RFM segmentation: definition and use in a computer club
RFM segmentation — method for dividing a club’s customer base
Section titled “RFM segmentation — method for dividing a club’s customer base”RFM segmentation — a method of dividing a customer base into groups by three parameters: Recency (time since the last visit), Frequency (visit frequency), and Monetary (total revenue). Allows working with each segment separately instead of running one promotion for everyone.
Definition
Section titled “Definition”RFM is built on a simple idea: customers who visited recently, come frequently, and spend a lot behave differently from those who visited long ago, rarely, and spent little. Mixing them in one promotion wastes resources.
Three dimensions:
| Parameter | What it measures | How it is used |
|---|---|---|
| R — Recency | Days since the last visit | Fewer = better; high R = dormant |
| F — Frequency | Number of visits in the period (e.g., 90 days) | More = more loyal |
| M — Monetary | Total revenue over the period | More = higher value customer |
Each player receives a score on each axis (typically 1–5 or 1–3), and from the combination of scores their segment is determined.
Typical segments for a club
Section titled “Typical segments for a club”| Segment | R | F | M | What to do |
|---|---|---|---|---|
| Champions | High | High | High | VIP privileges, early access, retention |
| Loyal | Medium | High | High | Appreciation, loyalty program |
| Promising | High | Medium | Medium | Onboard into multipass, first bonus |
| At risk | Medium | High (formerly) | High (formerly) | Reactivation, “we miss you” offer |
| Dormant | Low | Medium | Medium | Reactivation campaign |
| Lost | Low | Low | Low | Minimum effort, only if cheap |
Where it is used in IZI
Section titled “Where it is used in IZI”In IZI the Analytics → Customers section shows basic activity segments: new, active, dormant, lost. This is a simplified RFM slice by Recency and partially Frequency.
A full three-dimensional RFM model is built from customer data exported from IZI, with scores assigned on each axis and grouping applied.
How to build RFM for your club
Section titled “How to build RFM for your club”Step 1. Define the analysis period (typically 90 days).
Step 2. For each player calculate:
- R = days since last visit (fewer = better → high score)
- F = number of visits in the period (more = better → high score)
- M = total revenue in the period (more = better → high score)
Step 3. Divide players into quintiles on each axis and assign scores 1–5.
Step 4. Group players by score combination into segments (e.g., R=5 F=5 M=5 = champion).
Step 5. For each segment — a separate communication mechanic and offer.
Why this matters
Section titled “Why this matters”Without RFM a club addresses its entire base the same way — wasteful and ineffective:
- Best customers need attention and exclusivity, not a “discount for everyone”
- Dormant customers need a specific reason to return, not a generic mailing
- New customers need onboarding into the loyalty program while they are still warm
ARPU and LTV are not equal across the entire base — they vary greatly by segment. RFM makes that difference visible and manageable.
Common mistakes
Section titled “Common mistakes”- Building RFM once and not updating — segments change every month; static segmentation becomes stale
- Treating “dormant” and “lost” customers the same — a dormant customer was active and can be brought back; a lost customer is already gone, reactivation cost is higher
- Ignoring M when working with “dormant” customers — a dormant customer with high M (valuable customer, hasn’t visited in a while) has higher reactivation priority than one with low M
Related
Section titled “Related”- Customer segments — basic segmentation principles
- Cohort — analysis by first-visit groups
- Churn — RFM helps identify customers on the verge of churning
- LTV — the M parameter in RFM is a proxy for LTV over the analysis period
- ARPU — declining ARPU is often visible as a shift in the RFM structure of the base
- Reactivating dormant customers — scenario for the “at risk” and “dormant” segments
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Frequently asked questions
What is RFM segmentation?
RFM — a method of dividing the customer base into groups by three parameters: Recency (time since the last visit), Frequency (visit frequency over a period), Monetary (total revenue from the customer). Each customer receives a score on each axis, and from the three scores their segment is determined.
What segments does RFM analysis produce?
Typical segments: Champions (high R, F, M — best customers), Loyal (high F and M, medium R), Promising (high R, medium F and M — new with potential), Dormant (low R with decent F — left but were active), Lost (low R and F).
How is RFM used in a computer club?
RFM allows the club to avoid running one promotion for the entire base and instead address each segment specifically: VIP privileges for champions, a reactivation offer for dormant customers, onboarding into the loyalty program for promising customers.
Where can I see RFM data in IZI?
In Analytics → Customers, IZI shows activity segments: active, new, dormant, lost. This is a simplified RFM slice by Recency and partially Frequency. A full RFM model is built from exported customer data.
How often should RFM segmentation be updated?
For a club with regular traffic (from 100 active players per month) — monthly. Segments change: a promising customer may become loyal, a loyal customer may become dormant. Static segmentation quickly becomes stale.