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

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 Assumptions Consistent donors Old donors
Donors with appreciated assets
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

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

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

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

17. Effective for Planned Giving:
Your constituents compared to Your success stories using Your data 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.
Planned Giving Rank Label
Planned Giving Score
Minimum
Maximum
0 Lower 50% 4
500
1 Top 50%
750
2 Top 25%
900
3 Top 10%
950
4 Top 5%
975
5 Top 2.5%
990
6 Top 1%
995
7 Top 0.5%
997
8 Top 0.25%
998
999
9 Top 0.1%
1,000

21. Evaluate by Comparing Scores to Actual PG Donors

22. Categorize Variables From Output

23. Sample of Possible Variables in Your Model
Category
Variable
Giving
Length of Giving Relationship
Frequency Index
Monthly Payment Preference
Capacity
Multiple Property Ownership
Geography
>100 miles from campus
Wisconsin (-)
55439 (+)
Management
Event Attendance (+)
Survey Response (+)
Alumni Volunteer(+)
Demographics
Education Job Title(+)
Single(+)

24. Opportunity: Review Portfolio, Prioritize Direct Marketing Appeals
Planned Giving Model Rank
Not Assigned a Prospect Manager
Managed
0 Lower 50%
53,425 92
1 Top 50%
26,507
257
2 Top 25%
15,330
724
3 Top 10%
4,767
585
4 Top 5%
2,201
474
5 Top 2.5%
1,197
410
6 Top 1%
326
208
7 Top 0.5%
129
139
8 Top 0.25% 59
101
9 Top 0.1% 20 88

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 Results Now 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. % of Color that are PG Donors
Consider Your View
Now, which slip will you select?
Population
Total Count
% of Total
PG Donors
% of PG Donors
% of Color that are PG Donors
Blue 60
46% 20
50%
33%
Green 12
30%
20%
Yellow 10 8% 8
80%
Total
130
100% 40
31%
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 SAS DataDesk 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: fax:
89646:JMB:abl: