AI USE CASE
Café Lapsed Customer Win-Back
Automatically identify lapsed regulars and send personalised win-back offers that actually land.
What it is
Using data from a simple loyalty app, this use case flags customers who haven't visited in over three weeks and triggers a personalised 'we miss you' message with a tailored offer. Cafés typically see a 15–20% win-back rate on these campaigns, turning lost regulars into recurring revenue with minimal manual effort. Setup requires no engineering team — most loyalty platforms expose this logic out of the box or with basic configuration. A café with 500 active loyalty members could realistically recover 30–50 lapsed customers per campaign run.
Data you need
A loyalty app or POS system with at least 6 months of customer visit history and basic contact details (email or phone).
Required systems
- none
Why it works
- Automate the trigger so lapsed-customer messages go out every week without manual intervention.
- Use the customer's actual order history to reference a favourite drink or item in the message.
- Set a modest but compelling offer (e.g. 20% off next visit) rather than a freebie that attracts one-time deal-seekers.
- Track redemption by campaign to learn which message tone and offer type works best over time.
How this goes wrong
- Loyalty app has too few enrolled customers to generate statistically meaningful segments, making win-back lists tiny.
- Messages feel generic rather than personal, reducing open and redemption rates significantly.
- Offers are too generous (e.g. free item) and erode margin without recovering net-new spend.
- Campaign is run once and forgotten, rather than automated as an ongoing re-engagement trigger.
When NOT to do this
Don't build this if your café has fewer than 200 loyalty members enrolled — the lapsed segments will be too small to justify the setup effort and the results will be statistically meaningless.
Vendors to consider
Sources
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