Quantitative Research

Gabor-Granger

Gabor-Granger

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Definition:

Gabor-Granger is a quantitative pricing method used to estimate price elasticity and identify the revenue-maximising price point for a product or service. Participants are shown a sequence of prices, typically in ascending or descending order, and asked whether they would buy at each level. The resulting data plots a demand curve that reveals how purchase intent erodes as price rises. Widely used in concept testing, new product development, and pricing strategy, Gabor-Granger gives insights teams a structured, repeatable framework for pricing decisions grounded in stated consumer behaviour rather than assumption or competitive benchmarking alone.

How Conveo Does It

Conveo pairs Gabor-Granger price testing with AI-moderated video interviews, so teams capture both the willingness-to-pay curve and the reasoning behind each price response from real participants, not synthetic respondents. Studies launch in under 30 minutes and return decision-ready findings in days across enterprise-scale samples spanning 50-plus markets and languages. When a participant hesitates at a price point, Conveo's AI moderator probes in the moment, surfacing the context that a survey alone would miss.

Frequently asked questions.
Gabor-Granger is a pricing technique that presents participants with a series of price points and asks whether they would purchase at each level. By aggregating responses across a sample, researchers build a demand curve showing how purchase intent changes with price. The method identifies the price point that maximises revenue, balancing the trade-off between higher margins and lower volume as price increases.
Gabor-Granger works best when a team needs a fast, structured read on price sensitivity for a defined product or service with a realistic price range already in mind. It suits new product launches, packaging or feature tier decisions, and annual pricing reviews. It is less appropriate when the product concept is still undefined or when the realistic price range is genuinely unknown, because the method relies on presenting credible price points to participants.
Gabor-Granger asks participants to respond to specific price points the researcher sets, producing a demand curve and a revenue-maximising price. Van Westendorp asks open-ended price perception questions, such as what feels too cheap or too expensive, to map an acceptable price range without anchoring to pre-set figures. Gabor-Granger is more precise when you have a realistic price range; Van Westendorp is more exploratory when you are still defining where to play.
Traditional Gabor-Granger runs as a survey, capturing stated intent without any follow-up. AI-moderated research adds a layer of qualitative depth: when a participant declines at a price point, the AI moderator can probe immediately, asking what would need to change or what they would pay instead. This turns a demand curve into a richer data set that explains the shape of the curve, giving pricing teams both the number and the reasoning behind it.
Enterprise teams typically run Gabor-Granger during concept testing or ahead of a product launch to pressure-test proposed price points against real consumer willingness to pay. The output informs pricing strategy, tier structure, and promotional thresholds. Teams running multi-market launches use the method across regions to identify where price sensitivity differs, avoiding a single global price that underperforms in markets where consumers would comfortably pay more.
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