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Survival Analysis: Predict Time-To-Event With Machine Learning (Part I)
Practical Application to Customer Churn Prediction
Predicting the probability of an event occurring is good, predicting the time remaining before an event occurs is even better!
Take the example of customer churn. What if, instead of predicting the probability of a customer leaving the company in the next months, you could predict this probability at several time points over the next months? The benefits of such an approach are immediate. It would allow you to anticipate and prioritize your marketing actions more effectively in time and, ultimately, reduce the churn rate.
This falls exactly in the field of survival analysis, also called time-to-event analysis. It refers to a learning framework and a set of techniques that can be used to estimate the time it takes for an event of interest to occur based on observations.
The name of survival analysis comes from the typical use case where it was first applied: predicting time to death for clinical research. However, one should not be misled by its name: it is not limited to the medical field, but can be applied to use cases in multiple industries. And with the recent advances in data science, survival analysis has reemerged leaving the world of classical statistics to…
