You used to shop with us most weeks. What changed?
O
Lapsed Customer Research
Interviewer agent · 1.1K uses
Interviewer Agent
Fuel Rewards Program Feedback
Most redeemed discounts pay for a stop that was already happening. Fuel is the most price-transparent purchase most people make, which is exactly why reward attribution is so hard. This conversation walks one real fill-up step by step, uses a neutral counterfactual instead of asking whether the discount influenced them, and then finds out what happened inside the store, where the margin actually is.
Incremental gallons identified
Inside-store attach lifted
Reward waste cut
Used 938+ 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 prompted the stop and the route they were onHow they decided where to pull inWhether the reward was decisiveWhere they would have stopped without itWhether they went inside the storeWhat they bought inside, item by itemWhy they did not go in, if they did not
Questions it always asks
The core fields every response captures.
Always anchor on the most recent real fill-up, not a typical one
Always ask where they would have stopped without the discount
How it adapts
Follow-ups that change based on what people say.
If they compared prices across apps, ask which tools and how big a gap would change their route
If they did not go inside, ask why not: hurry, habit, or paid at pump
Where it routes people
Different paths for different answers.
Send non-incremental redemption patterns to the loyalty finance team
Flag sites where drivers consistently skip the inside store to operations
Automations it can trigger
Actions that fire the moment a response comes in.
Post weekly incrementality findings to #fuel-marketing in Slack
Update the member record in HubSpot with their incrementality verdict
Alert operations when inside-store conversion is weak at a specific site
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 driver to walk through their most recent fill-up: what prompted the stop, where they were coming from and going to, and how they chose where to pull in. Rather than asking whether the reward influenced them, it asks where they would have stopped if the discount had not been available, and whether they passed another station on the way. It then asks what they bought inside, item by item, or why they did not go in.
Getting started
- 1
Select enrolled members across redemption frequencies, not just heavy users
- 2
Decide whether you are measuring fuel incrementality, inside-store attach, or both
- 3
Set the counterfactual framing you want used so results are comparable
- 4
Route the findings to the team that funds the discount
Template Details
- Agent Type
- Interviewer
- Category
- Retail and e-commerce
- Industry
- Retail & E-commerce
- Business outcome
- Reduce churn
- Job to be done
- Interview customers & users
- Journey stage
- Retention
- Replaces
- Survey tools
- Integrations
- Slack, Hubspot, Email
- Times Used
- 938+
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.
- Redemption reporting counts discounts given, not stops caused. A driver who passes your site every evening and scans the app looks exactly like one you won from a competitor, and the program claims both.
- Asking drivers whether the discount influenced them produces an unreliable yes, because the question suggests its own answer and people like to believe their decisions are rational.
- Fuel surveys rarely follow the customer inside, yet inside-store spend is where the margin is. A program that drives gallons but no basket is a very different investment case from one that drives both.
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 anchors on the most recent real fill-up and reconstructs the decision as it happened, including the route and what prompted the stop, so the answer describes behavior rather than self-image.
- Instead of asking whether the discount influenced them, it asks where they would have stopped without it and whether they passed another station on the way. That neutral counterfactual is far harder to answer flatteringly.
- It follows the customer inside and asks what they actually bought item by item, or why they did not go in, which is where the commercial upside of a fuel program usually sits.
What is a fuel rewards program survey?
It is research that tests whether a fuel discount or rewards program changes where drivers stop, rather than whether they like it. Fuel is unusually price-transparent, which makes attribution unusually hard: drivers see competing prices from the road, and most stops are decided by route rather than by loyalty. The only reliable way to separate influence from coincidence is to reconstruct a specific fill-up and ask a neutral counterfactual.
FAQ
Frequently Asked Questions
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