You were almost through checkout. What made you stop?
A
Cart Abandonment Survey
Evaluator agent · 2.2K uses
Evaluator Agent
In-Store Experience Feedback After a Visit
Your worst stores are costing you sales. This tells you what is different on the floor. Every chain has stores that quietly underperform and nobody can say why. Mystery shopping tells you whether staff followed the script. This conversation asks the real shopper what they came for, whether they found it, whether they bought it, and what specifically helped or blocked them, so the gap between your best and worst locations becomes actionable.
Conversion lifted at weak stores
Friction named, not scored
Outliers flagged weekly
Used 2,153+ 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 the shopper came in to do or buyWhat helped during the visitWhat got in the way or caused frictionWhether they found what they came forWhether they actually purchased itThe reason behind any failure to find or buyThe single change that would improve the next visit
Questions it always asks
The core fields every response captures.
Always ask separately whether they found the item and whether they bought it
Ask what single change would make the next visit better
How it adapts
Follow-ups that change based on what people say.
If they did not find what they came for, ask what it was and whether anyone helped them look
If they mention a staff interaction, ask what the employee did and whether it changed the outcome
Where it routes people
Different paths for different answers.
Send any store scoring below the chain average to its district manager the same week
Escalate stock and staffing themes that repeat across a region to operations leadership
Automations it can trigger
Actions that fire the moment a response comes in.
Alert #store-ops in Slack when a location is flagged twice in seven days
Update the store record in HubSpot with the week's themes
Create a follow-up task for the district manager on every flagged location
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
Shortly after a visit, the shopper gets a short conversation that walks the trip in order: arriving, shopping, checking out. It asks what helped and what got in the way as two separate questions, then asks directly whether they found what they came for and whether they actually bought it, since those are different outcomes. Results roll up by location so outliers surface against the chain average.
Getting started
- 1
Trigger the conversation from a receipt link, loyalty app, or post-visit email
- 2
Decide which parts of the visit you want measured consistently across every store
- 3
Set the threshold that flags a store as an outlier against the chain average
- 4
Route flagged stores to their district manager with the verbatim detail attached
Template Details
- Agent Type
- Evaluator
- Category
- Retail and e-commerce
- Industry
- Retail & E-commerce
- Business outcome
- Improve customer satisfaction
- Job to be done
- Collect customer feedback
- Journey stage
- Feedback & evaluation
- Replaces
- Survey tools
- Integrations
- Slack, Hubspot, Email
- Times Used
- 2,153+
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.
- A store score is a single number standing in for a visit with a dozen distinct moments. When it drops, nobody can say whether the cause was the queue, the stock, the signage or the staff, so the district manager receives a problem with no address.
- Static forms ask what you thought to ask. If the real friction was that a refit moved the category the shopper always buys, and there is no question about layout, that cause stays invisible however many responses arrive.
- Most in-store surveys conflate finding something with buying it, which are different outcomes with different causes. A shopper who found the item and left without it has a price or confidence problem. One who never found it has a layout or stock problem. One question cannot separate them.
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 walks the visit in order, arriving, shopping, checking out, so the feedback maps onto the parts of the store different people are responsible for. That is what turns a score into a task with an owner.
- It asks what helped and what got in the way as two separate questions. Shoppers answer them differently, and keeping them apart stops one good moment from canceling out a real problem in the average.
- Finding and buying are asked separately, which immediately splits the two failure modes and points at either merchandising or price and confidence. That single distinction tends to explain most of the gap between similar stores.
What is an in-store experience survey?
An in-store experience survey collects feedback from shoppers shortly after a physical visit, to understand what happened on the floor. It is distinct from brand tracking, which measures how people feel about the retailer in general, and from mystery shopping, which measures standards compliance. The purpose is operational: to find out what helped or blocked a real shopper on a real trip, in enough detail that a store or district manager can change something specific.
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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