Make data work harder

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Make data work harder

  1. 1. MAKE DATA WORK HARDERSUCCESSFULLY EMBED PREDICTIVE ANALYSIS INYOUR FUNDRAISING STRATEGY
  2. 2. Attitude• Data analysis does not replace fundraising skill, it compliments it.• Analysts must work in partnership with fundraisers to accomplish common goals.
  3. 3. Appetite• Find your champion• Demonstrate worth on small low risk project
  4. 4. 2010 ROI = = 138%Introduction of predictive model2011 ROI = = 294%
  5. 5. 2010 CRP = = £0.72Introduction of predictive model2011 CRP = = £0.34
  6. 6. Communicate• Understand your audience• Practical analytics not data science• Easy to go too far
  7. 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. 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. 9. What does a donor look like?
  10. 10. The questionsIs there any point looking at legacy pledgesto find new donors?Do these results give email address morepredictive power?
  11. 11. The answers… It is impossible to tell. Why?We have ignored our non donors.
  12. 12. The complete picture…
  13. 13. The answers…Email 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. 14. The question is NOT “Why do people give?”.xkcd.com
  15. 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. 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
  17. 17. EvaluateScore Non Donors Total Donor RatioDecile donors 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%
  18. 18. Evaluate 30% 25%Donor Ratio 20% 15% 10% 5% 0% 1 2 3 4 5 6 7 8 9 10 Constituent Decile
  19. 19. 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.
  20. 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. 21. Paul WeighandInsight ManagerUniversity of Edinburgh@paulweighand

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