Willingness to pay, or WTP for short, is the basic quantity behind almost every pricing decision. What matters is how it is measured: stated or revealed.
Why this matters for your decision
A target group's willingness to pay determines how many people choose an offer at which price. Price positioning, tariff structure and margin planning depend on it, and often also the question of whether a development is worthwhile at all. An error of a few percent in the price affects revenue and contribution margin over the entire lifetime of a product.
Stated willingness to pay is on average higher than the real one. A meta-analysis of 77 studies finds that hypothetical statements exceed real willingness to pay by 21 percent on average (Schmidt & Bijmolt 2020). The deviation varies widely between products and studies and is larger for higher-value products. A flat discount therefore does not solve the problem.
Willingness to pay is also not a fixed property of a person. It depends on which alternatives are visible, how the offer is described and which price was mentioned first. That is why it is worth measuring it in a situation that comes as close as possible to the later decision.
How to measure it
Direct surveys: the open question about the maximum price, or structured methods such as Van Westendorp and Gabor-Granger. Quick and inexpensive, but hypothetical.
Indirect surveys: conjoint analyses derive willingness to pay from choices between product profiles. This reflects trade-offs better, but remains a survey situation. In the meta-analysis mentioned, indirect methods overestimate even more than direct ones.
Incentive-compatible methods: with the BDM auction or incentive-compatible conjoint variants, the answer can lead to a real purchase. This reduces the tendency to set the value too high, but requires a product that can be delivered.
Observed behaviour: market data, price tests on your own channels or a behavioural test before market entry. In Horizon's Painted Door Test, each person sees exactly one price on a realistic offer page. Purchase intent is measured per price. Nothing is sold; anyone who chooses the offer learns afterwards that it is a test.
Example
An insurer is planning a supplementary dental tariff. In a survey, participants name €24 per month as appropriate on average. In the behavioural test, three prices are tested: €14.90, €19.90 and €24.90, and each person sees only one of them.
Measured sign-up intent at €19.90 is only slightly below that at €14.90, while at €24.90 it drops significantly. The range up to just under €20 holds up; the surveyed average would have set the price too high.
How it differs
Willingness to pay is not the same as an accepted price range, such as Van Westendorp delivers, and not the same as price elasticity. Elasticity describes how strongly overall demand reacts to a price change. Willingness to pay is the point at which an individual person switches from yes to no. The distribution of many individual willingness-to-pay values produces a demand curve.
The gap between stated and real willingness to pay is a special case of hypothetical bias and thus a form of the Say-Do Gap.
Limitations
A behavioural test does not deliver willingness to pay per person either, but demand per tested price. Between the price points, values are interpolated, not measured. Measured purchase intent is also not the purchase itself; the payment step is missing.
Not every pricing question fits an offer page. For products chosen on the shelf right next to competitors, for comparison-portal products or for very inexpensive items that nobody buys individually online, it does not reflect the decision. Surveys remain valuable for narrowing down the range and understanding why a price is perceived as too high.
Evidence
Schmidt & Bijmolt 2020: 77 studies, 115 effect sizes: hypothetical willingness to pay is on average 21% above the real one. Indirect methods overestimate more than direct ones, within-subject designs more than between-subject designs, higher-value products more than inexpensive ones. Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias, Journal of the Academy of Marketing Science 48(3). Source
Miller et al. 2011: Comparison of the open question, choice-based conjoint, BDM and incentive-compatible conjoint with real purchases: BDM and incentive-compatible conjoint pass the tests; hypothetically biased methods can also lead to the right pricing decision in individual cases. How Should Consumers' Willingness to Pay Be Measured? An Empirical Comparison of State-of-the-Art Approaches, Journal of Marketing Research 48(1). Source
Wertenbroch & Skiera 2002: In three studies, the incentive-compatible BDM method yields lower willingness to pay than non-incentive-compatible methods (open question, double-bounded contingent valuation); the difference stems from the binding nature of the answer, not from cognitive effort. Measuring Consumers' Willingness to Pay at the Point of Purchase, Journal of Marketing Research 39(2). Source
Frequently asked questions
How accurate is stated willingness to pay?
Too high on average, but not uniformly. A meta-analysis finds an average overestimation of 21 percent, with a wide spread between studies. A survey is useful for a first range, less so for the specific price.
Can the overestimation simply be calculated out?
Only roughly. The deviation varies by product, category and method, so a flat discount is itself an assumption.
What exactly does Horizon measure?
Measured purchase intent per price: how many people choose the offer on a realistic offer page at exactly that price. Each person sees only one price, and nothing is sold.
How high is the willingness to pay for your offer?
Bring your pricing question, and we will outline a possible test design.
You will speak with Daniel Putsche
Founder & CEO, 30 minutes
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