Peter Fader Professor of Marketing, The Wharton School Co-Director, Wharton Customer Analytics Initiative

Transcription

1 DATA-DRIVEN DONOR MANAGEMENT Peter Fader Professor of Marketing, The Wharton School Co-Director, Wharton Customer Analytics Initiative David Schweidel Assistant Professor of Marketing, University of Wisconsin- Madison School of Business Page

2 WHAT IS CUSTOMER ANALYTICS? Customer analytics is the collection, management, analysis and strategic leverage of an organization s granular data about the behaviors of its customers. The practice of customer analytics crosses boundaries between platforms and industries and is inherently multidisciplinary. Page 2

3 CONNECTING DECISION MAKERS WITH ACADEMICS Page 3

4 LAPSED OR DORMANT? LEVERAGING RECENCY AND FREQUENCY IN DONATION BEHAVIOR Page 4

5 SETTING Organization: A large non-profit firm supported primarily by donor contributions Challenge: Looking at each donor s history of whether or not they gave each year, what can we predict about their future giving patterns? Focal donors: - Initial focus on 1995 cohort, ignoring donation amount - 11,104 first-time supporters who made a total of 24,615 repeat donations over the next 6 years Reference: Fader, Peter S., Bruce G.S. Hardie, and Jen Shang (2010), Customer-Base Analysis in a Discrete-Time Noncontractual Setting, Marketing Science, 29 (6), Page 5

6 HOW MUCH WILL DONORS GIVE IN THE FUTURE? HOW DOES IT DEPEND ON THEIR PAST PATTERNS? ID ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Page 6

7 LET S FIRST LOOK AT BOB : WHAT CAN WE PREDICT ABOUT HIS GIVING IN ? ID ????? ????? ????? ????? ????? ????? ????? BOB ????? ????? ?????... Page 7

8 HOW MANY DONATIONS WOULD WE EXPECT BOB TO MAKE OVER THE NEXT 5 YEARS? a) 5.0 b) 4.75 c) 4.25 d) 3.75 Page 8

9 HOW ABOUT SARAH? ID SARAH ????? ????? ????? ????? ????? ????? ????? BOB ????? ????? ????? ????? Page 9

10 HOW DO MARY AND SHARMILA COMPARE? ID SARAH ????? ????? ????? MARY ????? ????? ????? ????? BOB ????? SHARMILA ????? ?????... Page 10

11 WHO IS EXPECTED TO MAKE MORE DONATIONS OVER THE NEXT 5 YEARS? a) Mary b) Sharmila c) Roughly equal Page 11

12 WHAT DONATION BEHAVIOR CHARACTERISTICS DO WE NEED TO TAKE INTO ACCOUNT? GIVING BEHAVIORS DONOR TYPES RECENCY: How recently did the donor give? When was the last time the donor gave? FREQUENCY: How many times did the donor give in the past 6 years? Most meaningful donation patterns can be described by these two metrics alone. ALIVE: DORMANT: LAPSED: Clearly an active donor: giving frequently and gave recently Has not given recently, but is likely to give again with the right development prompts Has not given recently and is not likely to give again ID MARY ????? SHARMILA ????? Page 12

13 MORE ABOUT RECENCY AND FREQUENCY Y Y N N N N Y Y N N Y Y What does it mean when there s one or more no donation at the end of a sequence? a) The donor lapsed (i.e. left the donor pool) b) The donor is dormant (i.e. decided not to give that year, didn t think of giving, etc.) c) We don t know, but can build a model to come up with a best guess Answer: c) We never know for sure whether the donor is lapsed or not; based on recency and frequency of his donation, we can make an educated guess about the probability of lapsing, so we can decide where to devote resources Based on our best guesses about the probability of death and propensity to donate, we can calculate expected frequency of future donations for each donor Page 13

14 WHAT ABOUT MARY VERSUS CHRIS? ID ????? ????? ????? ????? MARY ????? ????? ????? ????? ????? CHRIS ????? ????? Page 14

15 WHO IS EXPECTED TO MAKE MORE DONATIONS OVER THE NEXT 5 YEARS? a) Mary b) Chris c) Roughly equal Page 15

16 YOU CAN TRY THIS AT HOME Page 16

17 BUY TILL YOU DIE MODEL We employ a Buy Till You Die model to predict future donation behaviors The model only uses three inputs: 1. Recency (R) 2. Frequency (F) 3. Number of people for each combination of R/F This requires a small amount of data and provides an easier structure to work with (i.e. data are aggregated from individual-level to R/F groups) By assuming certain probability distributions for donors propensities, we can construct a robust model that is easy to implement on Excel Key details of the BG/BB model can be found in the paper by Fader, Hardie, and Shang (2010) Page 17

18 EXCEL IMPLEMENTATION Detailed step-by-step instructions available at Page 18

19 EXPECTED # OF DONATIONS IN AS A FUNCTION OF RECENCY AND FREQUENCY Name R F BOB SARAH MARY SHARMILA CHRIS # Rpt Trans ( ) Year of Last Transaction Page 19

