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.
MediumTechnical
0 practiced
Explain the design of an AutoML pipeline for churn prediction: candidate model families (GBM, linear, NN), feature transformations, search space, time-series-aware cross-validation, and guardrails to prevent leakage (temporal validation, forbidden features). Describe how to integrate human-in-the-loop for feature curation and how you'd detect and prevent AutoML overfitting to spurious signals.
MediumTechnical
0 practiced
Write a Python FastAPI microservice skeleton that loads a serialized churn model and serves online predictions. Requirements: input validation (JSON), authentication header check, support batch scoring up to 100 users per request, proper error handling and structured logging, and a health-check endpoint. Provide pseudo-code for model loading, input schema, and a sample JSON request/response.
EasyTechnical
0 practiced
List at least 12 candidate features you would engineer from transactional and usage logs to predict customer churn for a subscription product. For each feature, include (a) brief rationale, (b) expected predictive direction (higher/lower churn), and (c) a potential pitfall (e.g., leakage, sparsity). Cover behavioral, financial, support, and engagement signals.
MediumTechnical
0 practiced
In Python, implement a reproducible pipeline that ingests daily cohort churn rates and produces a 30-day forecast with 95% prediction intervals. Use either statsmodels ARIMA or Prophet (specify which), include aggregation steps, handle missing dates, model selection (p/d/q or Prophet params), time-series cross-validation (expanding window), and output a CSV-like table with date, predicted_rate, lower_95, upper_95.
EasyTechnical
0 practiced
Write a SQL query (PostgreSQL) that produces a monthly retention cohort table for a subscription product. Given tables: subscriptions(user_id, start_date DATE, cancel_date DATE NULL) and events(user_id, event_date DATE, event_type VARCHAR), compute for each cohort month (signup month) the percentage of users active at months 1..12 after start. Include use of date_trunc, generate_series (1..12), and window functions, and explain assumptions about cancel_date and partial months.
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