Metric Selection & Product Instrumentation Questions
Techniques for turning vague business questions into measurable, actionable product metrics. Includes identifying leading vs. lagging indicators, upstream vs. downstream metrics, aligning metrics with company strategy, balancing multiple stakeholders (user satisfaction, business growth, content value), and recognizing when metrics can be misleading or require multiple signals to capture impact.
MediumTechnical
0 practiced
Your PM asks for a single metric called 'feature delight' for a newly launched recommendation feature. Describe how you would translate this vague business request into a measurable metric or composite metric. Include the steps you would take to gather candidate signals, decide weights (if any), instrument the needed events, and validate that the metric reflects user delight.
HardTechnical
0 practiced
How would you instrument and analyze a funnel that includes branching and loops (e.g., user can retry payment, go back to cart, or search again) so that conversion rates are meaningful? Describe event modeling, SQL or algorithmic approach to compute funnels that account for repeated steps, and how to present this to product owners.
EasyTechnical
0 practiced
Your product has a 3-step signup funnel: Landing -> Signup Form -> Complete Signup. Describe what events and metrics you would instrument for each step, how you would compute conversion rates between steps, and how you'd instrument to detect where users drop out. Also describe two ways to make the funnel robust to users who complete steps across different devices.
MediumTechnical
0 practiced
Describe how you would automatically detect instrumentation regressions (e.g., missing events, schema drift) in a production analytics pipeline. Outline the components of a monitoring system, what signals you'd track (event volumes, cardinality, schema changes), and how you'd prioritize fixes.
MediumTechnical
0 practiced
Describe three sanity checks and corresponding alert rules you would configure for a critical metric like 'checkout_success_rate'. For each check: explain the logic, sample SQL/pseudocode, thresholding strategy (absolute vs relative), and how you'd reduce false positives from legitimate traffic spikes.
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