share their data then your donors are already part of the Cooperative Databases. Some groups would need to change their privacy policy to access modeled names. * Figure taken from a May 2013 Target/Blackbaud Coop profile report.

18 Case Study Vietnam Veterans Memorial Fund (VVMF) The VVMF had over one million lapsed donors and felt that those lapsed donors were not mined effectively.

19 Lapsed Reactivation Strategy Set up lapsed reactivation models for the VVMF donors. Broke the donors into two segments months donors and 61+ month donors. Set up a data test to evenly split the data to allow for three modeling agencies to test against each other and to allow the client to take advantage of the best performing lapsed reactivation model.

20 Lapsed Reactivation Strategy Set up the acquisition control package as the lapsed reactivation control package. Our thought was that the lapsed donors had received 36+ house file mailings that they failed to give a donation. Wanted to spend less money and due to the volume the acquisition package was the cheapest mailing. First tested the models to measure effectiveness and to determine a winner among the three agencies. Next, aggressively tested new lapsed-focused packages against the control package.

21 Lapsed Reactivation Model Many groups mail to lapsed donors based on a standard RFM data select. As the recency of the donor becomes older we tend to select higher gifts months $ months $ months $100+ Selections like these ignore the $25 donor that gave in 2007 who may be ready to give again?

22 Lapsed Reactivation Model RFM selects are based on past giving history to your organization only. Models look at recent giving history across all of the charities in their database as well as purchase history in some databases.

23 Previous Lapsed Reactivation Plan In 2011 there were eight lapsed mailings of varying creative. Selects based on RFM. Results on the surface were solid.

24 Previous Lapsed Reactivation Results Year Total Qty Gifts Response Pct Gross Avg Gift CPDR ,659 7, % $154,317 $21.77 $1.27 Overall cost per dollar raised of $1.27 was solid for lapsed donors. However, we knew VVMF could mail deeper into their lapsed donor pool and possibly improve their cost per dollar raised.

25 Sample Lapsed Scoring Month 61+ months Segment Volume Segment Volume A 25,000 A1 25,000 B 25,000 A2 25,000 C 25,000 B1 25,000 D 25,000 B2 25,000 E 25,000 C1 25,000 F 25,000 C2 25,000 G 25,000 D1 25,000 H 25,000 D2 25,000 I 25,000 E1 25,000 J 25,000 E2 25,000 Segment A is the highest ranked group of lapsed donors. A segment A name could be any donor of any value that had given a gift in the last months. Measuring the performance requires you to create coding to track by model. Lapsed model segments are usually larger than RFM segments which makes analysis of a segment easier.

26 Deployment Over the course of 2012 we mailed seven dedicated lapsed mailings - average 130K per mail drop. Each of the three agencies produced successful models but we determined the strongest of the three. Increased lapsed volume by 59%.

27 Increasing Lapsed Reactivation Year Total Qty Gifts Response Pct Gross Avg Gift CPDR ,104 14, % $234,942 $15.74 $ ,659 7, % $154,317 $21.77 $1.27 Percentage Increase 59% 111% 32% 52% -28% -6% Increased the number of gifts by 111% Response rate up 32% Gross revenue up as expected and more importantly cost per dollar raised fell by 6%

28 Takeaways If your organization is not using lapsed reactivation models consider it. If you have a limited budget consider focusing some of your spending at lapsed reactivation instead of acquisition. Can be accomplished simply by mailing modeled names inside your acquisition efforts. Or setup a lapsed reactivation track separate from acquisition.

29 Case Study Food Bank For New York City (FBNYC) All of the lists rented by the FBNYC for acquisition purposes are limited to the five boroughs of New York City. We wanted to efficiently grow our program but we needed new names.

30 FBNYC Issue Performance had been solid through 2009 but in 2010 our list performance slipped. Over the years the FBNYC had mailed many donor and commercial lists in our acquisition mailings. Had only one co-op model list in our continuation list of lists. Wanted to grow program but felt limited thinking we reached a wall on the good performing donor lists - were not willing to accept poorer performance levels to widen our list market.

31 Acquisition Strategy Co-op Models Expand our modeling efforts by testing multiple segments within two additional co-op models. After Year I we found success with each of the two new models. Year II we expanded the volume on the three working models and tested two new co-op models. By Year III we had five strong modeled lists and tested our sixth model. As we approach this fall we are in the process of adding our seventh model.

32 Concerns One of our major concerns was that by expanding within more models, they would hurt the performance of the other models. Another concern was that we would be paying for many of the same names twice or three times by expanding our modeling efforts.

33 Concerns Found some cross over between models but overall we were happy with net names after the merge purge. Overall percentage of multibuyers is lower in 2012 then Last year our merge purge produced a sizeable number of multibuyers. However, the multibuyers were a top ten list with a cost per dollar raised of less than $1.20.

34 Results In 2008 & 2009 our list mix consisted of: Co-op Models 13% Rental lists (non-models) 65% Multibuyers 22% Overall cost per dollar raised was $1.50 In 2012 our list mix now consists of: Co-op Models 50% Rental lists (non-models) 27% Multibuyers 23% Overall cost per dollar in 2012 was $1.04.

35 We anticipated your questions

36 Some models may be sexy, but are they appropriate for you? Each organization is different and models can vary widely. Our advice is to let the numbers guide you. Each modeling agency will be able to tell you which model of theirs is the strongest. Test their strongest model remember they have a strong incentive to develop a successful model for your organization.

37 What qualities and characteristics should you look for in a modeling partner? If this is your first foray into modeling then go with an established group/s. If your organization has 100,000 or more 25+ month lapsed names you should have your lapsed names modeled immediately. You are leaving money on the table. Co-op models test at least one model from two different modeling agencies.

38 Are certain models out of your league, if you're a smaller organization with a smaller file? Lapsed models are tough to develop without a large number of names. Co-op models may require minimums ask for details don t make assumptions. Don t write off models because your charity is small. Reach out to see what a modeling agency can do for your group.

39 In acquisition, what is the best mix of vertical rental and exchange lists vs. modeled lists? This is a question best answered by your results. Your list broker and/or agency will make a recommendation. There is no silver bullet here.

40 Any other questions?

41 Thank You! Have a great conference!

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