You used to shop with us most weeks. What changed?
O
Lapsed Customer Research
Interviewer agent · 1.1K uses
Interviewer Agent
New Store Opening Customer Research
A site model tells you who lives there. It cannot tell you whether they would come. Most site decisions rest on traffic counts and demographics, which describe who is there rather than whether they want you. This conversation talks to people who live or work in the catchment about their current habits for the category, what those options do well and badly, and what specific change would be enough to make them try somewhere new.
First-year targets hit
Weak sites caught early
Real switching triggers, not demographics
Used 1,012+ 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 category and how they shop it todayWhere they currently go and how oftenHow they get there and in what formatWhat their current options do wellWhat they dislike or find frustratingConcrete triggers that would make them try somewhere newThe underlying need behind those triggers
Questions it always asks
The core fields every response captures.
Always establish current behavior before mentioning anything new
Ask for a concrete trigger that would make them try a different place
How it adapts
Follow-ups that change based on what people say.
If they say better prices, push for what better would actually look like in numbers or comparisons
If they describe a strong habit, ask what has broken that habit in the past
Where it routes people
Different paths for different answers.
Send catchment-level findings to real estate and market planning before sign-off
Flag catchments where no realistic trigger emerges as high risk
Automations it can trigger
Actions that fire the moment a response comes in.
Post a per-site summary to #real-estate in Slack when a study completes
Log the catchment findings against the site record in HubSpot
Alert leadership when a shortlisted site shows weak switching intent
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 grounds first in real current behavior: where they shop for the category, how often, and how they get there. Only once that is established does it explore what they like and dislike, laddering on the things that clearly matter. It closes by pushing for concrete switching triggers rather than vague answers like better prices, asking what better would actually look like to them.
Getting started
- 1
Recruit people who live or work in the catchment, through a panel or local outreach
- 2
Define the category precisely so the conversation stays on the relevant behavior
- 3
Decide which competitor formats you need represented in the sample
- 4
Deliver the findings before the lease decision rather than after
Template Details
- Agent Type
- Interviewer
- Category
- Retail and e-commerce
- Industry
- Retail & E-commerce
- Business outcome
- Build the right product
- Job to be done
- Interview customers & users
- Journey stage
- Discovery & research
- Replaces
- 1:1 interviews
- Integrations
- Slack, Hubspot, Email
- Times Used
- 1,012+
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.
- Demographic and traffic models describe who is nearby, which is not the same as whether any of them would change where they shop. Two catchments with identical profiles can perform completely differently depending on how entrenched the incumbent habits are.
- Concept surveys that describe a proposed store measure reaction to a description, which is a poor predictor of behavior. People are agreeable about hypothetical shops and unmoved by real ones.
- Asking what people want produces better prices and more choice, which is true everywhere and actionable nowhere. Without probing what better would actually look like, the answer cannot inform a format decision.
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 establishes real current behavior before any mention of something new, so the baseline is honest and the switching question has something concrete to push against.
- It deliberately avoids presenting a store concept for evaluation. Instead it asks what would genuinely make them try somewhere different, which produces triggers grounded in their actual frustrations rather than polite reactions to your idea.
- When someone says better prices, the conversation pushes for what better would actually look like in numbers or comparisons, which is the difference between a useless answer and a positioning input.
What is new store research?
New store research talks to people in a prospective catchment about how they currently shop a category and what would make them change. It complements site modeling rather than replacing it: the model tells you how many people are within a drive time, and the research tells you whether those people have any reason to alter a routine they are largely satisfied with. In markets with entrenched incumbents, that second question decides whether a site hits its first-year numbers.
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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