aws-samples/churn-prediction-with-text-and-interpretability
Predict customer churn with text and interpretability.
This project helps customer retention specialists and marketing managers identify which customers are at high risk of leaving before they actually churn. By analyzing a combination of customer support chat logs and structured data like purchase history, it predicts customer churn and explains the key reasons behind it. The outcome is a list of at-risk customers and insights into why they might leave, enabling targeted retention efforts.
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Use this if you need to proactively identify customers likely to churn and understand the underlying reasons, combining both what customers say and their other behaviors.
Not ideal if you only have simple numerical data and don't have access to customer conversation logs or similar text data.
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Jupyter Notebook
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MIT-0
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Last pushed
Sep 20, 2021
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