Predicting the Next Planned Gift

1. Predicting the Next Planned Gift Josh Birkholz Bentz Whaley Flessner

2. Bright Spots

3. Plan • Setting the stage • Introducing Predictive Analytics. • How is it accomplished? • Just One Statistical Principle:Randomized Testing • Bringing Analytics In-House

4. Setting the stage

5. Fundraising Has Three Primary Business Processes Base Development One-to-many strategies of engagement Major/Planned Gift Development One-to-one high ROI strategies Prospect Development Conversion from base to major

6. Market ResearchIdentification with screening and modeling Prospect Research Qualification with data Field Research Discovery / qualification through interaction Plan Strategy Major GiftFundraisingCycle Stewardship Cultivation Solicitation Prospect Development has Three Stages Feeding Major and Planned Gift Cultivation

7. Effective Prospect Development for Planned Giving • Identifies prospects meeting the criteria planned gift donors. • Traditional characteristics • Characteristics unique to your organizations • Works with fundraisers to develop strategies for aligning the prospects with the institution for a philanthropic partnership.

8. Characteristics Observations • Assumptions generally accurate for most institutions. • Other common characteristics from our research: • Legacy families • Multiple property owners • Employment in education and public service • Donor loyalty • Positive donor experience Assumptions • Consistent donors • Old donors • Donors with appreciated assets

9. How is Loyalty Achieved? + = Needs met consistently Needs Loyalty

10. Example: Positive Donor Experience

11. Introducing Predictive Analytics

12. What Is Meant by “Analytics?” • Analytics describes the statistical tools and strategies for: • Analyzing constituencies. • Building models to predict constituent behaviors. • Evaluating program performance using relevant metrics. • Projecting future program performance.

13. Analyzing Constituencies • Applications • Portfolio optimization. • Segmentation strategies. • Event programming. • Identifying core constituent groups. • Defining their characteristics. • Understanding their motivations.

14. Data Mining and Predictive Modeling:What Is “Data Mining?” • Using statistics to identify patterns in data. • Comparing characteristics of people or things doing a behavior with people or things not doing the behavior.

15. Common non-fundraising examples: Credit ratings Meteorology Airport security Data Mining and Predictive Modeling: Predicting Behaviors from the Patterns

16. Modeling Can Predict Many Things • Membership likelihood • Season ticket subscriptions • Alumni affinity • Channel preferences (mail, phone, email) • Next gift amounts • Loyalty scoring with precise weightings • Major, planned, and annual giving • Program or department models. (giving to fine arts, capital needs, scholarships, patient care, etc.)

17. Effective for Planned Giving: Yourconstituents compared to Yoursuccess stories using Yourdata to identify Your unique opportunity

18. How is it accomplished?

19. Method • Understand your goals before you begin. • Gather your data. Included demographics, giving, research, and screening data. • Prepare the data for modeling. • Model. • Evaluate the results against existing donors and prospects. • Score the file and implement the results.

20. Common Score Format (Fractional ranking displayed) All records have a ranking and a 0–1,000 score.

21. Evaluate by Comparing Scores to Actual PG Donors

22. Categorize Variables From Output

23. Sample of Possible Variables in Your Model

24. Opportunity: Review Portfolio, Prioritize Direct Marketing Appeals

25. Examples: Successful Implementation • New planned giving director. • Prepared new prospect list. • Felt it was a “stacked deck.” • Program needed jump-start. • Purchased predictive models. • Aggressively marketed and discovered new names. • Had best planned giving year in history.

26. Just One Statistical Principle:Random Testing

27. Drawing Planned Giving Donors Out of a Hat • Imagine a hat with 130 slips of paper. • About 31% of the slips have the words “planned giving donor” written on them. • If you draw a slip out of the hat, approximately 1 in 3 will be a PG donor. • For most organizations, planned giving donors represent a far lesser portion (<5%).

28. Can We Improve This Ratio? • We could survey our actual planned giving donors asking: • How would you describe yourself? • A blue slip of paper • A green slip of paper • A yellow slip of paper

29. Survey ResultsNow Which Slip of Paper Will You Select?

30. If You Choose Blue… • Will you draw a planned giving donor on average 1 out of 2 times?

31. The Answer: Unknown • There is not enough information. • You do not know the distribution of the random population.

32. Consider Your View • Now, which slip will you select? 1 in 3 1 in 5 4 in 5

33. Principle • Common characteristics may not be distinguishing characteristics. • How populations are different (target vs. random) is more interesting statistically and predictive than common characteristics of a target group.

34. Bringing Data Mining In-House

35. Bringing Data Mining In-House • More and more organizations have in-house data mining capacity, from large shops to small shops. • Large shops generally have dedicated staff. • Small shops have developed the skill sets in research, advancement services, or annual giving.

36. Making the Case • Gather references of peers and aspirant peers. • Build a cross-functional project team. • Start with short-term projects—specific appeals. • Communicate goals before the project. • Communicate the success after the project. • Educational and research institutions: • Explore on-campus knowledge resources (economics, statistics, business departments). • Explore on-campus software resources.

37. Statistics Software • SPSS • My personal preference • User friendly for expert and novice alike • Large network of other researchers using SPSS • SAS • Very powerful for large data sets • Needed for regulatory testing (not necessary in fundraising) • Good network of researchers using SAS • DataDesk • Object-oriented format easy to understand • Excellent for exploratory analysis • Large network of other researchers using DataDesk

38. Training • Software training courses • Conferences and users groups • Learning through outsourcing (you are buying methodology as well as analysis) • Onsite consulting • Campus resources

39. Learn Through Outsourcing • Many organizations outsource their analytics; benefits include: • Expert analysis. • Opportunity to learn from their methodology. • High level of service over the short term.

40. Developing In-House Capacities • It is not hard to learn. • Analytics is becoming part of the constituent relations and admissions skill set. • Nobody knows your data like you do. • Ability to create multiple models and analysis—not to be restricted by costs.

41. Final Thoughts

42. When You Leave Today, Remember: • Start with your bright spots. • Build a prospecting plan around your characteristics. • Consider predictive analytics to identify and prioritize your list. • Comparing PG donors to random donors is more valuable than summarizing common PG donor characteristics. • Whether you outsource or build analytics in-house, analytics is within your reach.

43. Questions? Joshua Birkholz Principal, Bentz Whaley Flessner Founder of DonorCast 7251 Ohms Lane Minneapolis, Minnesota 55439 ph: 952-921-0111 fax: 952-921-0109 jbirkholz@bwf.com www.donorcast.com 89646:JMB:abl:050410.