I went straight to behavioral signals like session frequency dropping or push notification opt-outs, which felt right, but I didn't spend enough time on the modeling side or how you'd actually act on the prediction once you had it.
Start by framing the problem as a binary classification task: predict whether a user will uninstall the app within a defined time window. Then outline a data-driven approach using behavioral signals, feature engineering, and a predictive model, and finally discuss how to validate and operationalize the model to drive retention actions.
Pro tip: Emphasize that prediction is only valuable if it leads to timely, personalized interventions—so tie your model to specific retention levers and measure incremental impact via holdout groups.
Clarify what 'about to delete' means: predict uninstall within the next 7 or 14 days. Specify the unit of analysis (user) and the observation window for features.
List key features such as declining session frequency, reduced time spent, fewer core actions, negative sentiment in reviews, and support interactions. Consider temporal patterns and trends.
Choose a model like logistic regression or gradient boosting that outputs a probability. Handle class imbalance, validate with time-based splits, and evaluate using precision/recall or AUC.
Test the model on holdout data and run a pilot to measure business impact. Integrate predictions into a real-time system that triggers retention campaigns (e.g., push notifications, offers).
Continuously track model performance and business metrics (e.g., churn reduction). Retrain regularly and A/B test interventions to ensure effectiveness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.