While Average Customer Purchase Latency is much better at predicting defecting customers, it can be difficult to calculate unless you use software like my app.
An alternative is to use RFM. It's a bit easier to calculate (the core is three simple sorts) and much easier to understand. RFM also gives you a lot more analysis options than Average Latency.
Using RFM for defection detection is weaker than Average Latency because RFM is more of a relative measurement (e.g. customer A is more defected than customer B) while Average Latency is more absolute (e.g. customer A has defected).
But not every system calculates Average Latency but many calculate RFM. Even old-school paper catalog marketing systems use RFM in their customer analysis. To use RFM for defection you'll want to set it up like so:
- count customers with a Recency of 1 as fully defected
- count customers with a Recency of 2 as potentially defected
- count customers with a Recency of 3 as borderline defected
Using this criteria, you'd want to use your anti-defection campaigns on customers with a Recency of 2.
(You could use a win-back campaign on Recency 1 customers but that's a different conversation)
Learn what your customers are actually doing instead of just guessing
One of the best ways to build a sustainable business starts by getting your customers to come back. Mastering that simple process can be difficult, but builds a lifelong business.
Repeat Customer Insights can help you understand your customer's behavior. With its collection of behavior reports, you can see what they're actually doing instead of guessing and having your efforts fall flat.