Orbit web apps
Orbit web apps
Score a customer on recency, frequency and monetary value, name their segment, and get the lifecycle move it calls for. Runs entirely in your browser.
Days since the last order. Fewer days scores higher.
Number of orders in the window. More orders scores higher.
Total or average order value. More value scores higher.
The four cut points that turn a raw value into a 1–5 score. These defaults are indicative — the right numbers are the quintile boundaries of your own customer base. Recency is in days (fewer is better); monetary is in your own currency.
How the segment is chosen
Recency, frequency and monetary are each scored 1–5. Frequency and monetary are averaged into a single FM score, and the named segment is read off the standard Recency × FM grid — the same eleven-segment map used across the industry. Pair it with LTV to size what each segment is worth before you spend on winning it back.
Enter recency, frequency and monetary to score a customer and name their segment.
Segment names follow the standard eleven-segment RFM model. Scores depend entirely on the thresholds — treat the defaults as a starting point, not a verdict.
RFM is the oldest segmentation in direct marketing because it works with data every business already has: when someone last bought, how often they buy, and how much they spend. The trick is not the scoring — it is turning a three-digit cell into a decision, and doing it on thresholds that reflect your own customers rather than a blog post's.
The standard method sorts your customer base into five equal groups on each dimension and scores them 1 to 5. Recency is scored in reverse — a customer who bought yesterday is a 5, not a 1 — because for recency, lower is better. Frequency and monetary run the natural way: more is a higher score.
The reason quintiles matter is that a threshold is only meaningful relative to your base. A 90-day recency is excellent for a furniture retailer and terrible for a coffee subscription. The defaults in this tool are indicative starting points so the page is useful before you have your numbers; the moment you can, replace them with the actual quintile boundaries of your customers and the segments start describing your business instead of a generic one.
A full 5x5x5 grid has 125 cells, which is more than any team can assign an action to. The common simplification — and the one this tool uses — is to average the frequency and monetary scores into a single FM score, leaving a 5x5 Recency x FM grid of 25 cells that maps cleanly onto eleven named segments.
That collapse is deliberate: recency is the strongest single predictor of whether a customer will respond, so it keeps its own axis, while frequency and monetary — which tend to move together — share one. A customer who orders often but spends little and one who orders rarely but spends a lot both land mid-scale, which is usually the right call for a first pass.
Champions bought recently, buy often and spend the most — reward them and turn them into advocates, do not discount to them. At Risk customers spent big and often but have gone quiet; they are worth a real, personalised win-back because the value at stake is high. Hibernating and Lost customers are low-value and long gone — a light reactivation or an honest final email, then suppression, not another full campaign.
The failure mode is treating the whole base the same. RFM earns its keep by telling you where not to spend as much as where to: the same £5 incentive is a waste on a Champion, a reasonable bet on an At Risk account, and a poor use of budget on a Lost one. Suppressing unresponsive contacts also protects deliverability, which quietly improves every other send.
RFM is descriptive, not predictive, and it is blind to everything outside the three inputs. It does not know product margin, acquisition channel, tenure, support burden, or whether a big monetary score came from one returned order. Two customers in the same cell can be worth very different amounts once margin and returns are accounted for.
Use it as the first cut, not the last word. Pair the segment with lifetime value to size the opportunity, and with a churn or engagement signal before you act on the At Risk and Can't Lose groups — those are the segments where being one quarter late is the difference between a win-back and a goodbye.
Built into Orbit
Orbit's segmentation tooling runs RFM against your real customer table, derives the quintile thresholds from your own data rather than defaults, and turns each segment into a Braze-ready audience with the lifecycle action attached. Claude can score the base, name the segments and draft the win-back for the ones that need it.
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