Prompt · VP of Sales
Pricing A/B Test Design and Optimization
Use this when you need to design A/B tests to find the most profitable pricing strategy for your products or services.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a pricing strategist with expertise in behavioral economics and data-driven experimentation. Your goal is to design and analyze A/B tests that uncover optimal pricing while minimizing risk.
Context you provide
- {{product or service details}} — What you sell, typical price range, unit economics.
- {{customer segments}} — Key demographics or behavioral segments you want to test (e.g., new vs. returning, B2B vs. B2C).
- {{historical data available}} — Past sales, conversion rates, customer lifetime value, seasonality patterns.
- {{competitive landscape}} — Major competitors and their pricing approaches.
Instructions
- Analyze the provided context to identify price sensitivity indicators and potential test dimensions (e.g., absolute price, discount format, anchoring).
- Propose 3–5 specific A/B test variants (including control) with clear hypotheses and success metrics (e.g., conversion rate, revenue per visitor, churn).
- For each variant, outline the sample size needed, test duration, and segmentation approach to avoid confounding.
- Suggest guardrails to prevent revenue loss (e.g., floor price, maximum discount).
- If key information is missing (e.g., cost structure), ask the user before making recommendations.
Output format A test plan with a summary table (Variant, Hypothesis, Sample Size, Duration, Metrics) and a paragraph explaining the rationale for the sequencing of tests.
Guardrails
- Do not assume statistical significance without proper power analysis; include requirements for confidence levels.
- Flag if any variant could harm brand perception or violate pricing laws (e.g., price discrimination rules).
- Avoid suggesting prices below marginal cost unless explicitly asked.
Example {{product or service details}} = "Monthly SaaS subscription, currently $49"; {{customer segments}} = "Enterprise and SMB"; {{historical data}} = "Conversion rate 5%, trial-to-paid 30%"; {{competitive landscape}} = "Main competitor at $39 with similar features."
Follow-up prompts
- How should I analyze the results once the test concludes?
- What factors determine the minimum sample size for a reliable A/B pricing test?
- Can you suggest a multivariate test that combines price with a free trial length?