Understanding your metrics.

A single 0–100 number that rolls up recency, frequency, spend, and momentum into one read on how a customer is trending. Higher is healthier.

It is a composite of the signals below — not a separate measurement. Use it to sort a list quickly; open the underlying signals before you act on any one customer.

The modeled likelihood that a customer places another order within their expected window, scored on your store's own purchase history.

Read it as a ranking instrument, not a promise. A customer at 0.72 is more likely to come back than one at 0.31; neither number is a guarantee about any single person.

Across the stores we have measured, a meaningful share of lapsed customers come back on their own — roughly 2.7% in a given window — with no message sent at all.

This floor is why we hold out a control group before crediting any win-back or overdue message. A campaign only counts as working if it beats the return rate of customers who got nothing. See recovered revenue below.

The date range, not a single day, in which a customer is expected to order again — derived from their own cadence and the product's replenishment pattern.

It widens for irregular buyers and tightens for consistent ones. When a customer passes the far edge of their window without ordering, they move toward overdue.

The typical interval between a customer's orders — for example, every 34 days. It is the backbone of the next-order window and of overdue detection.

Whether a customer's cadence is drawn from their own repeat history, borrowed from the product's typical replenishment interval, or from a blend of both.

We label the source so you know how much personal history sits behind an estimate. A customer on their second order leans on the product's cadence; a customer on their tenth leans on their own.

The typical interval at which a consumable product is bought again across all customers — a 60-day coffee bag, a 90-day supplement. It anchors cadence for customers who do not yet have enough personal history.

The stretch after a customer's first purchase when the second order is most likely — and most fragile. Converting a first-time buyer into a repeat buyer is the single largest lever on retention.

In our backtests, predictive flags acting inside this window showed a 3.2× lift over baseline, which is why Retrics watches it closely.

A ranking of customers by their spend with you — top, middle, and emerging tiers. It answers "how much is this relationship worth so far?" and is measured, not modeled.

Where a customer sits in their journey with you: new, active, at risk, lapsed, or recovered. Stage is driven by recency against the customer's own expected window, so an "active" cadence-buyer and a dormant one-timer are treated differently.

The classic segmentation trio: how recently a customer bought (Recency), how often they buy (Frequency), and how much they spend (Monetary). Each customer gets a score on all three.

RFM is transparent and battle-tested. Retrics uses it as a readable foundation and layers the predictive models on top, rather than replacing it with a black box.

A projection of how much a customer is likely to spend with you over their lifetime, modeled from their behavior so far and the patterns of similar customers.

It is explicitly a forecast. We label it modeled so it is never confused with realized spend. Treat it as a planning input, not a booked number.

Customers grouped by the month of their first order, then tracked to see what share keeps buying in each following month. It shows whether the customers you are acquiring now retain better or worse than those from a year ago.

The percentage of customers who place a second order — the most direct measure of whether your product earns a habit. Retrics tracks it overall and by cohort so improvements are attributable.

The expected value of customers who are drifting toward lapse — their forecast value weighted by how likely they are to leave. It sizes the opportunity in front of you so you can prioritize.

It is a modeled figure, useful for ranking where to spend attention. It is not money you have lost, and not money you are guaranteed to keep.

Revenue from customers who came back after receiving a Retrics message. It is easy to compute but it credits the message with every return — including the ~2.7% who would have come back anyway.

We show it plainly labeled as observational so it is never mistaken for proof that the message caused the return.

The revenue that a campaign returned above and beyond a held-out control group who received nothing. This is the honest number: it subtracts the natural-return floor and credits only the lift the message actually caused.

Causal measurement requires a holdout at send time, so this figure appears once a campaign has run with a control group. When both are available, trust the causal number.