Predicting the Next Planned Gift

Josh BirkholzBentz Whaley Flessner

Bright Spots

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Plan

Setting the stage Introducing Predictive Analytics. How is it accomplished? Just One Statistical Principle:

Randomized Testing Bringing Analytics In-House

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Setting the stage

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Fundraising Has Three Primary Business Processes

Base DevelopmentOne-to-many strategies of engagement

Major/Planned Gift DevelopmentOne-to-one high ROI strategies

Prospect DevelopmentConversion from base to major

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Market ResearchIdentification with screening and modeling

Prospect ResearchQualification with data

Field ResearchDiscovery / qualification

through interaction

Plan Strategy

Solicitation

Stewardship Cultivation

Major GiftFundraising

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

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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

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Introducing Predictive Analytics

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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.

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Analyzing Constituencies

Identifying core constituent groups. Defining their characteristics. Understanding their motivations.

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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.

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Data Mining and Predictive Modeling: Predicting Behaviors from the Patterns

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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.)

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Membership likelihood Season ticket

subscriptions Alumni affinity Channel preferences

(mail, phone, email) 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

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How is it accomplished?

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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.

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Common Score Format (Fractional ranking displayed)

Planned Giving Rank Label

Planned Giving Score

Minimum Maximum

0 Lower 50% 4 5001 Top 50% 500 7502 Top 25% 750 9003 Top 10% 900 9504 Top 5% 950 9755 Top 2.5% 975 9906 Top 1% 990 9957 Top 0.5% 995 9978 Top 0.25% 998 9999 Top 0.1% 999 1,00020

All records have a ranking and a 0–1,000 score.

Evaluate by Comparing Scores to Actual PG Donors

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Categorize Variables From Output

Demographics

Capacity

Management

Geography

Giving

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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(+)

DemographicsEducation Job Title(+)

Single(+)23

Opportunity: Review Portfolio, Prioritize Direct Marketing Appeals

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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

Examples: Successful Implementation

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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

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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 ResultsNow 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.

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PopulationTotal Count

% of Total

PG Donors

% of PG Donors

% of Color that are PG

Donors

Blue 60 46% 20 50% 33%

Green 60 46% 12 30% 20%

Yellow 10 8% 8 20% 80%

Total 130 100% 40 100% 31%

33%

67%

Now, which slip will you select?

1 in 3

1 in 5

4 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.

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Bringing Data Mining In-House

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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.

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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

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Training

Software training courses Conferences and users

groups Learning through

outsourcing (you are buying methodology as well as analysis)

Onsite consulting Campus resources

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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.

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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

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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.

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89646:JMB:abl:050410.

7251 Ohms Lane Minneapolis, Minnesota 55439

ph: 952-921-0111 fax: 952-921-0109jbirkholz@bwf.com www.donorcast.com

Joshua BirkholzPrincipal, Bentz Whaley Flessner

Founder of DonorCast

Questions?