How would you analyze and test a price increase?

Q: How would you analyze and test a price increase?

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.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

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:

Analyze whether a price increase is likely to be beneficial?
Decide what price change / packaging to ship (and for whom)?
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).

How would you analyze and test a price increase?

Case Study (Product / Data Science)

Prompt

Requirements

Solution

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How would you analyze and test a price increase?

Overview

Case Study (Product / Data Science)

Prompt

Requirements

Solution

Comments (0)