I went straight to DAU/MAU and kind of rambled from there.
Start by clarifying the product and its goals, then define adoption and engagement with specific metrics, and finally tie them to business outcomes. Structure your answer around a framework like HEART or AARRR, and emphasize how you'd measure and act on these metrics as an engineer.
Pro tip: Show that you understand the difference between vanity metrics and actionable metrics, and give an example of how you'd instrument or use data to drive a product decision. This demonstrates product sense and engineering empathy.
Ask questions to understand the product, its target users, and business objectives. This ensures your metrics are relevant and aligned with what matters.
Distinguish adoption (first-time use, activation) from engagement (ongoing interaction, depth of use). This shows you understand the user lifecycle.
Choose metrics like DAU/MAU, retention rate, session frequency, time spent, feature usage, and conversion rates. Explain why each matters.
Identify which metrics are most critical for the product stage and how they interconnect (e.g., adoption feeds engagement). Avoid metric overload.
Explain how you'd use these metrics to inform product improvements, experiments, and engineering decisions. Show a feedback loop.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.