Pricing experiment designer

When to use

Before changing a price, adding a tier or discount, or when reading the results of a change.

Inputs

• Current price(s) and what's included.
• Real numbers for the last 2–4 weeks: visits to the product page, checkouts started, sales, refunds.
• The idea to test (higher price, lower price, tiers, bundle, discount, pay-what-you-want).
• Constraints: can the platform split traffic? (Most small stores can't, so tests run sequentially.)

Steps

1. State the hypothesis with a number: "Raising $19 → $29 will cut conversion by less than a third, so revenue per visitor goes up."
2. Pick one metric: revenue per visitor (sales × price ÷ visitors). Conversion rate alone hides price effects; revenue alone hides traffic swings.
3. Check the sample. With fewer than ~30 sales per variant, results are noise. Compute the visits needed from the current conversion rate. If it would take more than 6 weeks, say so and recommend a bigger change (bigger price gap, new tier) or a qualitative test (ask 10 buyers) instead.
4. Design the run. Split traffic if possible; otherwise run A then B for equal full weeks, avoiding launches, holidays and promotions. Change nothing else during the test.
5. Stop rules. Decide in advance: the end date, and a safety stop (e.g. refunds above 10%).
6. Reading the result. Compare revenue per visitor with a rough interval. Call it a win only if the difference survives the noise; otherwise call it "no detectable difference" and keep the simpler price.
7. Honesty checks. Discounts must be real (the "before" price was actually charged). No fake countdowns or fake scarcity.

Output format

HYPOTHESIS:
METRIC: revenue per visitor (current: $X from N visits)
VARIANT(S):
SAMPLE NEEDED: ~N visits per variant (≈ W weeks at current traffic)
RUN PLAN: dates, what stays fixed
STOP RULES:
DECISION RULE: what result means keep / revert / inconclusive

Rules

• Use only numbers you were given or can compute. If traffic is unknown, the first task is measuring it.
• Never recommend deceptive tactics (fake original prices, fake timers).

Worked example

Input: "Notion templates at $12. Last 4 weeks: 2,100 product-page visits, 42 sales, 1 refund. Thinking of $19."

Output:

HYPOTHESIS: At $19, conversion falls from 2.0% to no lower than 1.3%, so revenue per visitor rises
  from $0.24 to at least $0.25.
METRIC: revenue per visitor. Current: 42 × $12 / 2,100 = $0.24.
VARIANT: $19, same product, same page copy apart from the price.
SAMPLE NEEDED: at ~525 visits/week and ~2% conversion, 4 weeks per variant gives ~40 sales at $12
  and ~27 at $19 if the hypothesis holds. That's borderline; plan 4 weeks of B, and treat a result
  within ±25% as inconclusive.
RUN PLAN: A = the last 4 weeks (already measured). B = 4 full weeks from Monday 5 Oct at $19. No
  newsletter pushes or discounts in either period. Same traffic sources.
STOP RULES: end on 1 Nov. Stop early if refunds exceed 3 in B (they were 1 in A).
DECISION RULE:
- B revenue/visitor ≥ $0.30 → keep $19.
- $0.19–$0.30 → inconclusive. Keep $19 only if you prefer fewer, better-fit buyers; otherwise revert.
- < $0.19 → revert to $12.
Note: EARLY-style discount codes during B would contaminate it; pause them.