RFM segmentation
RFM segmentation groups customers by recency (when they last bought), frequency (how often) and monetary value (how much). It turns a customer list into states such as new, active, at risk and lost, so each state gets a different message.
What it means
RFM is the oldest useful model in retail because it needs only orders. A customer who bought last week, buys monthly and spends a lot is a different person from one who bought once, a year ago. The scores are simple, the groups are easy to explain to a team, and the states change as orders come in, so the segment a customer sits in today is not the one she sat in last month.
Why it matters for a store
Most stores send the same newsletter to every state. RFM lets the store do the obvious things it never gets round to: thank the best customers without a discount, remind the at-risk ones before they are lost, and stop paying to re-acquire people who bought last week.
An example
A supplement store marks customers who used to order every six weeks and have not ordered in nine as at risk. They get a replenishment reminder with their usual products; the active group gets nothing extra that week.
How the agent uses it
The agent keeps RFM states up to date from every order and uses them as inputs, together with predicted customer lifetime value, for retention journeys and for ads audiences. You can also build and save your own segments on the same data.
Related terms
See also Predicted customer lifetime value, Loyalty points, Dynamic coupon.
See which of last week's visitors you missed
Thirty minutes, with your store and ad accounts open. Then 30 days free. A holdout group decides: if the agent doesn't add orders in 30 days, you don't pay.
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