Willingness to pay: turn stated amounts into a price test
Work through fictional willingness-to-pay thresholds for a refill offer. Compare candidate prices with costs, context and objectives before choosing a range.
Willingness to pay concerns the amount someone would exchange for a defined offer in a specific situation. A direct maximum-price question supplies a stated threshold, not a purchase commitment. Keep rejection and missing information separate, use the distribution to choose feasible candidate prices, then test complete displayed offers and actual buying when that is the decision.
Willingness to accept asks the minimum compensation someone would require to give up a product or benefit. That differs from the maximum a buyer would pay to obtain an offer. For a subscription pricing decision, do not substitute one question for the other.
Make the subscription answerable
Imagine you are considering a refill subscription for households: two 500 ml refills each month, with delivery included. You need candidate monthly prices for a further test. This illustrative example uses stated maximum amounts; define the quantity, commitment and alternatives so buyers know what they are evaluating.
This offer supplies two 500 ml refills each month with delivery included. Thinking about your current alternatives and household budget, what is the highest monthly price at which you would seriously consider subscribing?
Allow a monetary amount, rejection of the subscription, and not enough information. Follow with the reason: what alternative, budget or expected use informed the answer? Someone can value the refills and still reject a monthly commitment. A question that forces everyone to type a number would conceal that distinction.
Decide whether a maximum is a task buyers can meaningfully perform. If people struggle to imagine the offer or produce round guesses, a displayed-price cell or concrete choice can be more direct. Do not interpret specificity to cents as evidence that the person has a stable threshold.
Build the curve from the actual ten values
The ten maximum-price answers in the example are $9, $11, $12, $14, $14, $16, $18, $19, $22 and $28.
Ten thresholds narrow the next price test
For each candidate monthly price, count answers at or above that amount. The table shows those exact points, rather than estimating demand between prices.
Candidate monthly price | Count and share |
|---|---|
$10 | Values at or above price: 9 of 10 Share of the ten amounts: 90% |
$12 | Values at or above price: 8 of 10 Share of the ten amounts: 80% |
$15 | Values at or above price: 5 of 10 Share of the ten amounts: 50% |
$20 | Values at or above price: 2 of 10 Share of the ten amounts: 20% |
These shares follow a simplifying threshold rule: a value at or above the price counts as willing. They show how a curve can be constructed, not how likely ten actual people are to purchase. The median is $15, halfway between the fifth value ($14) and sixth ($16). The mean is $16.30. Neither statistic is automatically the right commercial price.
The threshold arithmetic has a precise meaning: at price p, count numeric values at or above p and divide by ten. The $12 point includes 12 itself. The $15 point includes $16, $18, $19, $22 and $28. It does not include either $14. Anyone reconstructing the figure can follow that same rule.
This also shows why the central value is an incomplete recommendation. The median is $15 even though none of the ten people supplied exactly $15. It sits between the two middle thresholds. The mean of $16.30 is lifted by the upper tail. Neither calculation contains production, delivery or acquisition economics.
Identify which uncertainty the curve ignores
A threshold curve assumes a person whose maximum is $16 would consider the same offer at $15 and all lower prices. That monotonic rule makes the worksheet easy to interpret. Real responses can violate it: a very low price can imply poor quality, an alternative may be available only at one channel, or the person may reinterpret the subscription after seeing another amount.
The stated curve also assumes the answer remains meaningful at the point of purchase. Household budgets, stock, delivery windows and habit can intervene. Use those reasons to define the next comparison instead of applying an arbitrary discount to convert stated interest into a sales forecast.
For the refill offer, ask whether delivery is valuable because supplies are hard to obtain, or merely attractive in principle. A household that already buys refills cheaply on its normal shop may give a low amount without disliking the product. A household with unreliable access may give a high amount for convenience. Those mechanisms suggest different targets and offer tests.
Do not turn every blank into zero
Answer to the refill prompt | Amount, state and follow-up |
|---|---|
“$14 per month” | Amount field: 14 Response state: Numeric consideration threshold Follow-up: What alternative and budget informed it? |
“Not for me at any price” | Amount field: Blank unless a stated coding rule requires zero Response state: Rejects the offer Follow-up: Which feature or occasion makes it unsuitable? |
“Does it include the container?” | Amount field: Blank Response state: Insufficient information Follow-up: Clarify the inclusion consistently before retesting |
No answer | Amount field: Blank Response state: Missing Follow-up: Check exposure and survey completion |
These additional response states do not change the ten-value example. In a real study, save the original amount and unit beside the state. “Does it include the container?” is an information gap; “not for me at any price” is offer rejection; no answer is missing evidence. Coding all three as zero mixes three different actions.
Report the numeric-threshold distribution and an all-eligible view under a declared rule. If valid rejection belongs in the latter, show it. If insufficient information is excluded from the numeric curve, show that base too. Neither a favorable curve from amounts only nor a pessimistic curve from zero-filled blanks should hide the people whose decision remains unresolved.
Investigate $28 before deciding it is an error
The high value is useful because it asks the team to inspect context. Did the respondent understand two refills each month? Were they thinking about emergency delivery, a larger quantity or a costly current alternative? A verified unit misunderstanding can be corrected under a documented rule; an unusual but coherent use should remain visible.
For this set, removing $28 would change the mean to $15 and the numeric base to nine. That is arithmetic sensitivity, not a reason to delete the answer. The corrected description of the decision would have to say why the record was removed. A median can be robust to a high value while still failing to distinguish households the launch must serve.
Likewise, a low maximum may describe affordability, low expected usage or opposition to subscriptions. Reasons can reveal an audience opportunity or an offer problem that a central statistic cannot. The next research brief should use that explanation, not only carry forward a lower average.
Choose an observation that matches the next claim
A stated question lets people evaluate an unlaunched offer and explain an objection. A transaction records buying under actual exposure, price, promotion and availability. Historical sales at two prices do not alone identify a causal price effect, because those conditions may have changed together.
If the next decision is “which monthly price deserves a trial?”, compare displayed feasible offers with the intended audience. If it is “will people reorder?”, observe repeat under a suitable pilot. Keep the trial’s opportunity base and duration with the result; a reorder count without knowing who had a chance to reorder does not answer the question.
For modeled thresholds, inspect task-relevant grounding and validation. The chart is still a summary of modeled answers, even when it resembles a demand curve. More rows or a smoother line do not change the evidence source.
Take $12 and $15 into a feasible-offer comparison
Eight numeric thresholds remain at $12 and five at $15. Those points create a clear next question. First confirm that either amount can cover the two refills and delivery under the business objective. Then compare the unchanged subscription at the two amounts, preserving comprehension, full intent categories and rejection reasons.
The threshold comparison cannot choose a winner without those economics and objectives. It can reduce a vague “what should we charge?” question to a candidate-price test. If neither amount works commercially, revise quantity, cadence, channel or commitment before testing the changed offer.
The useful deliverable names the candidates, the exact offer and audience, the objective and the next behavior evidence. That is more actionable than a headline willingness-to-pay figure detached from the subscription people imagined.
Further reading
- our pricing research guide
- our monadic pricing guide
Bring the decision, audience and evidence needed to a scoped Sapien study discussion.
