You were almost through checkout. What made you stop?
A
Cart Abandonment Survey
Evaluator agent · 2.2K uses
Evaluator Agent
Returns and Exchange Experience Feedback
Your reason codes were designed for the warehouse, not for merchandising. In apparel and other high-return categories, returns are the single largest margin leak and the data behind them is close to useless, because a shopper picking did not fit could mean the size chart, the photography, the description, or a between-sizes problem your grading created. This conversation asks what they actually meant.
Return rate reduced
Sizing and content problems named
Process failures caught separately
Used 896+ 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 reason code they selected at the timeThe real underlying reason for the returnWhether the two matchHow the return process worked, step by stepPain points in the processWhat went wellThe outcome: refund, exchange or creditSatisfaction with the process itself
Questions it always asks
The core fields every response captures.
Always capture the stated reason code before probing the real reason
Ask how easy the return process itself was, separately from the product
How it adapts
Follow-ups that change based on what people say.
If they cite fit, find out whether it was the size chart, the photos, the description, or grading
If the return process itself frustrated them, ask whether it changed their view of the brand
Where it routes people
Different paths for different answers.
Send sizing and description problems to the product and content teams
Escalate slow refunds and poor communication to fulfillment leadership
Automations it can trigger
Actions that fire the moment a response comes in.
Alert #merchandising in Slack when one product drives repeat fit returns
Write the true return reason back to the product record in Shopify
Update the customer record in HubSpot when the return process damaged the relationship
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 confirms the return and asks what reason code they selected, without judgment. It then probes past it with a neutral follow-up to find the real underlying cause, keeping that probing brief. From there it covers the mechanics of the return itself: how they initiated it, how easy it was, the shipping or drop-off, the time it took, the communication, and whether they got a refund, exchange or credit.
Getting started
- 1
Trigger the conversation when a return or exchange is completed in your system
- 2
Map your existing reason codes so results compare against them directly
- 3
Decide which findings route to product and which to fulfillment
- 4
Watch for return-process problems that cost the customer relationship on top of the item
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
- Shopify, Slack, Hubspot
- Times Used
- 896+
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.
- Reason codes exist to route stock, not to explain behavior. Did not fit covers a wrong size chart, misleading photography, inconsistent grading and a customer who ordered two sizes deliberately, and merchandising cannot tell them apart.
- Because the code is chosen during a transactional flow the customer wants to finish quickly, it is picked for speed rather than accuracy. The most convenient option absorbs a disproportionate share of returns.
- Return surveys usually stop at the reason and never examine the return process itself, yet a painful returns experience damages the relationship more than the original product problem did.
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 captures the stated code first without judgment, then probes past it briefly to find the real cause. Keeping both lets you calibrate your existing code data rather than replacing it.
- When fit is cited it asks whether the issue was the size chart, the photography, the written description or grading, which are four different owners and four different fixes.
- It covers the mechanics of the return separately: how they initiated it, how easy it was, the drop-off or collection, how long it took and how well they were kept informed.
What is a returns experience survey?
It is feedback collected after a return or exchange, covering both why the item came back and how the return itself went. Most retailers collect only the first, via a dropdown, and only for stock routing purposes. In high-return categories the returns process is also a major driver of repeat purchase, so measuring the experience alongside the reason gives you two levers instead of one.
FAQ
Frequently Asked Questions
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