Gabor-Granger Method: Building a Demand Curve from Yes/No Answers

The Gabor‑Granger method is a price‑sensitivity research technique developed by André Gabor and Clive Granger, two British economists, in the mid‑1960s. Each respondent sees one product at one price and answers a simple yes/no (or 5‑point intent) "would you buy this at $X?" question. If they say yes, the next question shows a higher price. If they say no, the next shows a lower price. The yes/no staircase continues until each respondent's individual price ceiling is found.

The output is a cumulative purchase‑probability curve at the sample level. Plot the percentage who said yes at each price point against price, and you have a price‑demand curve for the product. Multiply by predicted market size and you get a volume forecast at each price. Multiply by price and you get a revenue curve, with a peak that signals the revenue‑maximizing price.

The mechanic is monadic. Each respondent evaluates one product, in isolation, with no competitor on the screen. That keeps the survey short and easy to field, but it is also the method's main limitation. Real shoppers compare. Gabor‑Granger asks them to commit before the comparison.

How it differs from the Van Westendorp PSM. PSM asks four direct questions (too cheap, cheap, expensive, too expensive) and produces an acceptable price RANGE, not a demand curve. Gabor‑Granger produces the curve but does not test the quality‑floor concerns the PSM picks up. The two methods are complementary, not interchangeable. Use PSM to find the corridor, use Gabor‑Granger to locate the optimum within it.

How it differs from conjoint. Conjoint forces respondents to trade off bundles of features at different prices, and lets you decompose willingness‑to‑pay attribute by attribute. Gabor‑Granger does not. If the product is finished and the question is "what price?", Gabor‑Granger is faster and cheaper. If the question is "which features are worth paying for?", conjoint is the only method that answers it.

At each tested price point P:

Purchase Probability(P) = share of respondents who said yes (or "definitely" / "probably" on a 5‑point intent scale)

Predicted demand:

Predicted revenue:

Revenue‑maximizing price P_rev solves dR/dP = 0.
Profit‑maximizing price P_profit solves d[(P - VC) x Q(P)]/dP = 0.

P_profit is always at or above P_rev. Once contribution margin enters the calculation, every unit you stop selling also saves its variable cost, so a price a little above the revenue peak makes more profit.

Hypothetical‑bias correction (Juster scale). Stated purchase intent overstates real‑world buying behavior. Industry practice in FMCG concept testing is to multiply the raw "yes" share by intent‑class‑specific correction factors before treating it as a probability:

• "Definitely would buy": multiply by about 0.70
• "Probably would buy": multiply by about 0.30
• "Might or might not": multiply by about 0.05
• "Probably/definitely would not": 0

These weights are standard practice in FMCG for bringing stated intent closer to real‑world conversion. Without this step the raw yes‑share badly overstates demand, and the Juster correction typically removes 40 to 60 percent of it.

CrunchField tests a new line extension, a 300g biscuit to sit beside its Original range, at six shelf prices with 400 respondents in the target consumer group. The survey prices are shelf prices, what shoppers would pay. The retailer sets the real one; CrunchField decides its list price and the shelf price it recommends.

Raw "definitely" plus "probably" would‑buy:

• $3.99: 72 percent
• $4.49: 65 percent
• $4.99: 54 percent
• $5.49: 38 percent
• $5.99: 22 percent
• $6.49: 11 percent

After Juster‑scale correction (0.70 / 0.30 weights):

• $3.99: 38 percent
• $4.49: 33 percent
• $4.99: 27 percent
• $5.49: 18 percent
• $5.99: 10 percent
• $6.49: 5 percent

Assumed market size 200,000 target consumers, 80 percent awareness, 75 percent distribution.

