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PREDICTIVE MODELING WITH MAJOR DONORS

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					PREDICTIVE MODELING
WITH MAJOR DONORS
 The 2002 CARA Summer Workshop
    Peter Wylie, Margolis Wylie Associates
  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?
        WHAT IS IT?
A WAY TO USE THE RICHNESS OF
YOUR DONOR DATABASE TO
IDENTIFY GOOD PROSPECTS

CAN GET TECHNICALLY
COMPLICATED BUT CONCEPTUALLY
SIMPLE
           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
         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
 Let’s Walk Through An
        Example
from The U of Minnesota Annual Fund
                   Step 1:
     Decide What You Want To Predict


Randy Bunney & Pete Wylie decide on:

  – Life to date giving
  – Total number of gifts
                     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
              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
           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
     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)
             Job Title Status

  90 MEAN (AVERAGE) NUMBER OF GIFTS GIVEN BY
  80 WHETHER OR NOT JOB TITLE WAS LISTED IN
                    DATABASE
  70
  60                                         6.2

  50              7.0                              East
  40              6.0
                                                   West
                  5.0
  30              4.0
                                                   North
NUMBER OF GIFTS            1.7
  20              3.0

                  2.0
  10              1.0
    0             0.0
                    NOT LISTED   JOB
         1st Qtr 2nd Qtr 3rd Qtr 4thTITLE LISTED
                                      Qtr
                 Marital Status

  90       MEAN (AVERAGE) NUMBER OF GIFTS GIVEN BY
        WHETHER OR NOT LISTED AS "MARRIED" IN DATABASE
  80
  70                                              4.5

  60            4.5

  50            4.0                                       East
                3.5
  40            3.0
                                                          West
  30
NUMBER OF GIFTS
                2.5
                               1.2                        North
                2.0
  20            1.5
                1.0
  10            0.5

    0           0.0
                      NOT LISTED      LISTED AS MARRIED
         1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
            Age as a Factor

  90 MEAN (AVERAGE) NUMBER OF GIFTS GIVEN BY
        WHETHER OR NOT BORN BEFORE 1948
  80
  70
                                               6.2
  60
                7.0
  50            6.0
                                                     East
  40            5.0                                  West
  30
NUMBER OF GIFTS
                4.0
                              1.8
                                                     North
                3.0
  20            2.0

  10            1.0

    0           0.0
                      OTHER         BORN BEFOR '48
         1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
The Model We Came Up With

Score = (Bus Phone Good) + (Home
Phone Good) + (Job Title Listed) +
(Married) + (Born Before 1948)
                               Step 7:
Check Model Against the Other Sample
                MEAN (AVERAGE) NUMBER OF GIFTS GIVEN BY SCORE LEVEL ON
      90                   UMINN CROSS VALIDATION SAMPLE
      80
                16.0
      70                                                             14.3
                14.0
      60
                12.0
      50        10.0
                                                   9.7
                                                            10.1         East
      40
      # GIFTS    8.0
                                                                         West
      30         6.0                                                     North
                                          3.5
      20         4.0

                 2.0             1.4
      10                0.4
                 0.0
        0              S0       S1       S2       S3       S4       S5

                1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
                                 SCORE LEVEL
               Step 8:
         Test the Model
90   UNIVERSITY OF MINNESOTA MAIL
80    CAMPAIGN: PERCENTAGE OF
70       GIVING BY SCORE LEVEL
60
     10.0                                   8.6
50                                                East
      8.0                             6.2
40                                                West
      6.0                 3.5   4.2
30 %                                              North
      4.0           1.7
20      2.0   0.2
10      0.0
              S0    S1    S2    S3    S4    S5
0
     1st Qtr 2nd Qtr SCORE LEVEL Qtr
                      3rd Qtr 4th
More Testing

 UNIVERSITY OF MINNESOTA MAIL
   CAMPAIGN: MEAN (AVERAGE)
  DOLLARS RECEIVED BY SCORE
             LEVEL

   40.00                                      35.42

   30.00
 $ 20.00
                                       8.02
   10.00                        5.56
                         2.96
           0.14   0.83
    0.00
           S0     S1     S2     S3     S4     S5
                    SCORE LEVEL
             Step 9:
       Implement The Model

