Evaluate Auto-Reply Feature Success with Metrics and Experiments
Overview
This question evaluates product analytics and experimentation skills, including metric selection, causal inference, experiment design, and diagnostic analysis for an auto-reply suggestion feature.
Google
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
13
0
Evaluate and Improve an Auto‑Reply Suggestion Feature
Background
A chat product ships an auto‑reply suggestion feature (e.g., "Thanks!", "Sounds good") that appears while composing or viewing a message. You are asked to evaluate whether the feature creates value and how to improve it.
Tasks
- Define primary success metrics that capture user value and adoption, and guardrail metrics that ensure no harm to user experience or business.
- Design an experiment to measure the feature's causal impact, including unit of randomization, rollout plan, power analysis, and analysis approach.
- If results are inconclusive, list additional diagnostics you would run to identify issues and opportunities.
Notes
- Consider send‑rate, click‑through/acceptance, recall/delete, typing latency.
- Monitor retention, spam/abuse, and revenue.
- Propose an A/B test or ramped rollout with robust power analysis.
Solution
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