This one sprawled in ways I didn't expect.
Start by clarifying the feature's goal (e.g., reduce typing effort, increase response rate) and then define success metrics that directly measure that goal, along with guardrail metrics to ensure no harm. Design an A/B test with appropriate randomization, sample size, and duration, and outline diagnostics for inconclusive results, such as segment analysis, metric sensitivity, and novelty effects.
Pro tip: Emphasize that guardrails should include both user experience (e.g., dismissal rate) and system health (e.g., latency), and consider long-term holdout groups to detect novelty effects.
Understand the auto-reply suggestion feature's purpose: to reduce user effort and increase engagement. Identify key user actions (e.g., accepting suggestions, sending replies) and potential risks (e.g., annoyance, privacy concerns).
Choose primary success metrics like suggestion acceptance rate or messages sent per user. Select guardrail metrics such as dismissal rate, app latency, or user retention to ensure no negative impact.
Propose an A/B test with random assignment, control for confounders, calculate sample size for desired power, and set duration to capture stable behavior. Consider using a long-term holdout to measure novelty effects.
If results are inconclusive, check for metric sensitivity, segment by user demographics or behavior, analyze novelty/primacy effects, and verify experiment implementation (e.g., logging, randomization).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.