You used to shop with us most weeks. What changed?
O
Lapsed Customer Research
Interviewer agent · 1.1K uses
Interviewer Agent
Is Our Loyalty Program Actually Working
Redemption is not incrementality. This finds out which rewards you are paying for twice. Loyalty reporting shows redemption, not causation. Members redeem because they were shopping anyway, and the program takes credit for trips it never influenced. This conversation walks one real recent trip, asks whether they would have shopped there without the program, then goes benefit by benefit to separate what moves people from what goes unnoticed.
Reward spend redirected
Incrementality evidenced
Dead benefits identified
Used 1,483+ 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 retailer category and program typeA specific recent trip, reconstructed in detailWhether the program influenced that tripWhich benefits actually changed behaviorWhich benefits go unnoticed or unusedThe underlying motivation behind the benefits that matter
Questions it always asks
The core fields every response captures.
Always anchor on one specific real trip before discussing the program in general
Always ask the counterfactual: would they have shopped there without the program
How it adapts
Follow-ups that change based on what people say.
If they say the reward decided the trip, probe for what they would have done without it
If a benefit goes unmentioned, ask about it directly rather than assuming it does not matter
Where it routes people
Different paths for different answers.
Send benefits with no measurable influence to the loyalty team for review
Flag members who only shop on promotion to the margin analysis
Automations it can trigger
Actions that fire the moment a response comes in.
Post a weekly incrementality summary to #loyalty in Slack
Update the member record in HubSpot with which benefits actually influence them
Tag members whose behavior the program does not change
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 member to think of one recent, real shopping trip and walks through it step by step: what prompted it, how they chose where to go, what happened at checkout. It then asks the counterfactual directly, whether they would have shopped there anyway, and probes the reasoning rather than accepting a yes or no. Finally it goes through each benefit in turn and ladders down to why the ones that matter matter.
Getting started
- 1
Select active members across every tier so the results are not skewed to your best customers
- 2
List the benefits you want tested individually, including the ones you suspect are ignored
- 3
Decide what counts as evidence of incrementality before you run it
- 4
Route the findings to the team that sets next year's reward budget
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
- Hubspot, Slack, Email
- Times Used
- 1,483+
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 data proves that a benefit was used, never that it changed anything. A member who was shopping with you anyway and scanned their card looks identical to one the program genuinely won, and the program takes credit for both.
- Asking members how satisfied they are with the program produces a high score and no decisions. People like free things. Liking a benefit and changing behavior because of it are unrelated, and satisfaction surveys cannot tell them apart.
- A benefits checklist invites members to tick everything that sounds appealing, which systematically overstates demand for benefits nobody actually uses. The result is a program that keeps adding perks and never removes any.
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 real, recent trip forces the answer to be about behavior rather than opinion. The member is describing what they actually did, which is far harder to rationalize than what they think of a program in the abstract.
- The conversation asks the counterfactual directly, whether they would have shopped there without the program, and then probes the reasoning rather than accepting a yes or no. That single question is the closest thing to an incrementality test you can run without a holdout group.
- Going benefit by benefit surfaces the ones that never come up spontaneously, which are usually the ones costing you money for nothing. Asking about each in turn is the only way to catch the perks members forgot they had.
What is a loyalty program effectiveness survey?
It is research designed to test whether a loyalty program changes where and how much people shop, rather than whether they like it. The distinction matters because almost every program scores well on satisfaction and very few can demonstrate incrementality. This template approaches it behaviorally: it reconstructs a single real trip, asks what decided it, and then tests each benefit against that trip and against the member's behavior generally.
FAQ
Frequently Asked Questions
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