Gabor Granger — of the prices you’re considering, which maximises revenue?
A sequential willingness-to-pay technique. Crowdmines builds the demand curve at each tested price, multiplies acceptance by price to get expected revenue at each point, and reports the price that maximises revenue.
The business question it answers
“Of the prices we are realistically considering, which one maximises revenue?”
Say you’re choosing between $4.99, $5.99, $6.99 and $7.99 shelf prices for a new CPG product. After 600 responses, the chat returns: at $4.99, 78% would buy → $3.89 revenue per respondent; at $5.99, 64% → $3.83; at $6.99, 47% → $3.29; at $7.99, 28% → $2.24. Optimum revenue lands at $4.99 — counter-intuitive when intuition says you could push $6.99.
Set a subgroup variable — “compare loyal customers vs prospects” — and the same demand curve is produced per group, showing how much pricing power exists in each segment.
How the methodology works
In the questionnaire, each respondent goes through a sequential price-testing sequence: “Would you buy this product at $X?” If yes, the price goes up; if no, it stops. The single output per respondent is their maximum willingness to pay (WTP) — the highest price at which they still said yes.
- Demand % at price P = proportion of respondents whose WTP ≥ P. Highest at the lowest tested price; falls monotonically as price rises.
- Projected revenue at price P = P × number of respondents with WTP ≥ P — an expected-revenue-per-respondent metric that captures both price level and volume.
- The optimal price is the price point that maximises projected revenue. Because the analysis operates on observed price points only — no curve smoothing — the optimum is always one of the prices respondents actually encountered in the questionnaire.
This is deliberately conservative: the platform reports what the data says, not what a fitted model extrapolates. If the client needs elasticity estimates between tested prices, that is a separate modelling conversation.
How it differs from Van Westendorp
- Question type: Gabor Granger asks the buying question itself (sequential buy / no-buy). Van Westendorp asks four perception questions.
- Output: Gabor Granger returns a single revenue-maximising price. Van Westendorp returns a price range (PMC → PME) with an optimal point.
- Best for: Gabor Granger for price optimisation when the consideration set is fixed. Van Westendorp for price discovery when the question is “what range is acceptable?”
- Data needed: Gabor Granger needs one max-WTP column. Van Westendorp needs four price columns.
- We have a shortlist of prices and need to pick one.
- What price would actually maximise revenue?
- We need to understand the demand curve, not just an acceptable range.
- How elastic is demand at the prices we’re considering?
- Marketing wants the revenue-optimal SKU price for the launch.
No preset shortlist of prices?
If you want to discover an acceptable price rangewith no preset shortlist — that’s a Van Westendorp fit, not Gabor Granger.
See Van Westendorp →From WTP column to revenue chart.
Here's what you experience in the chat, from start to finish.
How Crowdmines compares to Excel, SPSS and survey-platform pricing modules.
Gabor Granger is typically run by research agencies in SPSS or Excel, or through survey platforms with built-in pricing modules (Qualtrics, Conjointly, XLSTAT). It’s simpler than Van Westendorp (one column instead of four), but the workflow friction is similar.
| Capability | Traditional (Excel / SPSS / R) | Survey-platform add-ons (Qualtrics, Conjointly) | Crowdmines |
|---|---|---|---|
| Setup effort | Export data, build or reuse a template, map the WTP column manually | Configure pricing module within the survey platform | Ask a question in the chat — one column to map |
| Time to result | 15–30 min for an experienced analyst | Minutes once configured | Seconds from question to demand curve |
| Demand curve + revenue | Analyst computes manually or uses a custom script | Most platforms show demand; some compute revenue | Demand curve and revenue-optimal price computed automatically, charted and narrated |
| Subgroup comparison | Re-run per group, manually stitch outputs | Some platforms support it | Type “compare loyal vs new” — automatic per-group analysis |
| Iterative refinement | Change filter, re-export, re-run | Re-configure and re-run | Follow-up question in the same conversation |
| Transition to other pricing tools | Completely separate workflow for Van Westendorp or conjoint | May be available within the same platform, but different modules with different configuration | Same chat — “now run Van Westendorp on the same data” |
| Analyst dependency | Requires trained analyst | Researcher needs to know which module to use | Self-serve for anyone who can describe the pricing question |
The chart and table that settle the pricing debate.
Drop in the WTP column. Demand curve, revenue-optimal price, written summary — back in seconds. Then ask “now run Van Westendorp on the same data” without re-uploading.