The advantage of working on the 'agency' side is that we can see across several different industries. When comparing how frequently a customer shops across categories one can get a sense of the range of possibilities.
The following chart plots the 'depth of repeat' curve for several different industries - from fast moving retail to 'once in a blue moon' service organizations. To make comparative statements, each was normalized to the first visit, i.e. 100%.
- The drop from 1st to 2nd purchase is a steep fall off for all five examples. This suggests a strong "Thank You" program to help consumers decide to come back.
- There is a range of transactions where consumers need to be "Encouraged" to return. The group of customers with 2-5 transactions may need to be treated to a different set of offers.
- Loyalty can be operationally defined at the point at which the odds of coming back are greater than some point on the curve, e.g. 50%. These few, rare individuals need to be "Rewarded"
But can this information be predicted based on knowledge of past behavior? In a word: "Yes"
Here is the customer profile for one of the auto/service companies; it shows the number of consumers by number of repeat transactions over a two-year period against the estimated count of customers. The estimated number of repeat transactions are based on a combination of Recency and Frequency type information alone.
While possibly not as valuable as a predictive model due to the lack of explanatory variables; this does provide a benchmark method of defining what the expected number of purchases a person will have. Against this backdrop we can see if direct-to-consumer programs move the needle.