Concept-Test
In a 2025 craft-beer concept test, 54.7% of beer-buying consumers said they would purchase X beer. What drove that intent — and how would the answer change for a different income bracket, a different region, or a different level of brand awareness? Five tools let you explore.
Built in partnership with EMI Research Solutions. The data on this page comes from a craft-beer concept test conducted by EMI Research Solutions, an established commercial-research firm. EMI delivers Mirror engagements to their clients under their own brand, in partnership with Electric Insights, using the same analytical engine documented across this site.
Six Ways to Engage with the Data
Two simulators run the published 6-variable purchase-intent account; a stress test pressure-tests the 54.7% headline; a guided walkthrough opens the model up; an explorer and a model builder let anyone investigate any of five outcomes and build their own competing models.
All-or-Nothing Simulator
Pin every respondent to one chosen response level per package factor and see how the 54.7% top-2 purchase intent shifts. Runs on the published model — or a subgroup-specific refit when you filter to a subgroup.
Try itFine-Tuning Simulator
Same published model — or a subgroup refit — but redistribute response shares gradually rather than pinning. Watch how small distributional changes move the purchase-intent needle.
Try itNon-Response Test
Specify a nonresponse pattern and see whether the 54.7% top-2 purchase intent would survive it. A vulnerability check on the released score, separate from the explanatory model.
Try itInside the Model
A guided walkthrough of how the published 6-variable model turns one consumer's package-reaction answers into a predicted top-2 purchase-intent probability — and how those individual probabilities collapse into the 54.7% headline.
Try itSurvey Explorer
Browse the raw responses. See how consumers reacted to each package factor, which questions moved together, and how one group compared to another. Frequencies, cross-tabs, and correlations — against any of the five outcomes.
Try itModel Builder
Pick any of five outcomes — purchase intent, package appeal, brand fit, high-quality, premium — and refit with whichever predictors you choose. Add or remove items, refit on a subgroup, or let Auto-Build find the best combination. See where the published purchase-intent account holds up — or model an outcome it doesn't cover.
Try itAbout Concept Testing
How a concept test works
1. Show the concept
Respondents see a package design, ad mockup, or product description.
2. Capture reactions
A battery of questions probes appeal, fit, relevance, and intent — typically on 5-point scales.
3. Model what matters — and keep modeling
Statistical modeling identifies which reactions actually predict purchase intent. Most concept tests stop here; this one lets you keep going — refit on subgroups, swap predictors, stress the headline against nonresponse.
Package design
The visual concept shown to respondents
Purchase intent
5-point scale, top-2 box modeled
Attribute reactions
Twenty package-perception predictors
Subgroup cuts
Eight subgroup variables for refits
The Concept-Test
Beer-category respondents were shown a new package concept and asked a battery of reaction questions. Purchase intent was captured on a 5-point scale; the top-2 box (Extremely / Very likely) is the modeled outcome.
The published logistic model is validated against Stata 18 to four decimal places.
What the survey measured:
Building on a concept test of your own?
The platform works on any binary outcome with categorical or continuous predictors. Read the overview written for brand and insights teams, or see how engagements are structured.