How RFM self-adjusts as you sell better and more expensive products like subscriptions

A Repeat Customer Insights customer was asking about how the RFM model adapts to changes in the products offered as the store moves to more subscriptions:

So all of these [RFM scores] are relative to each other based on the ratings into 20% categories.

Does this mean that if we aim to extend our AOV and increase retention with subscription programs the model will automatically stretch the groups including whales as our AOV moves?

Yes. RFM doesn't use AOV but it uses the total spent by the customer which is better in this case (M is Monetary, aka total spent).

What'll happen with a subscription program is that each renewal will increase Recency (they just renewed today), Frequency (they ordered one more time), and Monetary (they have spent more).

Eventually the best subscribers will rise to the top scores and will show up in the better segments.

This will also push down customers with weaker signals (one-time, low total spent, etc).

Eventually as your best customers behave differently than your weaker ones, the spread in each group will be wider. e.g. a segment defined as $200 through $20 changing into a segment defined by $400 through $20.

This is one way RFM becomes self-adjusting as your store and customer behavior changes.

If you haven't installed Repeat Customer Insights yet, it's an easy way to get a detailed look at your customer behavior using RFM, cohort analysis, other algorithms.

Eric Davis

Use cohorts to find out who the best customers are in your Shopify store

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Repeat Customer Insights will automatically group your customers into cohorts based on when they first purchased. This will let you see how the date customers bought would impact their behavior.

Install Repeat Customer Insights for Shopify

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