MAKE DATA WORK HARDER SUCCESSFULLY EMBED PREDICTIVE ANALYSIS IN YOUR FUNDRAISING STRATEGY

Attitude

• Data analysis does not replace fundraising skill, it compliments it.

• Analysts must work in partnership with fundraisers to accomplish common goals.

Appetite

• Find your champion

• Demonstrate worth on small low risk project

2010 ROI =

= 138%

Introduction of predictive model

2011 ROI =

= 294%

2010 CRP =

= £0.72

Introduction of predictive model

2011 CRP =

= £0.34

Communicate

• Understand your audience

• Practical analytics not data science

• Easy to go too far

What is a predictive model?

Find those that look like your donors and

you will have a better chance of producing

more donors!

• Gather data about your constituents

• Find data with predictive power

• Combine data to produce a model

What gives data predictive power?

What does the average donor look like?

• Predictive models use distinguishing characteristics not

common characteristics

• Do not look only for similarities between your donors

• Look for distinguishing qualities between your donors

and the rest of your constituents

What does a donor look like?

The questions

Is there any point looking at legacy pledges

to find new donors?

Do these results give email address more

predictive power?

The answers…

It is impossible to tell.

Why?

We have ignored our non donors.

The complete picture…

The answers…

Email address = COMMON characteristic

Legacy pledge = DISTINGUISHING characteristic

MAJORITY of donors have email yet MINORITY of

those with email are donors.

MINORITY of donors have pledged legacy yet

MAJORITY of legacy pledgers are donors.

The question is NOT “Why do people give?”.

xkcd.com

Selecting Variables

Giving history Age

Wealth indicators Questionnaire/Survey responder

Interests Email clicks

Affiliations Twitter/facebook

Gender Events attended

Sign up/subscriptions Family relationships

Employment/positions Address

Marital status Email

Degree Phone

Mailing preference (opt outs) First gift amount

Volunteers Proximity

Prepare your data file

• Excel v SPSS

Constituent ID

Is a donor? Attended Event?

Has email? Over 40?

A 1 1 1 1

B 1 0 1 1

C 0 1 1 0

D 1 1 0 1

E 0 0 1 1

Evaluate

Score Decile

Non donors

Donors Total Donor Ratio

1 3611 59 3670 1.61%

2 4672 54 4726 1.14%

3 3351 145 3496 4.15%

4 4906 172 5078 3.39%

5 3698 275 3973 6.92%

6 3813 351 4164 8.43%

7 3511 489 4000 12.23%

8 3575 593 4168 14.23%

9 3593 802 4395 18.25%

10 3190 1010 4200 24.05%

Baseline 37920 3950 41870 9.43%

Evaluate

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 6 7 8 9 10

Do

no

r R

atio

Constituent Decile

Conclusions….

• The average donor and the average non-donor

may look the same.

• Look for distinguishing characteristics not

common ones.

• Don’t look at donors in isolation. Compare data

for donors with data for everyone.

Conclusions….

• Data modelling can help you focus your resources on the best prospects.

• Demonstrate worth on low risk segments.

• Consider your audience. Communicate results so that everyone can understand.

Paul Weighand

Insight Manager

University of Edinburgh

@paulweighand