This is the kind of question where your first instinct is wrong and you kind of have to let yourself be wrong for a second before course-correcting.
Start by clarifying the metrics and the experimental design, then generate distinct behavioral hypotheses that could explain the divergence between session length and DAU. For each hypothesis, propose a specific test using existing data or a follow-up experiment, and discuss how you would interpret the results to inform product decisions.
Pro tip: Acknowledge that shorter sessions might not be bad if users are finding what they need faster, and that higher DAU could reflect increased engagement frequency. Emphasize the importance of guardrail metrics like user satisfaction and long-term retention to ensure the change is net positive.
Define session length and DAU precisely, and confirm the experiment design (e.g., A/B test, rollout). Check for novelty effects, seasonality, or external factors that could confound results.
Brainstorm plausible reasons for shorter sessions but higher DAU, such as increased content discovery leading to more frequent but shorter visits, or users consuming snippets and leaving satisfied.
Rank hypotheses by likelihood and impact, and identify existing data sources (e.g., logs, surveys) that can provide initial evidence for or against each.
For each hypothesis, outline a specific test: could be a deep-dive analysis, a follow-up A/B test, or qualitative research. Specify metrics, success criteria, and potential confounds.
Explain how you would synthesize results to determine if the change is beneficial overall, considering trade-offs and long-term effects. Suggest next steps like iteration or guardrail monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.