Purchase Intent Fine-Tuning Simulator
Published model · Gradual Distributional Shifts · ← All-or-Nothing Simulator
Change how respondents reacted to X beer's package and see how purchase intent shifts.
Shift the mix of package reactions gradually — moving 15% of neutral ratings to positive rather than moving every respondent at once.
Curious what's happening inside the model when you apply a scenario? See the companion walkthrough Inside the Model — it traces one respondent's answers through the equation and shows how the headline emerges from all 803 individual predictions.
How to Use This Tool
Shift response distributions continuously and see how purchase intent would have changed
How to Use This Tool
Shift response distributions continuously and see how purchase intent would have changed
1. Pick the outcome (optional)
The concept test asked consumers about six outcomes: top-2 and top-1 purchase intent, package appeal, brand fit, high-quality perception, and premium perception. You're on the published Purchase Intent model — the primary case study with a hand-picked six-variable specification and a fully calibrated fit. Use the outcome picker at the top to explore the other five as exploratory auto-built models — same respondents, same sliders, different predictors chosen by Auto-Build.
2. Filter to a subgroup (optional)
Leave the filter at All respondents to use the published full-sample model, or filter to a subgroup like frequent beer drinkers or high-income households. When you pick a subgroup, the simulator searches for a custom model built on that subgroup's data only — the variables may differ from the headline set. A blue callout names the discovered variables; a gray notice tells you when the sample was too small for a reliable custom model and the headline variable set is being refit on that subgroup instead.
3. Set the distribution
Use the preset buttons for a quick start, or drag the sliders to change the mix of responses for any variable. Unlike pinning everyone to one answer, sliders let you shift just some respondents — for example, moving 10% of Disagree ratings to Neither. The colored bar under each header shows the variable's model leverage. Each variable's percentages must sum to 100%.
4. Run the scenario
Click Run scenario to send your slider settings through the active model. The result shows the projected purchase intent and a 95% confidence interval. The How certain is this result? chart shows 10,000 simulated outcomes so you can see how much the baseline and your scenario overlap.
5. Explore each factor
Click Explore Each Factor after a run to see a full per-variable sensitivity breakdown. Each card shows the predicted purchase intent if that one variable's distribution were set to 100% at each level, holding your other sliders fixed. Combined best/worst cards show the result of every variable's best or worst level at once.
6. Switch to All-or-Nothing
Fine-Tuning shifts distributions, approximating realistic shifts in package reaction. If you'd rather pin every respondent to one specific answer per question — useful for testing extreme scenarios that make the model's assumptions visible — use the All-or-Nothing Simulator link above the scenario panel. Your subgroup filter and outcome choice carry over.
7. Save, share, and reset
Every run is saved to the Saved Scenarios drawer at the bottom of the page — click Load on any card to restore its slider settings, or Remove to drop it. Use Share scenario to copy a link with your settings encoded, or Reset to baseline to return sliders to the empirical concept-test distributions. The baseline shown is the model's estimate for whichever filter is active.
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Want to set every respondent to one specific value instead? All-or-Nothing Simulator →
Set the distribution
The number on each card is the variable's swing on this page — how far the prediction would move if you concentrated all responses in this variable's lowest-leverage level versus its highest-leverage level, using the real values within each level. A bigger swing means redistributing this variable matters more.
Note: this page doesn't pin everyone to one specific value. It shifts what kind of responses show up in the model's average — emphasizing some, de-emphasizing others — so the answers stay grounded in situations the data actually contains. That's why swings here are smaller than on the All-or-Nothing page, where every respondent is set to the same extreme value.
Predicted outcome
How did you change the survey?
Each pair of bars compares the actual distribution (green) to the distribution under your scenario (red where you changed it). This is what you changed — not the predicted impact, just the input.
How certain is this result?
Every prediction has wiggle room — these histograms show how much. The green bars are the plausible answers for the baseline; the red bars are the plausible answers for your scenario. Where the colors overlap, the two answers are close enough that the model can't cleanly tell them apart.
Calibration: predicted vs. observed
How closely the model's predicted probabilities track the observed outcome rates, binned by predicted decile. Bubble size shows respondents per bin. The dashed diagonal is perfect calibration; the blue scenario line is your run; the gray line (when present) is the unmodified base model for comparison.
Explore each factor
For each variable, see how the predicted outcome would change if you switched that one distribution to 100% at each level — holding all your other selections fixed. Reveals which individual levels have the most leverage given your current scenario.