{"site":{"name":"Koji","description":"AI-native customer research platform that helps teams conduct, analyze, and synthesize customer interviews at scale.","url":"https://www.koji.so","contentTypes":["blog","documentation"],"lastUpdated":"2026-06-02T06:42:39.728Z"},"content":[{"type":"documentation","id":"cb3352f7-69db-4095-a130-00f59ef8b7f4","slug":"gabor-granger-pricing-method","title":"Gabor-Granger Pricing Method: Find Your Revenue-Maximizing Price","url":"https://www.koji.so/docs/gabor-granger-pricing-method","summary":"The Gabor-Granger method finds the revenue-maximizing price by asking respondents whether they would buy a product at a series of prices, then aggregating responses into a demand curve and a revenue curve whose peak is the optimal price. Created by economists André Gabor and Clive Granger, it is fast and decision-ready but works best when respondents are real buyers and the reasons behind each rejection are captured.","content":"# Gabor-Granger Pricing Method: How to Find Your Revenue-Maximizing Price\n\n**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.\n\nPricing 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.\n\n## Who created it?\n\nThe 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.\n\n## How the Gabor-Granger method works\n\n1. **Pick a price range.** Choose a realistic span of prices to test, usually five to seven points bracketing your candidate price.\n2. **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).\n3. **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.\n4. **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.\n5. **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.\n\nBecause 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.\n\n## A worked example\n\nSuppose you test a SaaS plan at $20, $30, $40, $50, and $60:\n\n| Price | % who would buy | Revenue index (price × %) |\n|---|---|---|\n| $20 | 85% | 17.0 |\n| $30 | 72% | 21.6 |\n| $40 | 58% | 23.2 |\n| $50 | 38% | 19.0 |\n| $60 | 21% | 12.6 |\n\nDemand 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.\n\n## What Gabor-Granger is good at — and where it falls short\n\n**Strengths:**\n- Fast and inexpensive to field.\n- Produces a clear, decision-ready output: demand, revenue, and an optimal price.\n- Lets you estimate price elasticity for a single product.\n\n**Limitations:**\n- It tests one product in isolation — it ignores competitor prices and substitution (use conjoint analysis or a competitive study for that).\n- Stated willingness to pay tends to overstate real willingness to pay; people say they will buy at prices they would not actually pay.\n- It surfaces a revenue-maximizing price, not necessarily a profit-, growth-, or positioning-optimal one.\n- 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.\n\nThat 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.\n\n## How Koji modernizes Gabor-Granger research\n\nTraditional 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.\n\n- **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](/docs/structured-questions-guide).\n- **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.\n- **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.\n- **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.\n\nThe 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.\n\n## Gabor-Granger vs Van Westendorp vs conjoint\n\n- **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.\n- **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.\n- **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.\n\nMany 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.\n\n## Best practices\n\n- **Screen hard for real buyers.** A clean curve from the wrong audience is precise and useless.\n- **Bracket the range generously.** If demand stays high at your top price, your range was too low and you have capped your own answer.\n- **Pair numbers with reasons.** Always capture *why* a price was rejected — it is the difference between lowering price and raising perceived value.\n- **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.\n- **Use enough respondents.** Aim for at least ~100 qualified buyers per segment so each point on the demand curve is stable.\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — run the purchase-intent ladder with scale and yes_no questions\n- [Van Westendorp Price Sensitivity Meter](/docs/van-westendorp-price-sensitivity-meter) — find your acceptable price range\n- [Pricing Research Interviews](/docs/pricing-research-interviews) — the qualitative \"why\" behind willingness to pay\n- [Pricing Research Survey Guide](/docs/pricing-research-survey-guide) — design rigorous pricing surveys\n- [Conjoint Analysis Guide](/docs/conjoint-analysis-guide) — model price against features and competition\n- [Willingness to Pay Interview Template](/docs/willingness-to-pay-interview-template) — structure WTP conversations","category":"Research Methods","lastModified":"2026-06-02T03:17:14.826678+00:00","metaTitle":"Gabor-Granger Pricing Method: Find Your Optimal Price","metaDescription":"Learn the Gabor-Granger pricing method: how the demand and revenue curves work, a worked example, limitations, Gabor-Granger vs Van Westendorp vs conjoint, and how to run it with AI.","keywords":["Gabor-Granger pricing method","Gabor Granger method","optimal price research","revenue-maximizing price","price sensitivity research","willingness to pay survey","price elasticity research","pricing research method","demand curve survey","Gabor-Granger vs Van Westendorp"],"aiSummary":"The Gabor-Granger method finds the revenue-maximizing price by asking respondents whether they would buy a product at a series of prices, then aggregating responses into a demand curve and a revenue curve whose peak is the optimal price. Created by economists André Gabor and Clive Granger, it is fast and decision-ready but works best when respondents are real buyers and the reasons behind each rejection are captured.","aiPrerequisites":["Basic familiarity with pricing or market research concepts"],"aiLearningOutcomes":["Explain how the Gabor-Granger method estimates demand and revenue curves","Identify the revenue-maximizing price from purchase-intent data","Recognize the methods limitations, including stated vs real willingness to pay","Choose between Gabor-Granger, Van Westendorp, and conjoint analysis","Run a Gabor-Granger study that captures both the price and the reasoning"],"aiDifficulty":"intermediate","aiEstimatedTime":"10 minutes"}],"pagination":{"total":1,"returned":1,"offset":0}}