How would you test a price increase?

Quick Overview

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.

How would you test a price increase?

Company: Amazon

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

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: 1) **Should we raise the price?** 2) **By how much, and for which segments/plans?** 3) **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

Quick Answer: 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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Dec 20, 2025, 12:00 AM
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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:

  1. Should we raise the price?
  2. By how much, and for which segments/plans?
  3. 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
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