I went straight to cohort analysis and survival curves, which felt right, but I fumbled when they pushed on how I'd separate correlation from causation.
Start by defining retention precisely for the subscription context (e.g., renewal rate at 30/60/90 days) and segment users by cohort and acquisition channel. Then propose a mix of descriptive, diagnostic, and predictive analyses—such as survival analysis, cohort curves, and driver modeling—to identify which behaviors and product features correlate with long-term retention, and suggest experiments to validate causality.
Pro tip: Emphasize that correlation isn't causation: propose a follow-up A/B test or quasi-experimental design (e.g., propensity score matching) to confirm that the identified drivers actually cause retention, not just co-occur with it.
Clarify what 'long-term retention' means for this subscription (e.g., renewal at 6 or 12 months) and select a primary metric like renewal rate or churn probability. Also define guardrail metrics to ensure any intervention doesn't harm other areas.
Break users into cohorts by signup date, acquisition channel, plan type, and early behavior. Plot retention curves (e.g., Kaplan-Meier) to spot differences and identify when churn risk is highest.
Analyze which early actions (e.g., feature usage, content engagement, support interactions) correlate with long-term retention. Use statistical tests and regression models to quantify relationships while controlling for confounders.
Propose A/B tests or natural experiments to test whether increasing a promising driver (e.g., onboarding flow, feature adoption) causally improves retention. Define success metrics and sample size upfront.
Summarize which drivers are most impactful and feasible to influence, and suggest product changes or targeted interventions. Include a plan to monitor impact over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.