Take the blue pill: Churn Prediction with Survival Analysis
An easier alternative compared to machine learning
Imagine you’re standing in front of two doors. Behind the blue door lies a path that’s straightforward and easy to follow. Behind the red door, however, lies a path that’s difficult and complex, requiring a deeper understanding of the underlying principles.
This is the decision that many telecommunication companies face when it comes to churn prediction: should they take the blue pill and opt for the easier route of survival analysis, or take the red pill and dive into the complex world of machine learning?
While both methods can be effective, they have their own unique advantages and challenges.
In this blog, we will take the blue pill and dive into the easier, but more efficient approach
Survival analysis overview
Survival analysis is a statistical method used to analyze time-to-event data, where the event of interest is a specific outcome that may occur over time, such as death, disease onset, or failure of a mechanical device. The primary goal of survival analysis is to estimate the probability of the event occurring at a given time and to identify factors that may influence the time to event.
