Singapore Reservation & Loyalty Industry Report 2025
What drives cancellations and repeat visits? New data from 731,927 reservations exposes the real revenue leak for restaurants.
What 731,927 Reservations Reveal About Demand, Cancellations and Customer Loyalty
Period analysed: 29 Nov 2024 – 29 Nov 2025
Sample size: 731,927 dine-in reservations across Singapore
Reservations with identifiable customers: 683,925
Reservations with capacity data: 138,781
From the Desk of Jonathan Lim, Founder & CEO, Oddle
If you’ve been in the F&B industry long enough, you’ve probably heard — or said — some version of this:
“Customers these days are irresponsible.
Cancellations are out of control.
We need deposits or we’ll lose money.”
These sentiments didn’t come out of nowhere. They were shaped by a very specific period: the post-COVID reopening, when Singapore went through a wave of revenge dining. Demand was extreme. Everyone was desperate to dine out. Tables were scarce, waitlists were long, and every cancellation felt like a painful missed opportunity.
In that environment, demonising cancellations made sense.
It was emotional, but it was understandable.
But the industry is no longer in that period.
And the data today tells a very different story — one that deserves a sanity check.
Over the last 12 months, we analysed a sample of 731,927 dine-in reservations across Singapore — and I want to emphasise that this is only a sample of the data we see across our broader ecosystem.
What we found surprised even us:
- Cancellations almost never happen when the restaurant is full.
- Only 0.10% of cancellations occur at or above 80% capacity.
- The financial impact of cancellations is far lower than the emotional impact.
- The real leak is not cancellations — it’s retention.
More than 85% of reservations in our sample came from first-time diners.
Only 10.66% returned within a year.
This is the real revenue challenge in Singapore dining:
We serve thousands of new customers every week, but very few come back unless we build systems that bring them back.
Cancellations aren’t a moral failing of customers.
They’re a normal feature of demand volatility.
The real question isn’t “How do we punish cancellations?”
It’s “How do we make it easy for customers to book, show up, and come back again?”
This report exists to give restaurant owners clarity — to separate feelings from facts, to recalibrate reservation strategies based on real data, and to recognise that demand management, not deposit policies, is the real key to revenue growth.
1. The State of Reservations in Singapore
1.1 Reservation & Cancellation Overview
| Metric | Value |
| Total reservations | 731,927 |
| Total cancellations | 138,858 |
| Cancellation rate | 18.97% |
| Last-minute cancellations (≤2 hours) | 38,310 |
| % of cancellations that are last-minute | 27.59% |
What this means
Roughly 1 in 5 reservations is cancelled — normal for an urban market with high spontaneity and strong walk-in culture.
But cancellation volume is not the problem.
Timing and demand conditions determine revenue impact.
2. Do Cancellations Actually Hurt Revenue?
2.1 Cancellations vs capacity (the real revenue question)
| Capacity status at cancellation | Cancellations | % of cancellations |
| Timeslot ≥80% capacity | 135 | 0.10% |
| Timeslot <80% capacity | 138,646 | 99.90% |
| Total | 138,781 | 100% |
What this means
A cancellation matters only if:
- The restaurant is full or nearly full, and
- There is demand to fill that table.
This scenario almost never happens.
So for 99.9% of cancellations, the restaurant still had spare seats.
Meaning: there was no lost revenue to protect in the first place
3. The Real Issue Isn’t Cancellations — It’s Demand & Retention
3.1 First-time vs repeat reservations
Out of 683,925 identity-known reservations:
| Customer Type | Count | Share |
| First-time customers | 585,740 | 85.6% |
| Repeat customers | 98,185 | 14.4% |
What this means
Restaurants overestimate how many “regulars” they have.
The data shows the opposite: most bookings are from first-timers.
This makes post-visit systems (reminders, follow-ups, reactivation) far more critical than cancellation policies.
3.2 Return after first reservation
| Metric | Value |
| Unique first-time customers | 573,335 |
| Returned at least once | 61,093 |
| Return rate | 10.66% |
What this means
Only 1 in 10 customers returns after their first reservation.
The true revenue leak is not cancellations — it’s the absence of structured retention.
4. Who Cancels? First-timers or repeat customers?
4.1 Cancellation mix & rates
| Customer Type | Cancellations | Cancellation Rate | Share |
| First-time customers | 114,728 | 19.6% | 82.6% |
| Repeat customers | 24,234 | 24.7% | 17.4% |
What this means
- First-timers contribute most cancellations because they dominate demand.
- But repeat customers cancel more often (24.7% vs 19.6%).
Behavioural inconsistency is universal, not limited to new diners.
Deposits won’t “fix” this.
Systems will.
5. When Do Cancellations Happen? (By Party Size & Daypart)
5.1 By party size
| Covers | Cancellation Rate |
| 1 | 21.9% |
| 2 | 18.2% |
| 3 | 17.3% |
| 4 | 19.0% |
| 5–6 | 20.2% |
| 7–8 | 20.9% |
| 9–12 | 21.4% |
| 13+ | 20.8% |
What this means
Cancellation rates across group sizes are tightly clustered between 17–22%.
