How would you test a price increase?

Q: How would you test a price increase?

This Analytics & Experimentation question evaluates pricing analysis, elasticity estimation, A/B and multivariate experiment design, metric selection, causal inference and rollout/risk-management skills for subscription products.

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Question

You are a data scientist at a B2C AI video editing software company (subscription-based, with a free trial and paid tiers). Product leadership is considering raising prices (e.g., increasing the monthly price of the Pro plan by 10–25%).

Design an analysis and experimentation plan to answer:

Should we raise the price?
By how much, and for which segments/plans?
How do we roll it out while minimizing risk?

In your answer, address:

Key success metrics and guardrails (including tradeoffs)
How you would estimate price sensitivity/elasticity
An A/B (or multivariate) experiment design, including unit of randomization and how you’d handle existing subscribers
Major confounders and biases to watch for (e.g., seasonality, selection effects, plan switching)
How you would interpret results and decide a rollout plan

How would you test a price increase?

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