Design and Product Analytics Questions
Using quantitative metrics to inform product and design decisions. Covers key user engagement metrics such as conversion rates, task completion, retention, and feature adoption, and how to instrument and interpret these signals using analytics platforms and product dashboards. Explains how quantitative data complements qualitative research, how to identify design problems from metrics, design experiments and metrics for validation, and how to translate findings into design priorities and success criteria.
HardTechnical
71 practiced
Explain propensity score matching (PSM). Provide a product example where PSM could be used to estimate the effect of a non-randomized feature rollout, outline the steps to implement PSM, and discuss limitations.
HardSystem Design
73 practiced
Design a lightweight analytics pipeline to support product dashboards for near-real-time feature flags and conversion metrics. Include data sources, ingestion method, transformation steps, storage choices, latency targets, and how you'd ensure data correctness.
MediumSystem Design
73 practiced
Design a measurement plan for a new onboarding flow targeted at increasing trial-to-paid conversion for SaaS. Include primary/secondary metrics, event names and properties to collect, sample segmentation, guardrail metrics, and dashboard mockups (describe layout).
HardTechnical
102 practiced
Technical coding: Given a table events(user_id, event_name, event_time) and a list of funnel steps, implement (in Python/pandas) a function that computes per-user funnel completion step (highest step reached) and returns aggregated conversion counts for each step. Include handling of out-of-order events and duplicate events.
HardTechnical
51 practiced
You are given two metrics: 'time-to-first-value' (TTFV) and 7-day retention. You suspect TTFV mediates the effect of an onboarding change on retention. Describe how you would test mediation with available product data and what evidence would support mediation.
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