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CPG Demo · Beer Concept Test (n=803)

Model Builder

Find the drivers behind the outcome you choose

Pick one of six outcomes — purchase intent, package appeal, brand fit, high-quality perception, premium perception, or most-often-purchased brand — then choose predictor variables across eight categories and see which ones move the headline you've selected.

Five of the six are a top-2-box recode of a 5-point scale: 1 if the respondent picked the top two boxes (e.g. Extremely / Very likely for purchase intent, or Strongly / Somewhat agree for premium perception); 0 otherwise. The sixth, most-often-purchased brand, is an unordered multinomial outcome across seven brand categories — the six most common brands plus an Other group.

The simulators and stress test are scoped to the Purchase Intent outcome. To explore drivers of the other five outcomes, use this Model Builder. The Survey Explorer works across all six outcomes.

How to Use This Tool

Pick the outcome you want to model, then check the predictor variables you want to test, then click Run Analysis to fit. If you've chosen Purchase Intent, the published model's six variables are pre-selected as a starting point.

1. Pick an outcome

Six outcomes are available: Purchase Intent (Q21, the case study's headline), Package Appeal (Q12), Brand Fit (Q16), High-Quality Perception (Q18r6), Premium Perception (Q18r8), and Most-Often-Purchased Brand (Q10). The first five can be modeled on their top-2-box (logistic); where the outcome is an ordered rating, the tool fits it as an ordered logit on the full scale and still reports the headline as the top-2 probability. Most-Often-Purchased Brand is fit as a multinomial logit across seven brand categories (headline = the predicted Bud Light share). Switch outcomes anytime with the chooser at the top of the variable section.

2. Pick predictors

Use the search box and category buttons to find variables: Test Design, Package Reaction, Competitor Intent, Brand Knowledge, Beer Preferences, Behavior, Background, and Demographics. For Purchase Intent, six Package Reaction variables are pre-checked from the published model; for other outcomes, start from scratch or use Auto-Build below. Run Analysis is capped at 20 predictors; Auto-Build searches the full set and isn't capped.

3. Fit and read

Click Run Analysis to fit the model with whatever variables you've checked. Or use Auto-Build Standard to let the algorithm search all variables automatically, or Auto-Build Actionable Predictors to weight selection toward levers you can move through package design or marketing over fixed context like demographics.

Optional: subgroup

By default, the model is fit on all 803 respondents. Optionally restrict it to one subgroup at a time using the Whose data? panel — e.g. just younger respondents, or just heavy-category buyers — to see how the model behaves within that group. Subgroup levels with fewer than 100 respondents are hidden.

After you run — what to expect

Review results

Results show how well the model predicts the outcome and how much each predictor contributes. The Other Variables in This Survey section lists every variable not yet in your model — each card shows whether adding it would likely improve fit.

Use the simulator

Click Launch in the Simulator panel to open an interactive tool. In Pin mode, set any combination of survey responses — e.g. high package appeal and low brand fit — and watch the predicted headline probability update. For ordered outcomes you also see the full outcome distribution shift — where each rating's share starts versus where your scenario moves it — and a Distribute mode lets you reshape response shares with sliders that sum to 100%.

Iterate and compare

Each run is saved in the Saved Analyses tray. Click Load to restore a run, or Pin two runs to view them side by side. Each card shows AUC, Brier, and either Tjur R² (logistic) or McFadden R² (ordered logit) — higher R²/AUC and lower Brier mean a better-performing model.

If you ran with a synthetic variable, results show three sections:

Full Model

Complete model including all selected predictors and the synthetic variable.

Base Model

Model performance without the synthetic variable.

Synthetic variable impact analysis

How well the synthetic met its specifications and how it changed model fit (versus the base model).

What are you trying to predict?

Choose the outcome your model will try to explain.

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Survey Questions to Include in Your Model

Download CPG Questionnaire (PDF)
For Purchase Intent, six Package Reaction variables are pre-checked — the published model. For the other four outcomes, no variables are pre-checked. Check the variables you want to test, or scroll down to Run Analysis or Auto-Build.
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Vulnerability check — optional; a synthetic variable that tests how sensitive the model is to an omitted variable

Vulnerability check. Inserts a synthetic variable with a known relationship to the outcome, then sees how sensitive the model is to it. If the synthetic shows up as a significant predictor in the result, the model may be absorbing signal that doesn't really belong to your chosen variables — a sign the findings are sensitive to omitted variables of similar strength. (Similar in spirit to a sensitivity analysis: how much would an omitted variable have to matter before it changes the picture.)

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The model you're about to fit
Predictors (X)
No predictors selected yet. Check variable cards above or click Auto-Build below.
Outcome (Y)
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A logistic regression model uses the predictors on the left to estimate the probability of the outcome on the right. This summary updates as you change your selection — and shows exactly what gets submitted when you click Run Analysis. Auto-Build ignores this selection and chooses predictors for you.

Run Analysis

Build a model from exactly the predictors you've selected above. Full analyst control.

Returns: model fit, predictor strengths, calibration, and an interactive simulator.

Auto-Build

Ignores your selection above. Searches the full set of available predictors and picks the best subset for you. Actionable Predictors is selected by default — switch to Standard below if you prefer pure fit:

Finds the predictors that can actually be changed — useful for planning.

Returns: a chosen subset of predictors plus full model fit, strengths, and an interactive simulator.

Auto-Building Optimal Model… 0s

Full Model Performance

Complete model including all predictor variables and the synthetic variable