Start by defining a composite quality metric that balances user engagement and negative feedback, then use historical CTR distributions to set performance benchmarks. For the A/B test, outline a clear hypothesis, randomization unit, and success metrics, and plan for deep analysis including segmentation and novelty effects.
Pro tip: Always consider the trade-off between short-term CTR and long-term user retention; a notification that boosts clicks but increases opt-outs is not high-quality. Also, ensure your A/B test accounts for network effects and interference, especially in social apps like Meta's.
Propose a metric that combines positive signals (e.g., click-through rate, conversion rate) and negative signals (e.g., dismissal rate, opt-out rate, report rate) into a single score, such as a weighted sum or ratio. Justify why this captures 'quality' from both user and business perspectives.
Analyze historical CTR data to understand its distribution (e.g., mean, median, percentiles) and trends over time. Use this to set thresholds for what constitutes 'good' or 'bad' performance, considering seasonality and user segments.
Define the hypothesis, control and treatment groups, randomization unit (e.g., user), and sample size calculation. Specify primary and guardrail metrics, and outline the test duration to capture full user behavior.
Beyond average treatment effect, perform segmentation analysis (e.g., by user demographics, past engagement), check for novelty effects, and evaluate long-term impact on retention. Use statistical tests to ensure significance and consider practical significance.
Combine findings from the metric, historical analysis, and A/B test to make a recommendation on whether to ship the new strategy, iterate, or abandon. Discuss potential trade-offs and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.