How to do RFM analysis without waiting on the data team
The short answer
To do RFM analysis, score each customer from 1 to 5 on recency, frequency and monetary value: how recently they bought, how often and how much they spend, across all channels. Combine the scores into named segments such as Champions, loyal, at risk and lapsed. With shared definitions, marketing can build the list itself.
Why it matters
Every segment request that goes through the data team waits in a queue, and marketing's campaign waits with it. A shared RFM definition that marketing can apply directly frees analyst time and gets lists out the same day.
The question to ask
Copy this into Lexi as written.
How to work it out
1. Score each dimension. Rank customers into five equal groups on days since last purchase, number of orders and total spend over two years.
2. Name the segments. Map score combinations to segments, such as recency 5 with frequency and monetary 4 or above as Champions.
3. Publish the rules. Write the scoring and segment rules where marketing can use them directly, so lists match every time.
What the answer looks like
Illustrative output on sample data
Illustrative example: how Lexi answers this for a jewelry retailer. It scores 180,000 customers on two years of purchases using shared recency, frequency and monetary (RFM) rules.
- Champions: recency 5, frequency and monetary 4 or 5
- Loyal: recency 3 or 4, frequency 4 or 5
- At risk: recency 2, frequency 3 or more
- Lapsed: recency 1
What the data shows
Marketing asked for frequent, high-spend customers. Under these rules that is Champions plus Loyal, 24,500 customers.
What it means
With shared rules, the segment is ready the same day and matches what other teams use.
What to do
Send Champions and Loyal the new collection preview this week.
Want me to push the list to Klaviyo?
Frequently asked questions
What segments does RFM segmentation usually produce?
Common groups include Champions, who bought recently, buy often and spend the most; loyal customers; promising new customers; customers at risk, who used to buy often but have slowed; and lapsed customers. Names vary, but each group needs a different message, offer and channel.
Why do different teams get different RFM lists?
Each team scores over a different window, uses different cut-offs or leaves out store purchases. The same customer can then be a Champion in one list and at risk in another. Agreeing one window, one set of cut-offs and one customer view fixes it.
Is self-serve segmentation safe to hand to marketing?
Yes, when the definitions are shared and governed. The data team sets the scoring rules, customer matching and exclusions once, and marketing applies them. That keeps lists consistent while removing the queue, and the data team can still audit how each list was built.