Pricing experiments
A founder/indie-hacker skills pack for Claude Code, Cursor, Codex and Gemini CLI: market research, competitor analysis, outcome-based pricing, landing-page copy, MVP spec, cold outreach, growth experiments and fundraising narrative.
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Design pricing and packaging experiments — A/B tests, willingness-to-pay research (van Westendorp), and tier/packaging tests. Use when the user is unsure what to charge, wants to raise prices, is choosing seat vs usage vs outcome pricing, or wants to test packaging without guessing.
SKILL.md
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pricing-experiments — test what to charge
When to use
The user asks "what should I charge?", "can I raise prices?", "how do I test a new plan?", or is deciding between seat / usage / outcome-based pricing. Pricing is the highest-leverage growth lever and the most under-tested.
Process
- Pick the model first (this constrains the experiment):
- per-seat — predictable, good for collaboration tools
- usage — aligns cost with value, good for infra/APIs
- outcome-based — charge per result delivered; strongest NRR in 2026, but you must be able to measure the outcome
- Find willingness-to-pay before A/B testing prices:
- Van Westendorp 4 questions (too cheap / cheap / expensive / too expensive) → plot to find the acceptable price band.
- Or fake-door / Stripe pricing-page test with real "Buy" intent.
- Run the test cleanly:
- Test ONE variable (price OR packaging), not both.
- Randomize by new visitor, never switch an existing customer's price mid-flight.
- Decide sample size and stop rule up front; watch conversion AND revenue per visitor (a higher price with slightly lower conversion often wins on revenue).
- Grandfather existing customers when you raise prices; announce early.
What to produce
- A recommended model with a one-line rationale.
- A concrete experiment: variants, the single variable, the metric (revenue per visitor, not just conversion), sample size, and stop rule.
- A van Westendorp question set if WTP is unknown.
Pitfalls
- Optimizing conversion instead of revenue-per-visitor → you leave money on the table.
- Testing price and packaging at once → uninterpretable result.
- Surprise price hikes on existing users → churn and trust damage.
Report back
State the chosen model, the experiment design, and the decision criterion you'll use to call a winner.