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User Retention and Engagement Questions

Comprehensive coverage of strategies and tactics used to retain and reengage users or customers, deepen engagement, and build healthy communities that drive long term value. Topics include diagnosing the root causes of churn through cohort analysis and retention curve analysis, defining and tracking core metrics such as churn rate, retention rate at key intervals, reactivation rate, cohort lifetime value, and engagement metrics including daily active users and monthly active users. Candidates should be able to identify at risk segments using behavioral segmentation and propensity modeling, prioritize levers, and design targeted reengagement and lifecycle campaigns such as email sequences, win back offers, incentives for lapsed users, referral and loyalty programs, content recommendation, and personalized messaging and notifications. Product levers include onboarding and activation flow optimizations, habit forming engagement loops, recommendation systems, and community activation programs including events, moderation, governance, and community health monitoring. Candidates should also demonstrate experiment design and iterative A B testing, proper instrumentation and analytics, cross functional collaboration with engineering, design, and marketing, and the ability to measure and interpret both short term campaign metrics such as open and click rates and longer term outcomes such as retention curves and changes in lifetime value. Interviewers may probe segmentation and personalization strategies, prioritization frameworks, trade offs between acquisition and retention, and examples of optimizations and their measurable impact.

HardTechnical
0 practiced
Describe how to build and deploy a churn propensity model that uses time-varying covariates (rolling 7-day counts, last purchase recency). Discuss model choices (Cox proportional hazards, survival forests, RNNs), training and validation using time-sliced data, feature pipeline and feature store needs, online scoring architecture, and monitoring for calibration drift.
MediumSystem Design
0 practiced
Design a retention and engagement dashboard for product managers. List the essential charts/widgets (cohort heatmap, retention curve, funnel conversion, DAU/MAU trend, LTV curve), the filters needed (country, acquisition channel, signup cohort, platform), recommended data refresh cadence, and performance considerations (materialized views, pre-aggregations).
HardSystem Design
0 practiced
Design an experiment to measure long-term LTV uplift from a recommendation ranking change. Specify randomization unit, holdout duration, sample size considerations given heavy-tailed revenue, required instrumentation (impressions, clicks, orders, revenue), and the analysis approach you would take to estimate incremental LTV.
MediumTechnical
0 practiced
Explain how overlapping experiments can introduce contamination in retention analyses. Provide specific mitigation strategies (e.g., consistent bucketing, exclusion rules, hierarchical testing) and describe how you'd detect contamination after the fact and adjust the analysis.
MediumTechnical
0 practiced
You receive an events table with duplicate rows, partial user IDs, and multiple device IDs per user. Provide a step-by-step SQL or Python approach to deduplicate events, map multiple devices to canonical users, and build a canonical users table suitable for retention analysis. Explain assumptions and pitfalls (e.g., identity resolution, cross-device linking).

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