UM decided to only re-appeal to
records scored 3 or above.
      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)
Oklahoma State University
                  Pledges By Score


            OKLAHOMA STATE UNIVERSITY PHONE CAMPAIGN:
             PERCENTAGES OF ALUMS MAKING PLEDGES BY
                          SCORE LEVEL

       25                                                    23

                                                       19
       20
                                       16        17
                             15

       15               12                                         13
   %
       10          9

              5
       5


       0
             S0   S1   S2    S3       S4        S5    S6    S7    S8
                                  SCORE LEVEL
Oklahoma State University
               Dollars Pledged by Score


       OKLAHOMA STATE UNIVERSITY PHONE CAMPAIGN:
        MEAN (AVERAGE) DOLLARS PLEDGED BY SCORE
                         LEVEL

       18.00                                                               16.41
                                                                   15.97
       16.00                                               15.29

       14.00

       12.00

       10.00
   $                                                7.74
        8.00                  6.16   6.66   6.28
        6.00

        4.00           2.89
        2.00    1.15

        0.00
                S0     S1     S2     S3     S4      S5     S6      S7      S8
                                      SCORE LEVEL
What About Major Giving?

Will modeling work as well
as it does for the annual fund?
 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
Business Phone Status
       PERCENTAGES OF DONORS GIVING $1000 OR MORE BY WHETHER OR
                    NOT A BUSINESS PHONE IS LISTED


  20     19

  18
                    16
  16

  14
                               12
  12

% 10                                                          BUS PHONE LISTED
                                           9
                                                              NOT LISTED
   8
              6                     6
   6                                           5
                                                      4
   4                     3

   2                                                      1

   0
       SCHOOL A   SCHOOL B   SCHOOL C   SCHOOL D   SCHOOL E
                E-mail Status

         PERCENTAGES OF DONORS GIVING $1000 OR MORE BY WHETHER OR
                      NOT AN E-MAIL ADDRESS IS LISTED


    25                24



    20



    15                           14

%          12                                                   E-MAIL LISTED
                                                                NOT LISTED
    10                                       9
                7
                                      6          6
                                                        5
    5                      4

                                                            1
    0
         SCHOOL A   SCHOOL B   SCHOOL C   SCHOOL D   SCHOOL E
               Giving and Age
       PERCENTAGES OF DONORS GIVING $1000 OR MORE BY WHETHER OR
                  NOT THEY ARE LISTED AS 52 OR OLDER


  30

                     26
  25
                                23


  20

                                           15
% 15      14                                                   52 OR OLDER
                                                               OTHER

  10


   5           4                     4          4      4
                          3
                                                           1
   0
       SCHOOL A    SCHOOL B   SCHOOL C   SCHOOL D   SCHOOL E
LET’S LOOK AT AGE AT ONE OF
       THESE SCHOOLS
            PERCENTAGE OF RECORDS GIVING $50,000 OR MORE BY BIRTH YEAR



       35          33


       30


       25

       20                                                               17
   %
       15

                                9
       10
                                            4
       5
                                                          1

       0
               1953 OR    1954-1962   1963-1969   1970 OR LATER   NOT LISTED
               EARLIER
                         Multiple Factors
          PERCENTAGES OF DONORS GIVING $1000 OR MORE BY WHETHER
         NONE, ONE, TWO, OR THREE ATTRIBUTES (BUSPHONE, E-MAIL, AND
                           52 OR OLDER) ARE LISTED


    45                                     44

    40                                                         38

    35                                33                                         33

    30
                        27
                                                                                                       NONE
    25
%                  22                                                                                  ONE
    20                                                                                                 TWO
                                                          16                                      15
    15                           14                                         14                         THREE
              11                                     11
    10                                                                  9
                                                                                              5
    5     3                                      4                  3                     3
                             2                                                        1
    0
         SCHOOL A            SCHOOL B           SCHOOL C            SCHOOL D          SCHOOL E
                 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.
  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.
    In-house Modeling & Analysis

Worth the consideration because of:
•   The richness of info in your database
•   Speed
•   Continuity
•   The “Big Picture”
•   Vendor screening
Questions & Comments

				
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