Identify Causes and Validate Web Product Performance Drop

Q: Identify Causes and Validate Web Product Performance Drop

This question evaluates root-cause analysis, data validation, causal inference, and experimentation design skills for diagnosing unexpected drops in DAU and conversion rates within the Analytics & Experimentation domain.

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

Scenario

Daily active users (DAU) and conversion rate for a web product unexpectedly drop.

Tasks

Enumerate plausible root causes and describe the specific analyses you would run to validate or rule out each cause.
Propose how you would design an A/B experiment to test a fix for the identified issue.
Specify primary, secondary, and guardrail metrics to track in the experiment, and explain why.

Hints

Consider instrumentation/data pipeline issues, release rollouts, and external events.
Outline power, sample size, expected duration, and metric sensitivity/variance for the experiment.

Identify Causes and Validate Web Product Performance Drop

Scenario

Tasks

Hints

Solution

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