Customer Retention and Lifetime Value Optimization Questions
Show strategic thinking about customer retention, expansion, and lifetime value. Discuss how you'd analyze retention challenges, design retention strategies, optimize customer success operations, and coordinate post-sale processes. Demonstrate understanding of financial impact of retention improvements.
HardTechnical
0 practiced
Design a multi-armed bandit system for determining the best retention offer (e.g., discount percentage, free month, loyalty points) per user segment, where reward (retention) is delayed and sparse. Describe choice of bandit algorithm (contextual Thompson Sampling vs UCB), handling delayed rewards, non-stationarity, safety constraints (minimize revenue loss), and offline evaluation via counterfactual policy evaluation.
MediumTechnical
0 practiced
Compare two approaches to predict CLTV: (A) probabilistic models (e.g., BG/NBD + Gamma-Gamma) and (B) supervised machine learning regression models that predict future revenue. For a transactional business with repeat purchases, explain pros/cons, data requirements, calibration/validation strategies, and when you'd choose one over the other.
EasyTechnical
0 practiced
As you build churn prediction models, reviewers often debate which evaluation metric to use for an imbalanced dataset where churn is only 5% of users. Explain the differences between accuracy, precision, recall, F1, ROC-AUC and PR-AUC in this context. Which metric(s) would you prioritize for operationalizing churn prevention campaigns and why? Include discussion of calibration and business cost trade-offs.
MediumTechnical
0 practiced
You're running a retention promotion (discounted month for targeted customers). Describe how you would build and evaluate an uplift model to target customers who would be retained only if offered (positive uplift). Discuss approaches (two-model, uplift trees, meta-learners), data requirements (treatment and control), evaluation metrics (Qini curve, uplift AUC), and practical pitfalls like sample size and heterogeneous treatment effects.
HardTechnical
0 practiced
Propose and justify a hybrid recommendation system to improve retention for new users with little interaction (cold-start) on an e-commerce platform. Combine content-based, collaborative-filtering, and popularity-based components; describe feature representations, cold-start heuristics, online learning considerations, and offline evaluation metrics that correlate with retention rather than short-term clicks.
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