Gabor-Granger Pricing Method: How to Find Your Revenue-Maximizing Price

Bottom line: The Gabor-Granger method is a direct survey technique that finds your revenue-maximizing price by showing each respondent a single product at a series of prices and asking, at each one, whether they would buy. Aggregating the "yes" responses across everyone produces a demand curve (purchase likelihood at each price) and, when multiplied by price, a revenue curve whose peak is your optimal price point. It's the fastest quantitative way to answer "what should we charge?" — and it's far more reliable when the buyers you survey are the right ones and you also capture why they say no.

Pricing is the highest-leverage number in your business. A classic McKinsey analysis of the S&P 1500 found that a 1% improvement in price, with volume held constant, lifts operating profit by an average of more than 8% — a larger effect than equivalent improvements in variable cost, sales volume, or fixed cost. Yet most teams set price by copying competitors or marking up cost. Gabor-Granger replaces that guesswork with willingness-to-pay data straight from buyers.

Who created it?

The method is named after economists André Gabor and Sir Clive Granger — the latter a 2003 Nobel laureate in Economic Sciences — who developed it in the 1960s to study how price affects purchase intent. Decades later it remains a staple of pricing research because it is simple to run, fast to analyze, and produces a directly actionable output: a price.

How the Gabor-Granger method works

1. Pick a price range. Choose a realistic span of prices to test, usually five to seven points bracketing your candidate price.
2. Show one price at a time. Each respondent sees the product at a randomly selected starting price and answers a purchase-intent question ("How likely are you to buy at $X?" or a simple yes/no).
3. Adapt the next price. If they would buy, show a higher price; if they would not, show a lower one. This continues until you have found each respondent's highest acceptable price.
4. Aggregate into a demand curve. Across all respondents, calculate the share who would buy at each price. As price rises, that share falls — that is your demand curve.
5. Derive the revenue curve. Multiply purchase likelihood at each price by the price itself. The result typically rises, peaks, and falls. The peak is your revenue-maximizing price.

Because higher prices reduce the number of buyers but raise revenue per sale, the two effects trade off — and the revenue curve's peak is the price where that trade-off is optimized.

A worked example

Suppose you test a SaaS plan at $20, $30, $40, $50, and $60:

Demand falls steadily as price climbs, but revenue peaks at $40, where the higher price still retains enough buyers. Drop to $30 and you leave money on the table; push to $50 and you lose more buyers than the price increase recovers. Gabor-Granger surfaces that peak directly.

What Gabor-Granger is good at — and where it falls short

Strengths:

• Fast and inexpensive to field.
• Produces a clear, decision-ready output: demand, revenue, and an optimal price.
• Lets you estimate price elasticity for a single product.

Limitations:

• It tests one product in isolation — it ignores competitor prices and substitution (use conjoint analysis or a competitive study for that).
• Stated willingness to pay tends to overstate real willingness to pay; people say they will buy at prices they would not actually pay.
• It surfaces a revenue-maximizing price, not necessarily a profit-, growth-, or positioning-optimal one.
• It tells you what price people accept, not why — the reasoning that tells you whether you can move the curve with better packaging or messaging.

That last gap is the important one. A number without a narrative is dangerous in pricing, because the "why" is what tells you whether a low willingness-to-pay reflects the price, the perceived value, or simply the wrong audience.

How Koji modernizes Gabor-Granger research

Traditional Gabor-Granger studies run through legacy survey tools like SurveyMonkey or Qualtrics: you write the branching price logic, field it to a panel, export a spreadsheet, and hope the respondents were real buyers. Koji, an AI-native research platform, keeps the quantitative rigor but fixes the two things that most often make pricing research wrong — thin reasoning and the wrong respondents.

• Structured questions capture the numbers. Koji's six structured question types — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no — let you run the Gabor-Granger purchase-intent ladder as a series of scale or yes_no questions, producing the same demand and revenue curves you would get from a classical survey. See the structured questions guide.
• AI-moderated follow-ups capture the "why." The moment a respondent rejects a price, Koji's AI interviewer asks why — "What would make that price feel fair?" or "What were you comparing it to?" — turning a flat yes/no into a reason you can act on.
• Automatic thematic analysis clusters those reasons across all respondents, so you see not just that demand drops at $50, but that it drops because buyers anchor to a cheaper competitor — a fixable positioning problem, not a hard ceiling.
• Customizable AI consultants and screening help ensure you are talking to genuine target buyers, not professional survey-takers — the single biggest driver of pricing-research accuracy.

The payoff: instead of a spreadsheet that says "charge $40," you get "$40 maximizes revenue, and the 42% who balk at $50 do so because they compare you to a cheaper competitor — close that perception gap and the curve shifts right." Teams using AI-assisted research report substantially faster time-to-insight, turning a multi-week pricing study into a few days — and you do not need a research background to run it.

Gabor-Granger vs Van Westendorp vs conjoint

• Gabor-Granger finds the revenue-maximizing price for one product via direct purchase-intent at set prices. Best when you have a specific product and a price range in mind.
• Van Westendorp Price Sensitivity Meter maps a range of acceptable prices using four open-ended price-perception questions. Best for early-stage products with no anchor price yet.
• Conjoint analysis models how price trades off against features and brand in a realistic, competitive choice. Best when price is one of several variables and you need to understand substitution.

Many teams sequence them: Van Westendorp to find the plausible range, Gabor-Granger to pinpoint the revenue peak, and qualitative interviews to understand the "why" behind both.

Best practices

• Screen hard for real buyers. A clean curve from the wrong audience is precise and useless.
• Bracket the range generously. If demand stays high at your top price, your range was too low and you have capped your own answer.
• Pair numbers with reasons. Always capture why a price was rejected — it is the difference between lowering price and raising perceived value.
• Validate against behavior. Treat stated willingness to pay as a directional input, then confirm with a real pricing test or pre-orders where you can.
• Use enough respondents. Aim for at least ~100 qualified buyers per segment so each point on the demand curve is stable.

Related Resources

• Structured Questions Guide — run the purchase-intent ladder with scale and yes_no questions
• Van Westendorp Price Sensitivity Meter — find your acceptable price range
• Pricing Research Interviews — the qualitative "why" behind willingness to pay
• Pricing Research Survey Guide — design rigorous pricing surveys
• Conjoint Analysis Guide — model price against features and competition
• Willingness to Pay Interview Template — structure WTP conversations