I started with predictive power which felt right, basically does this metric correlate with outcomes we actually care about.
Start by clarifying the current metric and the proposed one, then evaluate the new metric against key criteria: alignment with the product goal (meaningful interactions), statistical validity, and practical feasibility. Finally, propose a test plan to compare the new metric with the existing one and assess its impact on decision-making.
Pro tip: Emphasize that a metric is only useful if it drives better decisions; suggest running a retrospective analysis to see if the new metric would have led to different (and better) product decisions in the past.
Clarify what 'meaningful interactions' means, how the current metric is defined, and what specific problem the new metric aims to solve. Ask for the exact definition and calculation of the proposed metric.
Assess whether the new metric aligns with the company's north star and product objectives. Consider if it captures the intended user behavior and if it could be gamed or misused.
Check the metric's reliability, sensitivity, and validity. Does it have low variance? Is it sensitive to changes in user behavior? Does it correlate with other key metrics and long-term outcomes?
Evaluate the cost and complexity of computing the metric, whether it can be measured in real-time, and if it can be used for experimentation and decision-making. Also consider potential unintended consequences.
Suggest running a backtest or A/B test to compare the new metric with the current one. Define success criteria, such as improved correlation with user retention or better sensitivity in experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.