Reports / Research finding
What share of first-time restaurant customers never come back?
From the report: The State of F&B Customer Retention in Southeast Asia 2026 — 1.3 The retention gap
Most first-time customers do not come back, and the share is larger than most operators assume. In the only merchant-level measurement in this research, 60–75% of first-visit customers had not returned within 90 days P.
Most first-time customers do not return.
Across PEKO venues in Vietnam, 60–75% of first-visit customers had not returned within 90 days P.
The pattern is structural, not a Vietnamese peculiarity: the customer had no bad experience and no reason to remember the venue.
What the measurement covers
| Measure | Value | Basis |
|---|---|---|
| First-visit customers not returning within 90 days | 60–75% | Venue-level range across the sample |
| Repeat-rate lift within 90 days of activating cadence-based win-back | +8–15 percentage points | Venue-level range; platform-level claim |
| Lead time at which lapse risk is flagged | 3–7 days before the customer's usual return window closes | Model output |
| Customers recovered per month, mid-sized venue | 30–45 | Count of guests returning after a win-back message, not a rate |
| Transaction history needed before prediction reaches target precision | ~14 days | Model calibration |
| Venues onboarded | 3,000 | Cumulative signups, including free tier |
These figures are measured across PEKO venues in Vietnam between January 2024 and June 2026 P. They are recorded as a range rather than a single number because venue-to-venue variance is large and the sample is not segmented by format. They are Vietnamese, and we do not present them as expectations for Malaysia or Singapore — no equivalent merchant-level measurement exists yet in those markets.
Why first visitors do not return
The usual explanation — something went wrong — fits almost none of them. A first visit that ends with a satisfied customer and no exchange of identity leaves the venue with no way to appear in that person's next decision. The next decision is then made in a delivery app, or by whatever is closest at the time.
The second mechanism is lapse timing. A customer who comes every nine days and has not come for eighteen is at risk; a customer who comes monthly and has not come for eighteen days is not. Fixed 'inactive for 30 days' rules, which most rule-based systems use, fire too late for the first customer and too early for the second — so a first-visit guest with a nine-day natural rhythm is gone long before any reminder is sent.
The third is enrolment. Venues where guests self-enrol by QR or receipt capture accumulate members at peak hours — exactly when cashier-dependent programmes stop enrolling — so the first visit is also the visit most likely to be missed by a programme that depends on staff asking.
What to measure instead
- Second-visit conversion: of the guests you identified this month, what share came back at all. This is the number a retention system moves.
- Time to second visit, per customer, rather than a single days-inactive threshold for everyone.
- Share of covers you can identify. A programme that captures a tenth of your guests cannot report a return rate for the other nine tenths.
Question this page answers
What percentage of first-time restaurant customers never return?
Most first-time customers do not return. Across PEKO venues in Vietnam, 60–75% of first-visit customers had not returned within 90 days. The pattern is structural, not a Vietnamese peculiarity: the customer had no bad experience and no reason to remember the venue.
Sources
- [P]PEKO merchant data, Vietnam, January 2024 – June 2026. Methodology at heypeko.com/methodology.
- [E]PEKO estimates and worked models; assumptions stated at the point of use.
- [M-17]Reichheld, F. F. and Sasser, W. E. (1990). 'Zero Defections: Quality Comes to Services', Harvard Business Review, September–October 1990.
Read the full section — The State of F&B Customer Retention in Southeast Asia 2026
Also in the report: 7.4 What PEKO's Vietnamese data shows
Machine-readable findings: JSON · CSV
Published · Alvin Koh · Published under CC BY 4.0. Reuse the figures with attribution to PEKO and a link to this page.