Price research

Find the profit-maximizing price with two one-question surveys and a correction step, instead of guessing, copying competitors, or averaging "what would you pay?" answers. The method combines an open question (which people overstate) with a randomized yes/no question (which is unbiased but coarse) to get an accurate demand curve cheaply.

When to use

• Pricing a new product, plan, or course before launch
• Deciding whether to raise or lower an existing price
• The user proposes a "how much would you pay?" survey, Van Westendorp, or competitor matching

When not to use

• Choosing the digits or presentation of a price already decided → the pricing skill
• Unable to get ~500 completed responses (30 per closed price × 7, the same again for the open survey, plus drop-off): the method needs volume; suggest a price A/B test instead

Inputs

• Product description, images, or a demo (realism matters)
• Unit cost, reachable audience size, currency
• The lowest and highest price you can imagine charging
• Channels to recruit respondents (site, newsletter, customers, panel)
• Strategy: maximize profit now, volume/reach, or premium positioning

Procedure

1. Why not the usual methods. Averaging open "how much would you pay?" answers overstates by a large margin (often ~50%). Asking "would you buy at $X?" alone is biased by the price acting as a quality cue. Competitor matching ignores your own demand. Van Westendorp gives an "acceptable range", not the profit-maximizing point. The lab-standard auction method (BDM) is accurate but requires selling the real product and is too costly for most teams.
2. Build the price ladder. Pick 7 prices with equal steps from the lowest to the highest plausible price ($20, $40 … $140). More steps need more respondents. †
3. Write two surveys with identical product descriptions. Survey A (open): "What is the most you would pay for [product]? (in $)". Survey B (closed): "Would you buy [product] for $X? Yes / No", where X is one ladder price chosen at random per respondent. Use a tool that supports random-subset questions (Qualtrics does) and a random link splitter to send 50% to each survey.
4. Sample size. At least 30 answers per closed price → 210 for B, the same for A, plus ~80 for drop-off ≈ 500 total. † Work back from your expected response rate: at 5%, a list of 10,000 yields ~500. Recruit existing or prospective customers first; panels are a fallback. Say "one quick question" and offer a reward (coupon, prize draw). Keep it open 2–3 weeks.
5. Test before launch. Preview both surveys, open the split link in incognito several times, confirm prices randomize, delete test answers.
6. Clean. Drop $0 and absurd answers from Survey A (the script drops anything above 5× the median; adjust with --max-multiple). †
7. Correct. corrected = open_answer − mean(open) + mean(closed), where mean(closed) is the area under the yes-share curve across the ladder (the script uses the trapezoid rule, assuming 100% would buy at $0 and 0% one step above the top price). Each corrected value is an estimate of that respondent's willingness to pay; the distribution, not any single value, is what you use.
8. Demand and profit. For each ladder price: share of corrected values ≥ price; buyers = share × audience; profit = (price − unit cost) × buyers. Pick the maximum; the script also lists near-ties (within 10% of the best profit), refuses to recommend a price when none is profitable at the given cost or when any price (or the open survey) has fewer than 30 answers, and stops on any closed-survey answer that isn't a recognisable yes/no. Audience is the number of people who will actually see the offer (expected launch reach), not the size of the survey list. Unit cost is the marginal cost per sale; for digital products that is near $0 plus any per-sale fees, and the profit-maximizing price then equals the revenue-maximizing one. If two adjacent prices are within ~10% on profit, treat them as a tie and let strategy (step 9) or a live A/B test decide.
9. Override by strategy. A premium brand may choose a higher price at lower profit to attract future buyers; a reach-first launch may go lower. Say which you chose and why. †

Run scripts/wtp_calc.py --open open.csv --closed closed.csv --cost 5 --audience 8000 (open.csv: column wtp; closed.csv: columns price,answer). Example output on synthetic data (230 open answers, 30 per price on a 7-step ladder):

Open survey: 230 answers, 3 dropped (<=0 or >5x median)
Closed survey: 210 answers across 7 prices
       30.00  yes  96.7%  n=30
       60.00  yes  86.7%  n=30
       90.00  yes  40.0%  n=30
      120.00  yes  23.3%  n=30
      150.00  yes  13.3%  n=30
      180.00  yes   3.3%  n=30
      210.00  yes   0.0%  n=30

mean_open   = 125.59
mean_closed = 94.00
correction  = mean_closed - mean_open = -31.59  (applied to every open answer)
corrected mean WTP = 94.00

     price  share willing     buyers       profit
     30.00          83.7%       6696       167401
     60.00          63.0%       5040       277181
     90.00          42.3%       3383       287577
    120.00          26.9%       2150       247225
    150.00          17.2%       1374       199295
    180.00          11.9%        952       166520
    210.00           7.5%        599       122819

Profit-maximizing price: 90.00  (profit 287577 at cost 5, audience 8000)
Near-ties (within 10% of the best profit): 60.00 — treat as tied; decide by strategy or a live A/B test.
Override for strategy: a premium brand may pick a higher price; a reach-first launch a lower one.

Output

Common mistakes

• Averaging "what would you pay?" answers — overstated; needs the closed-survey correction.
• Reaching for Van Westendorp — gives a range, not the profit-maximizing point.
• Uneven ladders ($20, $50, $100) — steps must be equal for the correction to hold.
• Too many prices for the sample (10 prices with 276 answers) — under 30 per price; use 7 and run longer.
• Thin product description — respondents price what they imagine; show images or a demo.
• Picking the top-profit price blindly when the brand is premium — state the strategic override.

Quick reference

† No primary citation on file for this rule — a practitioner tip, not a measured finding.

Evidence and effect sizes: see reference.md.