You used to shop with us most weeks. What changed?
O
Lapsed Customer Research
Interviewer agent · 1.1K uses
Interviewer Agent
Why Shoppers Choose a Competitor Store Nearby
You are not losing customers. You are losing individual trips, and those are winnable. When two stores sit in the same trade area, shoppers rarely pick one and stay. They split trips by occasion, and the split is decided by things a survey never captures: parking at school pickup, one reliable item, a checkout that moves. This conversation walks through real recent trips to both and finds what tipped each one.
Trip share recovered
The real tiebreaker named
Fixes your store teams can act on
Used 1,628+ 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 trip type they were shopping forA specific recent trip and which store they choseA contrasting trip where they chose the other storeThe surface reasons behind each choiceThe underlying motivation beneath those reasonsConcrete conditions that would tip a trip either way
Questions it always asks
The core fields every response captures.
Always anchor on a specific recent trip rather than a general preference
Ask what would have to be true for the next trip to go the other way
How it adapts
Follow-ups that change based on what people say.
If they mention convenience, find out whether it is distance, parking, opening hours, or the route they were already on
If they raise price, ask which specific items they compare and how often they actually check
Where it routes people
Different paths for different answers.
Send location-specific tiebreakers to that store's manager weekly
Escalate any driver that appears across three or more trade areas to category leadership
Automations it can trigger
Actions that fire the moment a response comes in.
Post a weekly digest of tiebreakers by trade area to #store-ops in Slack
Log the competitive driver against the trade area record in HubSpot
Alert merchandising when a single missing item keeps deciding trips
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 shopper to walk through the last time they went to one of the two stores, then a trip where they chose the other. It never names either brand, letting the shopper use their own words. It probes the reasoning behind each choice until the underlying driver surfaces, then tests what specific conditions would flip a trip from the default to the alternative.
Getting started
- 1
Recruit shoppers who use both stores, from your own list or a panel you already buy
- 2
Name the trade areas you want covered so results roll up by location
- 3
Decide which occasions matter most: the weekly shop, the top-up, the emergency run
- 4
Route the tiebreakers to the store and category teams who can act on them
Template Details
- Agent Type
- Interviewer
- Category
- Retail and e-commerce
- Industry
- Retail & E-commerce
- Business outcome
- Strengthen your brand
- Job to be done
- Interview customers & users
- Journey stage
- Discovery & research
- Replaces
- 1:1 interviews
- Integrations
- Slack, Hubspot, Email
- Times Used
- 1,628+
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.
- Asking shoppers to rate you against a competitor produces a comparison nobody actually makes. Real shoppers do not score two stores on attributes, they decide one trip at a time based on what is happening that day, and a rating scale flattens all of that into a number that moves for no traceable reason.
- A survey that names your competitor teaches shoppers to think in terms of a head-to-head they may never have considered. Once the frame is set the answers describe a rivalry rather than a routine, and you learn how people rationalize choices rather than how they make them.
- Fixed attribute lists lock the answer into the things you already measure: price, range, convenience. The actual tiebreaker is frequently something narrower, such as one reliable product, the car park at a particular time, or a checkout that moves when you are in a hurry.
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 a real recent trip, which is the unit shoppers actually decide in. That keeps the answers concrete and makes the tiebreaker traceable to a moment rather than an abstract preference.
- It never names either store. The shopper uses their own words, which means you learn how they frame the choice rather than how they respond to your framing. That is also what makes the same template reusable by any retailer in any trade area.
- By asking for a contrasting trip where the other store won, the conversation gets both sides of the routine from the same person. The difference between those two trips is the tiebreaker, and it is far more actionable than a preference score.
What is competitor store research?
Competitor store research is the practice of talking to shoppers who use both you and a nearby rival to understand how they allocate trips between you. It is different from brand tracking, which measures perception at a distance, and from market share analysis, which tells you the outcome without the mechanism. In contested trade areas the mechanism is what matters, because share moves one trip at a time and the decisions are made in seconds on grounds that are often surprisingly 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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