Build Model to Predict Customer Contract Renewal

Q: Build Model to Predict Customer Contract Renewal

This question evaluates a data scientist's competency in end-to-end supervised machine learning for enterprise contract renewal prediction, covering problem framing, target definition and sampling windows, feature engineering, model choice, and model evaluation.

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Question

Predicting Enterprise Customer Renewal for Google Meet

You are tasked with designing a model to predict whether an enterprise customer will renew their Google Meet contract.

Requirements

End-to-end approach
- Define the sampling timeline (observation/feature window, blackout period, prediction window).
- Precisely define the target (what counts as a “renewal” vs. “churn/downgrade”).
Feature engineering
- Which features would you create and why? Consider product usage, account context, pricing/contract terms, and customer experience.
Model choice
- When is logistic regression sufficient?
- When would you prefer more complex models (e.g., Gradient Boosted Machines, Neural Networks)?
Evaluation and comparison
- How would you evaluate and compare models (AUC, calibration, business lift, feature importance, interpretability vs. performance trade-offs)?

Hints: Cover sampling window, target definition, feature importance, calibration, AUC, business lift, and interpretability vs. performance trade-off.

Build Model to Predict Customer Contract Renewal

Predicting Enterprise Customer Renewal for Google Meet

Requirements

Solution

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