Predicting the Next Planned Gift Josh Birkholz Bentz Whaley Flessner

Bright Spots 1

Plan Setting the stage Introducing Predictive Analytics. How is it accomplished? Just One Statistical Principle: Randomized Testing Bringing Analytics In-House 2

Setting the stage 3

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

5 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 Prospect Development has Three Stages Feeding Major and Planned Gift Cultivation

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.

Characteristics Assumptions Consistent donors Old donors Donors with appreciated assets 7 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

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

Example: Positive Donor Experience 9

Introducing Predictive Analytics 10

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. 11

Analyzing Constituencies Identifying core constituent groups. Defining their characteristics. Understanding their motivations. 12 Applications Portfolio optimization. Segmentation strategies. Event programming.

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. 13

Data Mining and Predictive Modeling: Predicting Behaviors from the Patterns 14 Common non-fundraising examples: - Credit ratings - Meteorology - Airport security

Modeling Can Predict Many Things Major, planned, and annual giving Program or department models. (giving to fine arts, capital needs, scholarships, patient care, etc.) 15 Membership likelihood Season ticket subscriptions Alumni affinity Channel preferences (mail, phone, ) Next gift amounts Loyalty scoring with precise weightings

Effective for Planned Giving: Your constituents compared to Your success stories using Your data to identify Your unique opportunity 16

How is it accomplished? 17

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. 18

Common Score Format (Fractional ranking displayed) Planned Giving Rank Label Planned Giving Score MinimumMaximum 0 Lower 50% Top 50% Top 25% Top 10% Top 5% Top 2.5% Top 1% Top 0.5% Top 0.25% Top 0.1% 9991, All records have a ranking and a 0–1,000 score.

Evaluate by Comparing Scores to Actual PG Donors 20

Categorize Variables From Output 21

Sample of Possible Variables in Your Model CategoryVariable Giving Length of Giving Relationship Frequency Index Monthly Payment Preference Capacity Multiple Property Ownership Geography >100 miles from campus Wisconsin (-) (+) Management Event Attendance (+) Survey Response (+) Alumni Volunteer(+) Demographics Education Job Title(+) Single(+) 22

Opportunity: Review Portfolio, Prioritize Direct Marketing Appeals 23 Planned Giving Model Rank Not Assigned a Prospect ManagerManaged 0 Lower 50%53, Top 50%26, Top 25%15, Top 10%4, Top 5%2, Top 2.5%1, Top 1% Top 0.5% Top 0.25% Top 0.1%2088

Examples: Successful Implementation 24 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.

Just One Statistical Principle: Random Testing 25

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%).

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

Survey Results Now Which Slip of Paper Will You Select?

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

The Answer: Unknown There is not enough information. You do not know the distribution of the random population. 30

Population Total Count % of TotalPG Donors % of PG Donors % of Color that are PG Donors Blue6046%2050%33% Green6046%1230%20% Yellow108%820% 80% Total130100%40100% 31%  Now, which slip will you select? 1 in 31 in 54 in 5 Consider Your View

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. 32

Bringing Data Mining In-House 33

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. 34

35 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.

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 36

Training Software training courses Conferences and users groups Learning through outsourcing (you are buying methodology as well as analysis) Onsite consulting Campus resources 37

38 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.

39 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.

Final Thoughts 40

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. 41

89646:JMB:abl: Ohms Lane Minneapolis, Minnesota ph: fax: Joshua Birkholz Principal, Bentz Whaley Flessner Founder of DonorCast Questions?