A/B Testing for Pricing & Packaging
Letting data, not opinions, set the price.
Workflow
Step 1Hypothesis Development
ObjectiveDefine what pricing hypothesis to test
InputPricing concerns, customer feedback, competitive data
OutputClear test hypothesis with success metrics
Key activities
Identify pricing question to answer, develop hypothesis, define primary metric, define success threshold, estimate sample size needed
Decision points
Hypothesis clear? Metric meaningful? Sample size adequate?
Tools: Hypothesis templates, statistical calculatorsRoles: Analytics (design), Product marketing (hypothesis)
Success: Hypothesis documented and approved
Quality gate: Hypothesis approved
Why it matters
- Price is too important to guess. Testing turns pricing into an evidence-based decision that compounds across every customer.
Best practices
- Test one variable at a time.
- Define significance before you test.
- Watch conversion AND revenue — a higher price may convert less and earn more.
- Capture learnings for the next test.
Common mistakes
- Testing on vibes with no hypothesis.
- Stopping early on a noisy result.
- Optimizing conversion while ignoring revenue per customer.
Key questions
- What price hypothesis would move revenue most?
- Is the result significant, or is it noise?
Agents that drive this
Pricing StrategistAttribution
Tools we use · alternates in [ ]
Metabase[ Looker Studio, Power BI, Grafana ]Supabase[ Postgres, Firebase ]