CPG Demo · Beer Concept Test (n=803)

Model Builder

Find the drivers behind the outcome you choose

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

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

Five outcomes are available, each a top-2-box recode: Purchase Intent (Q21, the case study's headline), Package Appeal (Q12), Brand Fit (Q16), High-Quality Perception (Q18r6), and Premium Perception (Q18r8). 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. Set any combination of survey responses — e.g. high package appeal and low brand fit — and watch the predicted top-2 outcome probability update instantly.

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 Tjur R², AUC, and Brier — higher Tjur R² and AUC, lower Brier means 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 Performance

How well the synthetic met its specifications and its impact on model fit.

What are you trying to predict?

Choose the outcome your model will try to explain.

Required
Loading outcomes…

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.
Loading variables…
Outcome: …
0 of … predictor variables selected
Advanced options — synthetic-variable vulnerability check (optional)

Vulnerability check. Inserts a synthetic predictor with a known relationship to the outcome. 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 it's vulnerable to omitted variables of similar strength.

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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

Section 1: Full Model Performance

Complete model including all predictor variables and the synthetic variable

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