The Curve: When to thank, encourage, and reward

What can we learn from transaction history?

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%.

There are several points to be gleaned from the chart based on looking at the odds of coming back for one more visit.

• 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"

To a large degree the shape of the curve is driven by purchase cycle.  For instance, the average interval for the retail company is much shorter than the Service and Auto 2 companies.

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.