You have a product or service ready for market, a shortlist of candidate price points, and a straightforward question: at each of those prices, how many people will actually buy? Most internal pricing debates stall because everyone has an opinion but nobody has data. The Gabor-Granger pricing method gives you direct, quantified demand estimates at specific price points so you can set a price that maximizes revenue, not just internal consensus.
Developed by economists Andre Gabor and Clive Granger in the 1960s, this pricing method remains one of the most efficient ways to build a demand curve and calculate price elasticity for an existing or clearly defined product. At Meraki Research, we use Gabor-Granger within our broader Pricing Research practise to answer the question that every pricing decision ultimately comes down to: what is your customer's willingness to pay, and how does it change as price moves up or down?
Our team has fielded Gabor-Granger studies for brands across consumer goods, technology, healthcare, and professional services. We know where this method shines, where it has blind spots, and how to pair it with complementary approaches for a complete pricing picture. When businesses need a direct demand estimate tied to specific price points -- not just an acceptable range -- Gabor-Granger delivers that precision.
Our Approach: The Demand Curve Method
The Gabor-Granger technique is built on a deceptively simple mechanism: show a respondent a clearly defined product at a specific price, ask if they would buy it, then adjust the price up or down based on their answer. The result is a purchase probability at every tested price point for every individual, which rolls up into a market-level demand curve.
We follow a four-phase process we call the Demand Curve Method to make sure every GG study we run produces pricing decisions, not just pricing data.
Phase 1: Product Definition and Price Ladder Construction
The first component of any Gabor-Granger study is a precise definition of what the respondent is evaluating. We work with your team to build a concept card that includes the product or service description, key attributes, brand identity, and any relevant contextual information (distribution channel, purchase occasion, competitive alternatives). Vague descriptions produce vague price responses.
Next, we construct the price ladder -- the set of price points that will be tested. Typically we test five to seven price points spanning the realistic range. If you have already run a Van Westendorp study, we use those results to anchor the ladder within the acceptable price corridor. If not, we derive the range from your current pricing, competition benchmarks, and cost-structure constraints.
Phase 2: Sequential Monadic Pricing Exercise
Each respondent enters the exercise at a randomly assigned starting price from the ladder. They see the product concept at that price and indicate their willingness to purchase on a calibrated intent scale. Based on their response:
- If they would buy, the next question presents a higher price point.
- If they would not buy, the next question presents a lower price point.
This sequential approach is more efficient than showing every price to every respondent because it zeroes in on each individual's threshold quickly, typically requiring only three to four price exposures per person. The random starting point eliminates order bias -- some respondents anchor high, others anchor low, and the aggregate result washes out individual starting-point effects.
Phase 3: Demand Curve Construction and Price Elasticity Estimation
From the individual-level responses, we build a demand curve that plots purchase probability against price. This curve is the core deliverable because it answers the fundamental Gabor-Granger question: how does demand change as price changes?
We then calculate price elasticity -- the percentage change in demand for a given percentage change in price. Elastic segments of the curve (where small price changes produce large demand shifts) are dangerous zones for price increases. Inelastic segments (where demand holds steady despite price movement) represent pricing headroom you may not be exploiting.
We also compute the revenue-maximizing price -- the point on the curve where price multiplied by estimated demand volume is highest. This is distinct from the profit-maximizing price, which requires cost data your team provides. Both numbers go into the final estimate.
For products or services sold across multiple segments or channels, we build separate demand curves for each group. A brand selling through both direct and retail channels, for example, may find very different elasticity profiles -- and therefore different optimal price points -- in each. These segment-level curves are among the most strategically valuable components of the analysis.
Phase 4: Strategic Pricing Recommendations
The demand curve and elasticity estimates are the raw materials. Our team translates them into a pricing recommendation that accounts for your brand positioning, competitive dynamics, channel requirements, and margin targets.
We present multiple pricing scenarios: a revenue-maximizing scenario, a penetration-pricing scenario, a premium scenario, and any custom scenarios your team wants to model. For each scenario, we estimate the demand implications, revenue impact, and competitive exposure. The goal is to arm your pricing decision-makers with a clear set of trade-offs, not a single "right answer."
We also benchmark your Gabor-Granger results against competition pricing where possible. If your revenue-maximizing price falls well below the market average, that signals either a brand perception gap or an untapped value story. If it sits above the competition, you may have pricing headroom that your current pricing method is not exploiting.
When Gabor-Granger Is the Right Pricing Method
Gabor-Granger works best when:
- You have a well-defined product or service and a realistic set of candidate price points.
- Your primary question is demand estimation -- how many people will buy at price X versus price Y?
- You want to calculate price elasticity and find the revenue-maximizing price.
- You need results quickly -- GG studies are efficient to field and analyse, often completing in three to four weeks.
- The product is relatively straightforward, meaning the purchase decision is primarily about price rather than complex feature trade-offs.
Gabor-Granger is not the best tool when the decision involves multiple attributes beyond price. If you need to understand how price interacts with features, brand, or service levels, Conjoint Analysis or Discrete Choice Modeling provides that trade-off modelling capability. If you do not yet know the right price range to test, start with Van Westendorp to establish boundaries, then use Gabor-Granger within that range.
We are also candid about a key limitation: Gabor-Granger measures stated willingness to pay, not revealed preference. Respondents in a survey context tend to overstate purchase intent relative to real-world behaviour. We calibrate for this by applying purchase-intent adjustment factors derived from category norms and, when available, your own historical conversion data.
What We Deliver
Every Gabor-Granger engagement produces deliverables designed to support a specific pricing decision:
- Demand Curve Visualization: A clear plot of purchase probability versus price, annotated with the revenue-maximizing price point and key elasticity thresholds.
- Price Elasticity Report: A detailed analysis of how sensitive demand is at each price point, identifying where you have pricing power and where you do not.
- Revenue and Margin Scenario Models: Multiple pricing scenarios showing projected demand, revenue, and (with your cost inputs) margin at each candidate price.
- Segment-Level Demand Curves: Separate curves for key customer segments, showing how willingness to pay varies across your audience. This analysis complements our Market Segmentation Research offering.
- Executive Recommendation Deck: A 25-to-35-minute presentation that walks your pricing committee through the data, the scenarios, and our recommended price path.
A standard Gabor-Granger study typically moves from kickoff to final deliverables in three to five weeks, depending on sample complexity and the number of products or price tiers being tested.
Industry Applications
Consumer Packaged Goods: Testing price points for new SKUs, line extensions, or pack-size changes where the product concept is well understood and the primary variable is price.
Technology and SaaS: Estimating demand at different subscription price tiers, per-seat pricing levels, or one-time license fees for B2B and B2C products.
Healthcare: Gauging patient and payer willingness to pay for elective procedures, out-of-pocket therapies, and wellness services where price transparency is increasing.
Professional and Financial Services: Testing fee structures, advisory price points, and premium service tier pricing where the purchase decision is high-consideration.
Retail: Optimizing everyday and promotional price points for private-label and national-brand products across categories.