Predict User Churn with Effective Modeling Techniques

Q: Predict User Churn with Effective Modeling Techniques

This question evaluates skills in predictive modeling for churn, temporal feature engineering and data-leakage awareness, handling class imbalance, and selecting evaluation metrics for subscription retention scenarios in the Machine Learning domain.

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Question

Predicting User Churn for a Subscription App

Context

You are building a model to predict which active subscribers are likely to churn soon so the team can target retention offers. Assume:

Label: churn in the next 30 days (cancel subscription or no activity for 30 days).
Features: recent engagement (recency, frequency, session duration), tenure, plan type, payment history, support interactions, device/geo, marketing touches.
Data is time-ordered; avoid leakage by using only information available before the prediction date.

Tasks

(a) Which modeling techniques would you consider and why?

(b) How would you address class imbalance?

(c) What evaluation metrics would you use, and how would you choose thresholds for action?

Predict User Churn with Effective Modeling Techniques

Quick Overview

Predicting User Churn for a Subscription App

Context

Tasks

Solution

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