03 · UNDERSTAND THE PATTERN
See what keeps going wrong—not just what happened once.
GuestSence keeps what guests actually said separate from the AI interpretation, so you can see whether the real issue is the wait itself, the uncertainty around it, or something else.
Harbour Table Kitchen
READ-ONLY SAMPLEGuestSence AI Analysis
Wait-time updates during busy service
strong evidence8 related responses
Updated 13 Oct 2026
AI Assessment
AI interpretation: the remaining wait-time problem appears to involve uncertainty during busy service, not only the absolute length of the wait. Several related guests describe missing or changing updates.
Sample AI analysis
Private to your restaurant · Guest email addresses, phone numbers, payment-card patterns and URLs are redacted from feedback text before AI processing.
OpenAI API data is not used to train OpenAI models by default. · Privacy details
Human decision required
What guests are actually saying
Guests describe missing or changing wait-time updates during busy service.
Recommended Test
Proactive wait-update trial
For the next two busy dinner services, give waiting guests an initial estimate and proactively update them whenever that estimate changes materially.
Several related guests describe uncertainty or missing updates, not simply a long wait. This tests the communication hypothesis before adding ongoing staffing cost.
- Give the guest an initial wait estimate when they join the queue.
- Make one floor team member responsible for updates during the busiest period.
- Proactively update the guest when the estimate changes materially.
- Record the change in GuestSence and compare new wait-related feedback.
14-day test · low effort
How GuestSence Will Measure It
Primary metric
Wait-time mention rate
Direction: decrease
Secondary metric
Wait-time mentions
Direction: decrease
GuestSence keeps the official before-and-after calculation deterministic.
View full AI analysis
Full analysis
What we know
8 guest responses mention wait time.
Official value: 8
Wait-time feedback represents 16% of this sample.
Official value: 16
The sample restaurant has a completed wait-time improvement result.
Official value: improved; before 9/25; after 3/25
What may be happening
Uncertainty during the wait
medium confidenceSeveral guests describe missing, changing or delayed updates during busy service.
Evidence for
- 8 guest responses mention wait time.
- Wait-time feedback represents 16% of this sample.
- Dinner was enjoyable, but we waited at the entrance without an update before a table became available.
- Friendly staff once we ordered, though the counter queue moved more slowly than expected.
- We arrived during the Saturday dinner rush and understood there would be a wait. The estimate changed twice, though, and nobody checked in until we asked. A short update would have made the wait much easier.
Evidence against
- Quick lunch service even while busy.
- We were seated quickly and the meals arrived together.
Why this test first
Several related guests describe uncertainty or missing updates, not simply a long wait. This tests the communication hypothesis before adding ongoing staffing cost.
Other approaches considered
Additional front-of-house coverage
Add another team member during the busiest service period.
Considered because Busy service periods appear in the evidence. Not ranked first because Current feedback does not establish understaffing as the main cause, and this option creates ongoing operating cost.
Kitchen throughput review
Review kitchen flow during services with longer waits.
Considered because Some guests experienced long waits. Not ranked first because Several comments specifically describe missing or changing updates, so the communication hypothesis can be tested independently first.
What GuestSence learned before
- Peak-service floor hand-off · Previous result: Improved.
- Wait-related feedback decreased, so GuestSence keeps that process and tests the remaining communication gap instead of starting over.
Guest evidence
“Dinner was enjoyable, but we waited at the entrance without an update before a table became available.”
“Friendly staff once we ordered, though the counter queue moved more slowly than expected.”
“We arrived during the Saturday dinner rush and understood there would be a wait. The estimate changed twice, though, and nobody checked in until we asked. A short update would have made the wait much easier.”
“Quick lunch service even while busy.”
“We were seated quickly and the meals arrived together.”
Limitations & what could change our view
Limitations
- This is a sample restaurant dataset, and the interpretation remains a hypothesis for the team to test.
What could change our view
- New related feedback consistently points to kitchen throughput rather than communication.
Read-only sample · No real guest data · Same sample restaurant as the interactive demo
