Time to event (Churn prediction)

Hello guys,

I am struggeling to built a workflow that predicts the time when someone will churn. I have a data base that consits of different Inputs Variables such as Age, time as a customer, amout of purchase and churn (yes or no) etc. Is it possible to predict the time of churn based on those characteristics if I have historical data for this (like a decision tree just with the inforomation when someone will churn not just if ? I tried to use the Kaplan Estimator for this but I thinkt this will not take my other input variables into consideration…

Thanks in advance guys

If it is not a classification problem (customer will churn / will not churn) then you can treat it as a regression problem.
So with your input features and target (“churn time” for churned customers) you should be able to use any regression algorithm you want e.g. Random Forest Regressor
br

Hi,

If you are familiar with Python, look at ecommerce tool:

Or this one, even better if you want to forecast churn or next action:
https://lifetimes.readthedocs.io/en/latest/Quickstart.html

I don’t if it works witlin the python node of knime. Try it