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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.

EasyTechnical
0 practiced
When running A/B tests focused on retention, how do you choose the primary metric and how do you determine test duration? Discuss trade-offs between short-term signals (e.g., 7-day retention) and long-term outcomes (e.g., 90-day retention), and list necessary guardrail metrics.
MediumTechnical
0 practiced
Describe a practical process to segment users by behavioral signals to find at-risk groups for targeted re-engagement campaigns. Include feature ideas (recency, frequency, session cadence), methods (rule-based, k-means, hierarchical), segment sizing best practices, and how to validate that segments are actionable.
HardTechnical
0 practiced
Discuss trade-offs between recommending popular items (popularity-based) vs personalized recommendations. Explain expected short-term engagement and long-term retention impacts, how to measure diversity and novelty, and propose an A/B testing plan to compare strategies including metrics and evaluation timeframe.
EasyTechnical
0 practiced
Given tables users(user_id, signup_date) and events(user_id, event_date), write a SQL query to compute 30-day retention: percentage of users who had at least one event within 30 days after signup. Explain assumptions about timezones, duplicate signups, and how you treat users with no events.
HardTechnical
0 practiced
When running many retention experiments and subgroup analyses, describe methods to control false discoveries: Bonferroni correction, Benjamini–Hochberg FDR control, hierarchical testing, and sequential testing. Explain trade-offs between Type I and Type II errors and how to integrate these corrections into an experimentation pipeline.

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