Gabor-Granger Method

What Is the Gabor-Granger Method?

The Gabor-Granger Method is a survey-based pricing research technique that presents respondents with a set of pre-specified price points and records what share of buyers would purchase a product at each price. Its two primary outputs are a demand curve — showing the percentage of respondents willing to buy at each tested price — and a revenue curve that identifies the revenue-maximizing price point.

Developed by economists André Gabor and Clive Granger in the 1960s, the method carries lasting academic credibility: Clive Granger received the 2003 Nobel Prize in Economics, though for separate work on time-series analysis. To illustrate the basic setup, an industrial components manufacturer might test five price points — $80, $90, $100, $110, and $120 per unit — and track the percentage of surveyed buyers willing to purchase at each level. The price point where (price × acceptance rate) peaks becomes the candidate optimal price.

How the Gabor-Granger Method Works

Executing a Gabor-Granger study involves four structured steps.

1. Define the price range and select test points. Set a floor below which low prices signal poor quality, and a ceiling above which virtually no respondent would buy. Most practitioners use five to seven evenly spaced points within this range. Choosing too narrow a range risks missing the revenue peak; too wide a range produces acceptance rates that cluster near zero or 100%, reducing the curve's useful resolution.

1. Design the purchase-intent question. Standard phrasing asks: "At a price of [X], how likely are you to purchase this product?" with a binary or simplified response scale. Consistent wording across all price points is essential — subtle reframing between questions introduces measurement bias that distorts the resulting curve.

1. Choose a survey administration design. Two variants exist and are frequently confused:

• Sequential adaptive: Each respondent sees one starting price, then branches up if they accept or down if they decline. This approach requires a smaller sample but carries anchoring risk — early prices can bias responses to subsequent ones.
• Full-monadic: Respondents evaluate all price points independently in randomized order. This eliminates anchoring but requires a substantially larger sample to achieve stable acceptance rates at each price.

1. Aggregate, plot, and identify the revenue peak. Collect acceptance rates per price point, plot the demand curve (% accepting versus price, which should decline monotonically), then overlay the revenue index (price × acceptance rate). The peak of the revenue curve indicates the price point that maximizes theoretical revenue.

Price% AcceptingRevenue Index$8078%62.4$9065%58.5$10050%50.0$11034%37.4$12020%24.0

Gabor-Granger vs. Van Westendorp Price Sensitivity Meter

These are the two most commonly used survey-based pricing research methods, but they answer fundamentally different questions.

DimensionGabor-GrangerVan Westendorp PSMDefinitionTests acceptance at pre-set price pointsElicits open-ended price thresholds across four quality/value questionsPrimary outputRevenue-maximizing price pointAcceptable price range with psychological boundsPrice pointsResearcher-defined before fieldworkRespondent-generated during fieldworkBest used whenA candidate price range is already knownEarly discovery; no prior price range existsKey limitationCannot find optimal prices outside the tested rangeDoes not directly predict purchase probability or revenue

Use Gabor-Granger when you have a defined price range and need to identify the revenue-maximizing point. Use Van Westendorp when you are in early discovery and need to understand the full range of psychologically acceptable prices before committing to a range.

Gabor-Granger in Enterprise and B2B Pricing Contexts

Enterprise manufacturers, consumer goods companies, and distributors most often use Gabor-Granger as an upstream research input — informing list price setting, price band guardrails, and price increase decisions before changes are operationalized across channels. A manufacturer might use acceptance-rate data to establish list price floors ahead of a contract renewal cycle. A consumer goods company might use the revenue curve to calibrate price band ceilings before a SKU relaunch, ensuring the new price does not breach the point where volume loss outweighs margin gain.

One limitation is underrepresented in most published descriptions of the method: in B2B and organizational buying contexts, the survey respondent is frequently not the final purchase decision-maker. A procurement analyst may complete the survey while a committee controls actual spend authorization. This structural gap amplifies hypothetical bias and reduces the method's predictive accuracy in enterprise settings — a meaningful caution for pricing teams relying on the results to set binding price guidance.

Limitations and Strategic Risks

Practitioners should weigh five core limitations before relying on Gabor-Granger results as the sole basis for pricing decisions:

• Hypothetical bias. Stated purchase intent consistently overstates real purchase behavior. Practitioners mitigate this with certainty-scaling adjustments — discounting "likely" responses at a lower rate than "definitely" responses — but no adjustment fully eliminates the gap.
• No competitive context. The method tests a product in isolation. If a competitor shifts price during or after the study, the acceptance rates lose validity against the actual market landscape.
• Bounded by pre-specified price points. The method cannot identify optimal prices outside the tested range. A revenue peak at the ceiling of the tested range signals that the range was set too low, requiring a redesigned study.
• Assumes monotonically decreasing demand. Results should show acceptance declining as price rises. Non-monotonic patterns — where acceptance increases at a higher price — typically signal survey design errors or data quality problems the method itself cannot self-correct.
• B2B decision-maker gap. In organizational buying, respondents may lack actual purchase authority, further amplifying hypothetical bias beyond the levels observed in consumer research.

Related Terms: Van Westendorp Price Sensitivity Meter | Conjoint Analysis | Price Elasticity | Willingness to Pay | Demand Curve