Predicted demand (units per month) and what shoppers would spend at each shelf price:

• $3.99: 45,600 units, $181,944 of shoppers' spend
• $4.49: 39,600 units, $177,804 of shoppers' spend
• $4.99: 32,400 units, $161,676 of shoppers' spend
• $5.49: 21,600 units, $118,584 of shoppers' spend
• $5.99: 12,000 units, $71,880 of shoppers' spend
• $6.49: 6,000 units, $38,940 of shoppers' spend

Shoppers' spend peaks at $3.99, and so do CrunchField's Net Sales, which are a fixed share of it. But CrunchField keeps only part of each shelf price, and every pack costs money to make and deliver. Assume the retailer keeps 25 percent of the shelf price, trade terms take 19.5 percent of CrunchField's list price, and the new pack costs what the Original 300g does: $1.29 to make and $0.21 to deliver, $1.50 in all (this card's assumptions). CrunchField then receives 60.375 percent of each shelf price, and once you count the cost, the best price moves up two steps.

Best shelf price for CrunchField's contribution: $4.99.
At $4.99: $161,676 x 60.375% = $97,612 of Net Sales, less 32,400 x $1.50 = $49,012 of contribution a month.
At $4.49: $177,804 x 60.375% = $107,349 of Net Sales, less 39,600 x $1.50 = $47,949 a month.
At $3.99: $181,944 x 60.375% = $109,849 of Net Sales, less 45,600 x $1.50 = $41,449 a month.

The one‑dollar step from $3.99 to $4.99 costs 28.9 percent of volume and adds 18.2 percent to CrunchField's contribution. The Gabor‑Granger demand curve made that trade‑off visible in numbers a CFO can sign off on. Without the curve, the team would likely have recommended $3.99, the price where shoppers spend the most, and given up $7,563 of contribution a month. If the retailer keeps its 25 percent, a $4.99 shelf means a list price of about $3.74; where the shelf price lands stays the retailer's call. This is the extension's own contribution. Packs it takes from the Original 300g at $4.29 are not counted here, and the Challenge's fourth question puts numbers on that cost, cannibalization, for another launch.

Price optimization is about profit. A demand curve and a contribution‑margin number, read together, are usually enough to find the right answer.

Use Gabor‑Granger when the product is finished, the price set is narrow, and the team needs a defensible demand curve fast. Common cases: pricing a line extension, validating a list‑price increase before a customer negotiation, sense‑checking a planned promotional depth.

Skip Gabor‑Granger when one of these is true:

• The product is still being defined. Conjoint is the right tool, because it lets you value features individually.
• The competitive context is the whole story. Gabor‑Granger evaluates one product in isolation. If the shopper's real choice depends on what is on the shelf next to it, no monadic test will catch that.
• The category is genuinely novel. With no reference frame, respondents anchor on whatever number you show first, and starting‑price bias dominates the result.

Three execution rules separate good Gabor‑Granger studies from bad ones.

First, randomise starting prices across respondents. Otherwise the first price you show becomes an anchor and your "demand curve" is partly a measurement artefact.

Second, choose at least five price points and bracket the realistic range generously on both sides. The output is bounded by the prices you tested. If your highest tested price still gets a 40 percent yes share, you have not found the ceiling and your revenue curve has no peak.

Third, segment the results by consumer type before drawing the demand curve. Aggregate curves average out the heterogeneity that determines actual behavior. A 60 percent yes share at $4.99 might be 80 percent for premium loyalists and 30 percent for value shoppers, and those two segments need very different commercial strategies.

What Gabor‑Granger does not catch:

• Stated‑intent bias. Real‑world purchase is swayed by promotion, distribution, shelf adjacency, and a hundred other factors not visible inside the survey.
• A gameable structure. The yes/no staircase is transparent to repeat respondents, who learn to say "no" first to bring the price down.
• Competitive context. The number you read off the curve is what the product is worth to consumers in isolation, not what it is worth versus the competitor SKU sitting next to it.

Treat Gabor‑Granger as one input among several, not as a price oracle. Before you fix a final price, check it against elasticity measured from scanner data (revealed preference) and against the acceptable range from a Van Westendorp PSM.