I started with behavioral segments like engagement frequency and feature usage, then layered in tenure and plan type.
Start by clarifying the business context and defining churn precisely, then outline a segmentation strategy that combines behavioral, demographic, and engagement dimensions. Emphasize that segmentation should be actionable, tied to hypotheses about why users churn, and validated with data.
Pro tip: Show that you think beyond static segments by proposing dynamic, time-based cohorts and survival analysis to capture churn risk over the user lifecycle. This demonstrates a deeper, more nuanced understanding of churn analytics.
Clarify what constitutes churn for the product (e.g., inactivity, cancellation) and the business goal (e.g., reduce churn, increase retention). Align with stakeholders on the definition and success metrics.
Choose relevant dimensions such as demographics, behavior (usage frequency, feature adoption), engagement (recency, frequency, monetary), and lifecycle stage. Prioritize dimensions likely to influence churn.
Decide between simple rule-based segmentation (e.g., RFM) and advanced techniques (e.g., clustering, decision trees). Consider data availability, interpretability, and actionability.
Compute churn rates per segment and compare to baseline. Use statistical tests or survival analysis to identify significant differences and trends over time.
Validate findings with holdout data or A/B tests. Translate insights into targeted retention strategies and monitor segment-level churn continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.