20 ANALYSIS Bob (R:6, F:6) is expected to donate 3.75 times out of 5 opportunities between 2002 and 2006, surprisingly low given his 100% donation rate Mary and Chris have the same RF (6,4), so their expected number of donations going forward is the same Even though Mary and Chris have lower F than Sharmila, their higher R suggests that they are Alive, thus they are 50% more valuable than Sharmila Sharmila (5,5), despite high donation rate, has likely lapsed Sarah, with very low R and F, is lapsed and/or a very light donor (hard to tell) # Rpt Trans ( ) Year of Last Transaction Page 20

21 HOW DO WE KNOW THE MODEL WORKS? The model does a solid job of making predictions about future donation behaviors, as conditional expectations correspond very well with actual holdout period data ( ) Page 21

22 HOW DO WE KNOW THE MODEL WORKS? Overall, the model is exceptional good at fitting the historical data as well as the holdout period data (i.e. model predictions vs. actual number of donations between 2002 and 2006) Page 22

23 A SECOND ILLUSTRATION Using a larger dataset from a different non-profit firm, we create a heat map that shows which combinations of RF will likely yield the most valuable donors x / tx Page 23

24 PROBABILITY OF BEING ALIVE x/tx Page 24

25 SUMMARY: HOW IS THIS METHOD DIFFERENT? There are many models that predict future donation behaviors; we believe our method is different / superior because: 1. The model requires a very small amount of data (Recency and Frequency), compared to other models that require a large dataset (typically detailed individual-level characteristics, e.g., demographics) 2. The model has demonstrated robust out-of-sample validation 3. The model can be generalized to other types of behaviors; it is not excessively customized to the donation domain 4. The model can easily be implemented on Excel; it does not require any proprietary or specialized software Page 25

26 COMMENTS/QUESTIONS June (Wharton Exec Ed Workshop in San Francisco): Probability Models for Customer-Base Analysis Professor Peter Fader Wharton Customer Analytics Initiative Page 26

27 LAPSED OR DORMANT? THE IMPACT OF FUNDRAISING EFFORTS ON DONATION ACTIVITY Page 27

28 DATA Donations to a nonprofit organization, provided by the DMEF Sample of 21,166; 20% sample randomly selected 56 months of observations, 40 for calibration and 16 for holdout Page 28

29 AGGREGATE DONATION BEHAVIOR Page 29

30 AGGREGATE MAILING BEHAVIOR Page 30

31 WHAT DRIVES WHAT? Page 31

32 ADDING FUNDRAISING EFFORTS TO THE RECENCY-FREQUENCY DISCUSSION Recency-Frequency Information ID MARY ????? CHRIS ????? Fundraising Efforts ID MARY ????? CHRIS ????? Page 32

33 MODEL ILUSTRATION Active Active Active Active Active Active? Active? Page 33

34 ANALYSIS SUMMARY 3 model components o o o Latent lifetime Time between donations Donation amount Fundraising efforts can impact each component Time between donations and donation amount may be correlated Fundraising efforts may vary across donors Page 34

35 HOLDOUT DONATIONS $12, Donations in Holdout Period $10, Total Donations $8, $6, $4, Observed Donations Predicted Donations $2, $ Month of Holdout Period Page 35

36 IMPACT OF FUNDRAISING EFFORTS Positive impact on likelihood of donating while active Mixed impact on donation amount o Positive effect for most donors (~75%) o Insignificant effect for some donors (~25%) Mixed results on latent donor lifetime o o For most donors (~75%), positive effect For some donors (~25%), negative effect Page 36

37 RELATING INFERENCES TO RECENCY AND FREQUENCY 2 levels of recency: Did a donor contribute within the last 12 months? 2 levels of frequency: Has the donor made at least 2 repeat contributions o Non-recent and low frequency: 74% o Recent and high frequency: 13% o Non-recent and high frequency: 3% o Recent and low frequency: 9% Page 37

38 P(ALIVE) AND RFM Recency Recent Non-Recent Frequency Frequency High Low High Low avg=.91 avg=.88 avg=.56 avg=.17 Monetary Monetary Monetary Monetary High Low High Low High Low High Low avg=.90 avg=.92 avg=.84 avg=.90 avg=.51 avg=.58 avg=.16 avg=.17 Page 38

39 FUNDRAISING S EFFECTIVENESS BY RECENCY $ $ Expected Increase in Donations $ $ $ $80.00 $60.00 $ mailings $20.00 $ Months Since Last Donation Page 39

40 IMPACT OF FUNDRAISING AND RFM Recency Recent Non-Recent Frequency Frequency High Low High Low Monetary Monetary Monetary Monetary High Low High Low High Low High Low $85 $34 $78 $40 $44 $25 $16 $8 Average increase in expected contribution is $20.49 Page 40

41 BENEFITS OF A DATA-DRIVEN APPROACH TO DONOR MANAGEMENT The core of the analysis relies on readily available historic, individual-level data Incorporates the role of fundraising into both aspects of the donation behavior Recognizes the relationship between fundraising efforts and donation history Can be used to evaluate ROI of fundraising efforts Page 41

42 COMMENTS/QUESTIONS Contact: Professor David Schweidel University of Wisconsin Madison School of Business Page 42

43 COMMENTS/QUESTIONS THANK YOU! Wharton Customer Analytics Initiative Page 43