You were almost through checkout. What made you stop?
A
Cart Abandonment Survey
Evaluator agent · 2.2K uses
Evaluator Agent
Store Cleanliness and Speed Feedback
Most complaints are forgiven. A few quietly end the habit. Fix those. In high-frequency, low-dwell formats the whole business rests on the next visit. This conversation asks about a specific recent quick trip, covers cleanliness, queues, staffing, stock and broken equipment without leading, then asks which of those would genuinely stop them coming back.
Repeat visits protected
Dealbreakers separated from irritations
Problem sites flagged fast
Used 1,859+ 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
A specific recent quick tripIssues experienced across cleanliness, queues, staffing, stock and equipmentWhich issue bothered them mostWhich issues would genuinely stop them returningReturn intentAny additional comments in their own words
Questions it always asks
The core fields every response captures.
Always anchor on one specific recent trip
Always separate what annoyed them from what would stop them returning
How it adapts
Follow-ups that change based on what people say.
If they report no issues, skip straight to return intent rather than probing for problems
If they name a dealbreaker, ask what they would do instead next time
Where it routes people
Different paths for different answers.
Send dealbreaker issues to the site manager and area manager the same day
Escalate equipment failures that repeat at a site to facilities
Automations it can trigger
Actions that fire the moment a response comes in.
Alert #field-ops in Slack when a site logs two dealbreakers in a week
Update the site record in HubSpot with the week's issue mix
Create a facilities ticket when the same equipment fails twice
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 asks the shopper to recall one specific recent quick trip, so the answers are grounded rather than general. It then covers the five operational areas without steering toward any of them. If nothing went wrong it moves on rather than forcing a complaint. When issues do surface it asks which ones were merely annoying and which would actually change where they stop next time.
Getting started
- 1
Trigger from a receipt link or app prompt shortly after a visit
- 2
Confirm the five operational areas match how your field team is organized
- 3
Set the threshold that marks an issue as a dealbreaker rather than an irritation
- 4
Route dealbreakers to the site and the area manager together
Template Details
- Agent Type
- Evaluator
- Category
- Retail and e-commerce
- Industry
- Retail & E-commerce
- Business outcome
- Operate more efficiently
- Job to be done
- Collect customer feedback
- Journey stage
- Feedback & evaluation
- Replaces
- Survey tools
- Integrations
- Slack, Hubspot, Email
- Times Used
- 1,859+
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.
- Complaint logs record volume, not consequence. A hundred mentions of a messy coffee station and five mentions of a persistently broken self-checkout look like a clear priority, right up until you learn which one actually stops people coming back.
- Satisfaction scores in low-dwell formats are compressed and noisy. Most visits are fine, so the score barely moves even when a specific recurring failure is quietly costing a site its regulars.
- Fixed checklists invite people to report everything they noticed, which inflates minor issues and buries the decisive ones. Without a severity question the output is a long list with no order.
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.
- Anchoring on one specific recent trip stops the conversation drifting into general impressions, so the issues reported are real and dated rather than remembered grievances.
- It covers all five operational areas without leading toward any of them, and if nothing went wrong it moves on rather than forcing the shopper to invent a complaint, which keeps the data clean.
- The dealbreaker question separates what annoyed someone from what would actually change their behavior. That single distinction turns an undifferentiated complaint list into a priority order.
What is a store cleanliness and speed survey?
It is operational feedback for high-frequency, low-dwell retail formats such as convenience, forecourt and quick-service, focused on the conditions that determine whether someone returns. The distinguishing feature is that it separates issues by consequence rather than by volume, because in these formats the whole economic model rests on the next visit and only a small subset of problems actually threaten it.
FAQ
Frequently Asked Questions
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