You rated the stay a 6. What would have made it a 10?
D
Post-Stay Survey
Evaluator agent · 1.9K uses
Evaluator Agent
On-Property Spend and Experience Feedback
The till records what happened. The money is in what nearly did. A visitor who considered a second drink, looked at the merchandise, and walked past the photo booth is worth more to understand than one who bought nothing at all. The till cannot see the near miss. This conversation asks what they thought about buying and what stopped them.
Secondary spend lifted
Near-miss purchases recovered
Queue and layout blockers named
Used 1,092+ 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
What they actually spent money onWhat they considered and decided againstThe blocker for each near miss: price, queue, awareness or convenienceWhere they were heading when they decided against itWhat would have converted the near miss
Questions it always asks
The core fields every response captures.
Always ask what they considered buying and decided against
Ask what specifically stopped each near-miss purchase
How it adapts
Follow-ups that change based on what people say.
If a queue stopped them, ask how long was too long and where they were heading next
If they did not know something existed, ask where they would have expected to see it
Where it routes people
Different paths for different answers.
Send queue and layout blockers to operations
Route awareness gaps to the signage and marketing team
Automations it can trigger
Actions that fire the moment a response comes in.
Post abandoned-spend blockers to #venue-ops in Slack weekly
Update the visit record in HubSpot with the near-miss categories
Alert operations when a queue repeatedly blocks the same purchase
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 walks the visit and asks what they spent money on, then asks the more valuable question: what they considered and decided against. For each near miss it finds the blocker, whether it was price, the queue, not knowing it existed, carrying it around, or simply not being asked at the right moment. Results point to specific, fixable friction rather than generic pricing complaints.
Getting started
- 1
Trigger shortly after the visit while the detail is still recallable
- 2
List the secondary spend categories you want covered so results compare across visits
- 3
Capture queue and timing context, since that is a frequent hidden blocker
- 4
Route each blocker type to the team that owns it
Template Details
- Agent Type
- Evaluator
- Category
- Hotels and hospitality
- Industry
- Hotels & Hospitality
- Business outcome
- Convert more leads
- Job to be done
- Collect customer feedback
- Journey stage
- Engagement
- Replaces
- Survey tools
- Integrations
- Slack, Hubspot, Email
- Times Used
- 1,092+
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.
- Till data records completed transactions and is structurally blind to the ones that nearly happened, which is where the recoverable revenue sits.
- Asking visitors whether they found the food and retail offer good value produces a predictable complaint about price that obscures the operational blockers doing most of the damage.
- Standard surveys rarely capture what the visitor was doing when they decided against a purchase, yet that context, heading to a show, carrying a child, running late, usually explains the decision better than price.
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 asks what they considered and decided against, which turns the near miss from an invisible loss into a measurable and addressable one.
- For each near miss it identifies the specific blocker, separating price from queue from simply not knowing the option existed, which are three very different fixes.
- Capturing what they were doing at the time reveals timing and layout problems, such as a merchandise outlet positioned where nobody has time to stop.
What is on-property spend research?
It is research into secondary spend at a venue, covering both what visitors bought and what they considered buying and did not. The second half is the point: venue economics usually depend on spend beyond admission, and the till can only see the transactions that completed.
FAQ
Frequently Asked Questions
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Replace drop-off, poor qualification, and missing context with AI conversations that capture structured data and real understanding. Set up in minutes.
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