I jumped straight to engagement metrics like streams and skip rate, which felt right but also pretty obvious.
Start by clarifying the goal of the recommendation algorithm—likely to improve user engagement and satisfaction—then define a primary success metric (e.g., listening time) and guardrail metrics (e.g., skip rate, diversity). Propose an A/B test to measure impact, and consider long-term metrics like retention and user lifetime value.
Pro tip: Don't just list metrics; tie them to Spotify's business model (subscription and ad revenue) and emphasize the importance of balancing short-term engagement with long-term user satisfaction to avoid over-optimizing for clicks.
Confirm the algorithm's objective (e.g., increase engagement, improve discovery) and state a hypothesis about its impact on user behavior.
Choose a primary metric (e.g., daily listening time per user) and secondary metrics (e.g., saves, playlist adds) that directly reflect the goal.
Select metrics to monitor unintended consequences, such as skip rate, song diversity, and user retention, ensuring no harm to user experience.
Propose an A/B test with a control group, define sample size and duration, and specify how to measure statistical significance.
Consider metrics like retention, churn, and lifetime value to assess whether short-term gains translate to sustainable business value.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.