Build Model to Predict Customer Contract Renewal
Overview
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.
Google
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Machine Learning
81
0
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
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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”).
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Feature engineering
- Which features would you create and why? Consider product usage, account context, pricing/contract terms, and customer experience.
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Model choice
- When is logistic regression sufficient?
- When would you prefer more complex models (e.g., Gradient Boosted Machines, Neural Networks)?
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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.
Solution
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