1. MAKE DATA WORK HARDERSUCCESSFULLY EMBED PREDICTIVE ANALYSIS
INYOUR FUNDRAISING STRATEGY
2. Attitude Data analysis does not replace fundraising skill,
it compliments it. Analysts must work in partnership with
fundraisers to accomplish common goals.
3. Appetite Find your champion Demonstrate worth on small low
risk project
4. 2010 ROI = = 138%Introduction of predictive model2011 ROI =
= 294%
6. Communicate Understand your audience Practical analytics not
data science Easy to go too far
7. What is a predictive model? Find those that look like your
donors andyou 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
8. 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
9. What does a donor look like?
10. The questionsIs there any point looking at legacy pledgesto
find new donors?Do these results give email address morepredictive
power?
11. The answers It is impossible to tell. Why?We have ignored
our non donors.
12. The complete picture
13. The answersEmail address = COMMON characteristicLegacy
pledge = DISTINGUISHING characteristicMAJORITY of donors have email
yet MINORITY ofthose with email are donors.MINORITY of donors have
pledged legacy yetMAJORITY of legacy pledgers are donors.
14. The question is NOT Why do people give?.xkcd.com
15. Selecting VariablesGiving history AgeWealth indicators
Questionnaire/Survey responderInterests Email clicksAffiliations
Twitter/facebookGender Events attendedSign up/subscriptions Family
relationshipsEmployment/positions AddressMarital status EmailDegree
PhoneMailing preference (opt outs) First gift amountVolunteers
Proximity
16. Prepare your data file Constituent Is a donor? Attended Has
email? Over 40? ID Event? 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 Excel v SPSS
19. Conclusions. The average donor and the average non-donor
may look the same. Look for distinguishing characteristics not
common ones. Dont look at donors in isolation. Compare data for
donors with data for everyone.
20. 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.
21. Paul WeighandInsight ManagerUniversity of
Edinburgh@paulweighand
