Churn prediction reads the change in each customer’s own purchase behaviour — visits spacing out, basket falling — and flags the risk while it is still forming. What the model needs, how Retano CRM & Loyalty prioritises the at-risk list by customer value, and what to send the people on it.
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In short
- Churn prediction identifies active customers whose behaviour shows the patterns that usually precede a customer drifting away.
- Retano CRM & Loyalty builds the model on receipt data the loyalty programme already collects. No new data sources are needed.
- Around eight months of usable purchase history are required before a first model can run.
- Each customer gets a churn probability and a customer lifetime value. Crossing them shows where the retention budget is worth spending.
- A dedicated win-back programme is configured for each value segment, and segments are exported straight into campaigns.
- The decision stays with the retailer: the platform produces segments and recommendations, campaigns run on rules set by marketing.
Why doesn’t a loyalty programme flag customer churn?
Because points, reward thresholds and cardholder prices answer one question — how to reward the customer who buys — and not the other: who has started buying less, and since when. Loyalty reporting is normally retrospective and aggregate, which is a different thing from knowing that this particular person is likely to stop buying over the coming months.
Winning a regular customer back later costs more than keeping them would have. This is the space predictive models occupy in Retano CRM & Loyalty: the same mechanics, aimed on the basis of what purchase data says about each individual.
What data does churn prediction need?
Receipt data linked to a loyalty card: which references, in what quantities, in which store, at what time, with what discount. The chain already records it every day. From this history Retano CRM & Loyalty computes a set of indicators for each customer:
- Days since the last purchase
- Number of weeks with purchases, total purchases and total spend
- Average visit frequency over rolling windows of 3, 5 and 13 weeks
- Average basket over the same windows
- Average number of references per receipt
- Average discount per receipt
The difference from a plain purchase history lies in the rolling windows: comparing the last three weeks with the last thirteen means reading a direction of travel, not a snapshot. The windows are configurable and are set according to how often customers shop in that format.
How is the customer base segmented?
Retano CRM & Loyalty groups customers into clusters of homogeneous behaviour, with several logics available in parallel: classic RFM, RFM built on deviations from averages, a machine-learning variant, a BCG matrix applied to the customer base, and groupings by consumption pattern. The resulting segments are exported straight into the platform as campaign target groups.
The predictable families emerge — the frequent full-price buyer, the stable customer with a regular basket, the occasional visitor, the new member, the one-time buyer — along with one that deserves separate handling: the customer who buys almost exclusively on promotion. While that cluster stays mixed into the rest of the base, promotional performance is unreadable, because the same mechanic produces opposite effects on the two groups. Separated, they point to two different instruments.
How does the churn model work?
It is a supervised learning problem. More than four months of past data are used to generate the predictors and the following four months to establish who actually stopped buying, which is what the model is trained on; in practice around eight months of usable history are needed before a first model can run. Since grocery retail has no contract to cancel, a customer counts as lost when no purchase is recorded over the observation period that follows.
The markers are the ones no one can follow by eye across millions of receipts: visits spacing out, average basket and total spend falling, fewer references per receipt, a changing discount profile. The output is the customer who is slowing down while the risk is still forming — that is, while intervening still makes sense. The probability threshold is configurable: the chain decides how cautious to be, moving the line between acting and letting go according to the retention budget available.
Which at-risk customers are worth acting on?
Those where a high probability meets a high value. Retano CRM & Loyalty assigns each customer both a churn probability and a customer lifetime value, and distributes the base into value segments. Crossed, the two produce a value matrix: probability says who is leaving, value says how much it is worth spending to prevent it. A high-value customer at high risk justifies immediate contact and a tailored offer; someone who bought once and never returned deserves a low-cost attempt, a standard coupon or a notification. A dedicated win-back programme is configured for each value segment.
Gianni Cassano, Country Manager, Retano Solutions:
“Loyal customers are what makes revenue stable. A chain can invest heavily in attracting new customers while quietly losing its regulars — and winning them back afterwards costs far more. For us loyalty does not end with the mechanics: it starts there, and with the data those mechanics bring with them.“
Which customers the model works on is a choice, not a given. The models are computed on a segment, and segments are built from customer attributes, so the retailer can restrict the analysed population to the one it intends to include — for example to customers who have given the corresponding consent.
What do you send a customer who is slowing down?
An offer built on what has changed in their behaviour. What does not work is the undifferentiated version: the same discount sent to the whole base, including the customers who were going to buy anyway. The classic mechanics work better when the choice follows the profile:
- A category has disappeared — concentrate the benefit there.
- Frequency is falling — a coupon earned today and spent on the next visit.
- Points are about to expire — a reminder is often enough.
- A birthday, a favourite category, a referral — mechanics the customer already knows.
Above these levers sits one more: the personalised product offer, built from the baskets of comparable customers. It is what gives a reason to return to the customer a generic discount no longer moves — the offer lands on a product the person has never bought but people who shop like them buy regularly. On how the model picks those products, read more here.
One more thread runs alongside this work. Association analysis identifies driver products: references that sell few units but pull the rest of the basket with them. For anyone working on churn they are useful twice over — as a pretext for a reactivation campaign, since their buyers can be extracted as a segment and given a coupon on that very item, and as natural candidates for a personal offer. It is a subject of its own, and it gets its own entry.
Does the system decide on its own?
No. Gianni Cassano, Country Manager, Retano Solutions: “We are often asked whether the system decides everything. It does not. What it removes is a different burden: the effort of analysing millions of receipts, the manual work of building segments, the cross-referencing of separate reports to reconstruct a single picture. A campaign can run automatically, but on rules defined by marketing.“
What actually changes
Profiling, churn prediction and the association map affect daily work far more than they affect the loyalty programme itself. The mechanics stay the ones customers already know. What changes is the addressee: the chain knows who it is talking to, how long that person has been slowing down, what they are worth keeping, what to offer them and which reference can pull another one into the basket. The data needed already exists — it is in the receipts recorded every day. The competitive difference is in reading it and turning it into action.
