Survival Analysis vs. Logistic Regression (2024)

Marketers and businesses in general are concerned with customer churn because this is the point at which a customer stops purchasing, cancels a subscription, or in some other way ends their relationship with the business. On the other hand, retention refers to keeping clients active, whether that is making monthly subscription payments, engaging in content, or updating their information regularly.

In an article by Eryk Lewinson survival analysis was introduced as a method for analyzing customer churn/retention. However, when predicting customer churn, oftentimes analysts and data scientists will use classification models, such as logistic regression.

In this post, we’re going to review the difference between these two approaches and when one might be more appropriate than the other. To start, let’s define the terms of interest:

Survival Analysis: According to an article in Science Direct, survival analysis focuses on analyzing time-to-event data, meaning that the goal is to describe the length of time between a start point (origin) and an endpoint.

Logistic Regression: Science Direct also published an article about logistic regression, explaining that this statistical method finds an equation…