Data Set Predictions
What happens when we want to go BEYOND period 2?
Application: customer lifetime value
Example case:
Organization: A public radio station supported primarily by contributions from its listeners
Challenge: Looking at listeners’ histories of whether or not they gave each year, what can we predict about their future giving patterns?
Focal donors:
Example 1: Question: Did they or didn’t they make a donation? (0 or 1)
Example 2:
Question: Who is likely to make more donations in the next 5 opportunities – Mary or Sharmila?
Answer: Mary à Sharmila has most recently not made a donation…..is she gone for good? Is she coming back? We do not know. Meanwhile, Mary has not donated twice but has come back both times.
Recency Vs. Frequency
What does it mean when there’s one or more “no donation” at the end of a sequence?
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
Example 3:
Question: Who is likely to make more donations in the next 5 opportunities – Mary or Chris?
Answer: Tie
Application: customer lifetime value
Example case:
Organization: A public radio station supported primarily by contributions from its listeners
Challenge: Looking at listeners’ histories 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
Example 1: Question: Did they or didn’t they make a donation? (0 or 1)
Example 2:
Question: Who is likely to make more donations in the next 5 opportunities – Mary or Sharmila?
Answer: Mary à Sharmila has most recently not made a donation…..is she gone for good? Is she coming back? We do not know. Meanwhile, Mary has not donated twice but has come back both times.
Recency Vs. Frequency
What does it mean when there’s one or more “no donation” at the end of a sequence?
- The donor lapsed (i.e., left the donor pool)
- The donor is dormant (i.e., decided not to give that year, didn’t think of giving, etc.)
- We don’t know, but can build a model to come up with a “best guess”
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
Example 3:
Question: Who is likely to make more donations in the next 5 opportunities – Mary or Chris?
Answer: Tie
