Retention Analysis AI Prompts

Identify the leading behavioral indicators that predict user churn before it happens. User behavior data: {{behavior_data}} Churn definition: {{churn_definition}} (e.g. no activity for 30 days, subscription cancelled) Observation window: {{observation_window}} (behavioral features measured in the N days before churn) 1. Feature engineering for churn prediction: Compute these behavioral features for each user in the observation window: - Login frequency: sessions per week - Days since last active - Core action completion rate: % of sessions where {{core_action}} was completed - Feature breadth: number of distinct features used - Engagement trend: comparing last 7 days vs prior 7 days - Support contacts: number of support tickets or error events - Billing events: failed payments, plan downgrades 2. Univariate analysis: For each feature, compare the distribution between: - Users who churned within {{horizon}} days - Users who did not churn Compute: mean, median, and statistical significance of the difference (Mann-Whitney U test) 3. Predictive ranking: - Which features show the largest and most statistically significant difference between churners and non-churners? - Rank features by predictive power (use AUC of a simple logistic regression per feature) 4. Early warning thresholds: - For the top 3 features: what threshold value separates high-churn-risk from low-churn-risk users? - Example: users with > 14 days since last login have a 3x higher churn rate than average 5. Churn risk segmentation: - Combine the top 3 indicators into a simple churn risk score (Low / Medium / High) - What % of users currently fall into each risk tier? - What intervention should each tier receive? Return: feature importance table, threshold analysis, risk tier definitions, and intervention recommendations.

Build and interpret a user retention cohort analysis. Event data: {{event_data}} (user_id, event_date, acquisition_date or cohort_date) Retention definition: {{retention_definition}} (e.g. any login, completed core action, purchase) Cohort granularity: {{granularity}} (weekly / monthly) 1. Build the retention matrix: - Rows: cohorts defined by {{cohort_period}} of first use or acquisition - Columns: periods since acquisition (Period 0, 1, 2, ... N) - Cell value: % of cohort still active in that period - Period 0 = 100% by definition (the acquisition period) 2. Key retention metrics: - Day 1 retention: % of users returning the day after first use - Day 7 retention: % returning in the first week - Day 30 retention: % returning within the first month - Long-term retention: at what period does the retention curve flatten? This is the product's natural retention floor. 3. Cohort comparison: - Are newer cohorts retaining better or worse than older ones? - Which cohort has the best Day 30 retention? What was happening during that acquisition period? - Plot cohort curves on the same chart: diverging curves indicate improving or worsening product health 4. Retention curve shape interpretation: - Sharp early drop then flat: high initial churn but strong core user base - Gradual continuous decline: no engaged user base, product is not habit-forming - Bump at specific period: seasonal return or notification-driven re-engagement 5. Retention by acquisition channel: - Which acquisition channels produce the highest Day 30 retention? - Are there channels bringing volume but low retention? (Wasted acquisition spend) 6. Recommendations: - At which period does the biggest retention drop occur? What is the likely cause? - What single change would most improve the retention curve shape? Return: retention matrix, key metrics table, cohort comparison chart description, curve interpretation, and top recommendations.