Purchase Intent Simulator
Published model · All-or-Nothing Scenarios · Part 2: Fine-Tuning Simulator →
54.7% of respondents rated their purchase intent in the top two boxes after seeing the package concept. What drove that number — and what could have changed it?
This simulator runs on the published model — six package-reaction questions chosen for the clearest, most interpretable story about purchase intent.
Pin every respondent to one chosen response level per package factor and see how top-2 purchase intent shifts.
How to Use This Tool
Explore what drives consumer purchase intent for the test package
How to Use This Tool
Explore what drives consumer purchase intent for the test package
1. Try a preset or set your own
Use the preset buttons for a quick start, or use the dropdowns to set each package-reaction question yourself. The colored bar beneath each label shows its model leverage — the purchase-intent swing from best to worst response on that variable. You can set one factor or several before running.
2. Run the scenario
Click Run scenario to send your settings through the published logistic regression model. The result shows the projected top-2 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.
3. 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 at every level of that variable, holding your other settings fixed. Combined best/worst cards show the result of taking every variable's best or worst level at once.
4. Review past scenarios
Every run is saved to the Saved Scenarios drawer at the bottom of the page. Each card shows which variables you changed and the predicted outcome. Click Load to restore a past scenario's settings, or Remove to drop it from the list.
5. Combine variables
Set multiple dropdowns at once before running to test compound scenarios — for example, what happens when both Premium and Personal Relevance ratings shift simultaneously. The model accounts for all variables jointly, so combined scenarios can reveal effects that single-variable tests miss.
6. Reset and iterate
Click Reset to baseline to return all dropdowns to "No change" and clear the results. The baseline of 54.7% is the model's estimate of top-2 purchase intent in the unmodified concept-test sample. Try different combinations to see which package-reaction questions matter most — or least.
Loading simulator…
Couldn't load the simulator
Set the scenario
The number on each card is the variable's swing on this page — how far the prediction would move if every respondent had this variable's lowest-leverage value versus its highest-leverage value, holding the other variables at their observed values. A bigger swing means a bigger lever.
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.
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
For each variable's level, the predicted outcome if every respondent had that response, all else unchanged. This is the analytic view of the simulator.
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 just that one selection — holding all your other selections fixed. Reveals which individual choices have the most leverage given your current scenario.