How would you analyze and test a price increase?
Company: Amazon
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: easy
Interview Round: Technical Screen
## Case Study (Product / Data Science)
You work on a subscription-based AI video editing/creation product and leadership is considering **raising prices** (e.g., increasing monthly subscription fees and/or changing packaging).
### Prompt
How would you:
1. **Analyze** whether a price increase is likely to be beneficial?
2. Decide **what price change / packaging** to ship (and for whom)?
3. Design an **experiment (or evaluation plan)** to measure the impact and make a launch decision?
### Requirements
In your answer, cover:
- Success metrics (primary + diagnostic + guardrails) and tradeoffs.
- Key segments (e.g., new vs existing users, region, creator vs casual, plan tier) and why segmentation matters.
- Confounders / risks (e.g., seasonality, competitor promos, selection bias, delayed churn).
- Practical experiment details (unit, randomization, duration, ramp plan, stopping criteria, and what you would do if you cannot fully randomize).
Quick Answer: This question evaluates pricing strategy, product analytics, causal inference and experimentation design competencies, including metric selection, cohort segmentation, confounder identification, and judgment about packaging and targeting; it falls under the Analytics & Experimentation domain for a Data Scientist role.
