1. PREDICTIVE MODELING WITH MAJOR DONORS The 2002 CARA Summer Workshop Peter Wylie, Margolis Wylie Associates

2. PREDICTIVE MODELING: AN OVERVIEW WHAT IS IT? WHY DO IT? HOW DO YOU DO IT? DOES IT REALLY WORK? SHOULD YOU DO IT YOURSELF OR HAVE IT DONE FOR YOU?

3. WHAT IS IT? A WAY TO USE THE RICHNESS OF YOUR DONOR DATABASE TO IDENTIFY GOOD PROSPECTS CAN GET TECHNICALLY COMPLICATED BUT CONCEPTUALLY SIMPLE

4. WHY DO IT? 1.You can learn huge amounts about who your donors are 2.You can save big money on appeals 3.You can generate lots more money for your mission

5. How Do You Do It? 1.DECIDE WHAT YOU WANT TO PREDICT 2.PICK A LIMITED NUMBER OF POSSIBLE PREDICTORS 3.BUILD A FILE (RANDOM SAMPLE FROM YOUR DATABASE) 4.IMPORT THE FILE INTO A STAT SOFTWARE APPLICATION 5.SPLIT THE FILE IN HALF AT RANDOM 6.SEARCH FOR PREDICTORS ON ONE HALF OF THE FILE AND BUILD A MODEL 7.CHECK THE MODEL OUT ON THE OTHER SAMPLE 8.TEST THE MODEL 9.IMPLEMENT THE MODEL

6. Let’s Walk Through An Example from The U of Minnesota Annual Fund

7. Step 1: Decide What You Want To Predict Randy Bunney & Pete Wylie decide on: –Life to date giving –Total number of gifts

8. Step 2: Pick a Limited Number of Possible Predictors These are some of the ones we chose: –Job Title –Gender –Birth Date –Marital Status –Grad Year –Degree Count –Bus Phone –Email

9. Step 3: Build A Random Sample IS folks built an Excel file of 10,000 random records from a database with over 700,000 living alumni and friends

10. Steps 4 & 5: Importing And Splitting Working over the phone, we imported the excel file into the stat application (Datadesk) and randomly divided the file into two halves of 5,000 records each

11. Step 6: (on 1/2 of the file ) Find predictors. Build a model. Some of promising predictors we found: –Job title listed (Yes/No) –Marital status listed as “married” (Yes/No) –Born before 1948 (Yes/No)

12. Job Title Status

13. Marital Status

14. Age as a Factor

15. The Model We Came Up With Score = (Bus Phone Good) + (Home Phone Good) + (Job Title Listed) + (Married) + (Born Before 1948)

16. Step 7: Check Model Against the Other Sample

17. Step 8: Test the Model

18. More Testing

19. Step 9: Implement The Model UM decided to only re-appeal to records scored 3 or above.

20. An Other Experiment Oklahoma State University SCORE = (Bus Phone Yes) + (Oc-Tit Listed) + (Emplr Listed) + (Bus City Listed) + (Stud Org Listed) + (Alum Member) + (Mrtl Code Listed) + (Child Fir Nam Listed) + (Child Birth Date Listed)

21. Oklahoma State University Pledges By Score

22. Oklahoma State University Dollars Pledged by Score

23. What About Major Giving? Will modeling work as well as it does for the annual fund?

24. Data From 5 Other Schools Large samples of records noting if a person: –Had given a total of $1,000 or more or not to the school –Had a business phone listed or not –Had an e-mail address listed or not –Had an age of 52 or older listed or not

25. Business Phone Status

26. E-mail Status

27. Giving and Age

28. LET’S LOOK AT AGE AT ONE OF THESE SCHOOLS

29. Multiple Factors

30. Modeling: In-house Or Have It Done For You? Doing it all by yourself isn’t feasible. Besides, there are excellent products and services out there that shouldn’t be ignored.

31. A NEW KIND OF RESEARCHER IN ADVANCEMENT? Without an inside specialist, the data enhancement products and services you purchase are less likely to be used effectively. A blunt question. In the past five years, have you spent more than $25,000 on enhancing your database with estimates of wealth, capacity to give, and so on, only to have the information untapped and unused by your development officers? Why buy the stuff if you’re not going to use it? An inside data analyst can not only help you use data effectively, he or she also can be a persistent thorn in your side until you do use it. A good data analyst is worth the effort and cost of creating a new position.

32. In-house Modeling & Analysis Worth the consideration because of: The richness of info in your database Speed Continuity The “Big Picture” Vendor screening

33. Questions & Comments