This is the kind of question where you can ramble forever if you're not careful.
Start by defining a metric tree that breaks down DAU into its components (new, retained, resurrected users) and segments (platform, region, content type). Then systematically analyze each component to identify the source of decline, and design targeted experiments to test hypotheses about root causes.
Pro tip: Always validate metric definitions and data quality first—declines are often due to tracking bugs or seasonality. Also, consider external factors like competitor launches or app store changes before diving into product changes.
Define DAU precisely (e.g., unique users who open the app daily) and break it down into acquisition, retention, and resurrection metrics. Validate data pipelines to rule out tracking issues.
Slice DAU by dimensions like platform (iOS/Android), region, user tenure, and content categories to identify where the decline is concentrated. Use cohort analysis to see if specific cohorts are driving the drop.
Generate hypotheses based on segments (e.g., a new feature caused churn, algorithm change reduced engagement). Use funnel analysis, retention curves, and engagement metrics (time spent, videos viewed) to narrow down causes.
If a hypothesis points to a product change, design an A/B test or quasi-experiment (e.g., holdout groups, switchback tests) to measure impact. Ensure proper power analysis and guardrail metrics.
Based on findings, recommend fixes (e.g., revert feature, improve onboarding) and set up dashboards to monitor recovery. Define success metrics and iterate if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by acknowledging the challenge of causal inference with observational data, then propose a method like instrumental variables or propensity score matching, and finally explain its validity using a simple analogy that a non-technical person can grasp.
Pro tip: Emphasize that while observational methods can provide strong evidence, they rely on assumptions; always validate with sensitivity analysis and, if possible, complement with a small-scale experiment.
Clarify that the goal is to estimate the causal effect of network speed on TikTok usage, and note that observational data may have confounding factors like user demographics or time of day.
Select a method such as instrumental variables (e.g., using distance to cell towers as an instrument for network speed) or propensity score matching to control for confounders.
Use an analogy: like comparing two groups of people who are similar in all ways except network speed, to isolate the effect of speed on usage.
Mention key assumptions (e.g., the instrument affects usage only through network speed) and suggest sensitivity checks to ensure robustness.
Propose running a small randomized experiment or using natural experiments to confirm findings, and communicate results with confidence intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.