What were you looking at when you decided?
A
Competitor Hotel Research
Interviewer agent · 941 uses
Interviewer Agent
Why Repeat Guests Stopped Booking With Us
Your best guests do not complain on the way out. They just stop appearing. Loyal guests almost never complain on the way out. They drift, usually because something small changed and a competitor made a better offer at the right moment. This conversation reaches guests who stopped without ever filing a complaint and reconstructs what actually happened.
Repeat guest rate lifted
Guest LTV protected
Silent churn made visible
Used 1,689+ times
What's inside this template
Start from this conversation and adapt it to your team — change any question, add your own logic, and connect the tools you already use.
Information it collects
The old booking pattern: frequency and trip purposeWhen it stoppedWhat changed: rate, refurbishment, staff, service or circumstanceWhether they ever raised it with anyoneWhere they stay now and what that property does betterRealistic conditions for returning
Questions it always asks
The core fields every response captures.
Always establish the old booking pattern before asking what changed
Ask what it would take for them to book with you again
How it adapts
Follow-ups that change based on what people say.
If they now stay elsewhere, ask what that property does that yours does not
If a specific stay went wrong, ask whether they mentioned it to anyone at the time
Where it routes people
Different paths for different answers.
Send winnable guests to the loyalty team with the drift reason attached
Flag repeated property-specific causes to that general manager
Automations it can trigger
Actions that fire the moment a response comes in.
Post weekly drift causes to #loyalty in Slack
Update the guest record in HubSpot with the lapse reason and win-back condition
Trigger a targeted win-back offer only for guests marked recoverable
SOC 2 Type II and ISO 27001:2022 certified. Responses are encrypted in transit and at rest, and you own your data. View our Trust Center.
How this AI agent works
The conversation reaches guests whose booking pattern has quietly stopped. It establishes what the pattern used to be, how often they stayed and what for, then finds what changed: a renovation, a rate rise, a staff change, a move, or a better alternative. It probes whether they ever raised it with anyone, which is usually no, and closes by testing what would realistically bring them back.
Getting started
- 1
Select guests whose booking frequency has dropped sharply without a complaint on file
- 2
Decide what counts as a lapsed pattern for your property type and guest mix
- 3
Set what you can realistically offer so the return conditions are tested honestly
- 4
Route the winnable guests to the property or loyalty team with the reason attached
Template Details
- Agent Type
- Interviewer
- Category
- Hotels and hospitality
- Industry
- Hotels & Hospitality
- Business outcome
- Reduce churn
- Job to be done
- Interview customers & users
- Journey stage
- Retention
- Replaces
- 1:1 interviews
- Integrations
- Hubspot, Slack, Email
- Times Used
- 1,689+
Forms collect fields. Conversations capture context.
Static forms force complex situations into rigid dropdowns. Perspective captures structured data and the reasoning behind it — so your team makes better decisions, faster.
The static form
yoursite.com/intake
Category *
Select...
Details
Describe your situation...
Submit
Result:Category: "Other"|Details: "It's complicated"
No context. No follow-up. No next step.
- Booking data notices a lapsed pattern long after the relationship has moved. By the time a report flags a guest who used to stay monthly, they have usually been staying somewhere else for a year.
- Complaint records systematically miss this group, because the defining characteristic of a drifting loyal guest is that they never complained. They simply booked elsewhere and it worked.
- Standard win-back offers assume price was the issue and lead with a discount, which insults a guest whose actual reason was a refurbishment that made their usual room type worse.
The AI conversation
"Tell me more about the timeline — when did this start, and is there a deadline your team is working against?"
Extracted & structured automatically
Category
High-priority
Urgency
Deadline: 2 weeks
Sentiment
Frustrated but hopeful
Next step
Route to senior team
Triggered: Slack alert sent| CRM updated
Right team. Full context. Instant action.
- The conversation reconstructs the old booking pattern first, which establishes the value of the relationship and makes the reason for its end interpretable.
- It probes the specific categories that end loyal relationships in hospitality, rate changes, refurbishments, staff turnover and service drift, without leading toward any of them.
- Asking whether they ever raised it is one of the more revealing questions in the template, because the answer is almost always no and that itself indicates where the feedback loop failed.
What is guest win-back research?
It is research with guests whose booking pattern has quietly stopped, to establish why. It is distinct from post-stay feedback, which reaches people during an active relationship, and from review analysis, which captures only those motivated enough to write. The target population here is the one that produces neither: loyal guests who drifted away without ever registering a complaint.
FAQ
Frequently Asked Questions
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