Pricing Experiment Plan
Product
Product Manager
Tone: Analytical and rigorous
Goal
Create a pricing experiment plan that tests pricing changes with statistical rigor while minimizing business risk.
Context
B2B SaaS with 3 pricing tiers ($29/$79/$199). Hypothesis that $99 middle tier increases ARPU without reducing conversion. 1000 monthly trials. Cannot affect existing customers.
Constraints
- •Existing customer prices unchanged
- •60-day significance window
- •Pricing page clarity
- •No simultaneous price tests
- •Legal price transparency
Do
- Clear hypothesis and null
- Sample size calculation
- Clean A/B methodology
- Guardrail metrics
- Pre-defined decision criteria
- Plan for all outcomes
Do Not
- Do not peek before sample size
- Avoid changing parameters mid-test
- Do not skip segment analysis
- Avoid deciding on directional only
- Do not skip sales team communication
Success Criteria
- Statistically significant results
- Clear pre-defined decision
- No existing customer impact
- Learnings documented
Output Format
Experiment plan with hypothesis, methodology, and decision framework
Generated Prompt
You are a product analytics specialist. Create a pricing experiment plan for a B2B SaaS company. ## Context B2B SaaS with 3 pricing tiers ($29/$79/$199). Hypothesis that $99 middle tier (up from $79) increases ARPU without significantly reducing conversion. 1000 new trials monthly. Cannot change prices for existing customers. ## Do - Define clear hypothesis and null hypothesis - Calculate required sample size for significance - Design clean A/B test methodology - Include guardrail metrics (conversion, churn signals) - Create decision criteria before test starts - Plan for all outcomes (positive, negative, inconclusive) ## Do Not - Peek at results before reaching sample size - Change test parameters mid-experiment - Forget to segment analysis by user type - Make decisions on directional results alone - Skip communication plan for sales team ## Output Format Experiment plan: Hypothesis statement, Test design and methodology, Sample size calculations, Metric definitions, Decision criteria matrix, Timeline, Communication plan. ## Success Criteria - Statistically significant results achieved - Clear decision based on pre-defined criteria - No negative impact on existing customers
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