There is no specific “problem segment.”
This volatility is normal and stable, and should be managed — not overreacted to.
5.2 By weekday/weekend & daypart
| Day Type/Daypart | Cancellation Rate |
| Weekday lunch | 19.5% |
| Weekday dinner | 18.1% |
| Weekend lunch | 19.7% |
| Weekend dinner | 19.5% |
What this means
Behaviour is consistent across the week.
The myth that “weekends are worse” comes from higher volume, not worse behaviour.
5.3 Lead Time by Group Size (How Far in Advance Customers Book)
We analysed how early different group sizes make reservations, using lead-time buckets ranging from <1 day to 60+ days.
Small groups (2 pax)
| Lead Time | Share |
| <1 day | ~57% |
| ≥1 day | ~43% |
Most 2-pax bookings are spontaneous.
They value ease of booking and low friction.
Mid-sized groups (5–6 pax)
| Lead Time | Bookings |
| <1 day | 49,091 |
| 1–2 days | 16,775 |
| 2–4 days | 16,801 |
| 4–7 days | 13,782 |
| 7–14 days | 11,669 |
| 14–30 days | 7,341 |
| 30–60 days | 1,658 |
| 60+ days | 286 |
Only ~40–45% book same-day.
The rest plan ahead.
Large groups (9–12 pax)
| Lead Time | Bookings |
| <1 day | 9,364 |
| ≥1 day | ~30,000 |
Only 1 in 4 is spontaneous.
Most are organised.
Very large groups (13+ pax)
| Lead Time | Bookings |
| <1 day | 1,781 |
| 7–14 days | 2,348 |
| 14–30 days | 2,174 |
| 30–60 days | 968 |
| 60+ days | 377 |
Planning peaks in the 7–30 day range.
These are advance, intentional bookings.
What this means
Different group sizes behave differently — and restaurant policies should reflect that.
1. Small tables → spontaneous demand
Most 2-pax bookings happen within the day.
Deposits create unnecessary friction and reduce conversion.
2. Large tables → planned, intentional demand
Most 8+ pax bookings are made a week or more ahead.
These bookings carry higher operational risk and justify selective friction (card holds, deposits).
3. Reminder strategies should differ
- Small tables → short-window reminders
- Large tables → multi-stage reminders (7 days → 3 days → same-day)
4. Demand forecasting becomes more accurate
Lead time gives restaurants visibility on expected load and staffing needs.
This is the essence of demand management:
Policies shaped by actual behaviour, not emotion or anecdotes.
6. After Cancellations & No-Shows: Do Diners Return?
| Segment | Unique Customers | Returned | Return Rate |
| After cancellation | 123,066 | 17,090 | 13.89% |
| After no-show | 59,675 | 3,993 | 6.69% |
What this means
A cancellation is not a lost customer — but most disappear unless you re-engage them.
A no-show is worse: fewer than 1 in 15 return.
The right move isn’t punishment.
It’s timely reactivation.
7. The Strategic Role of Friction: When Deposits Make Sense (and When They Don’t)
Deposits and card holds protect only the 0.10% of cancellations that occur under true pressure.
But friction reduces conversion for the entire funnel.
Use friction only when:
- The restaurant is often full
- There is a waitlist
- It’s a high-value slot (e.g., NYE, CNY)
- It’s a large group with higher risk
- Staff cost and prep cost are high
Avoid friction when:
- You are not full most of the week
- Most bookings are 1–2 pax
- Small tables dominate your business
- Walk-in behaviour is strong
- You rely heavily on first-time demand
What this means
Reservation policies should reflect demand patterns, not frustration.
Deposits are a scalpel, not a hammer.
8. Practical Playbooks (What Restaurants Should Do Now)
✔ Send confirmations & reminders
Reduces no-shows more effectively than penalties.
✔ Capture diner identity
Critical for retention and return behaviour tracking.
✔ Follow up after first visits
Boosts the most important metric: return-after-first.
✔ Reactivate cancellations & no-shows
Gentle nudges convert some of the 86–93% who otherwise disappear.
✔ Track the right metrics
- Return-after-first
- Return-after-cancellation
- Return-after-no-show
- Show-up rate
- Booking lead time
- Demand patterns by group size
What this means
Growth comes from systems, not policies.
From demand management, not punishment.
9. Final Word
Across a sample of 731,927 reservations, one message is clear:
Cancellations are normal.
Retention is weak.
Demand patterns are predictable.
And deposits are not the universal solution the industry believes they are.
The restaurants that thrive will be those that:
- Reduce friction
- Anticipate demand
- Communicate proactively
- Build strong customer databases
- Treat every first-time diner as the start of a relationship
This is what modern, data-driven demand management looks like.
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