This post introduces the new advanced customer churn simulation for learning the techniques of Fighting Churn with data (FCWD). The book was originally released with a simple simulation. The simple simulation served a purpose, but was lacking: Some of the code from Fighting Churn with Data either would not run or produced meaningless results on the simple simulation. But the recently advanced churn simulation solves this. Now, all of the code in Fighting Churn With…
In my previous post on Machine Learning for Churn prediction, I showed that Machine Learning models (ML also known as AI) are the most accurate. But in the past, machine learning models suffered from being hard to explain. In my book, Fighting Churn With Data, I wrote that you should use Logistic Regression to explain churn. But I also showed that the machine learning model XGBoost gave higher accuracy. Now I recently learned about a…
This article discusses using machine learning algorithms to forecast churn risks. Continued from Part 1, in which the XGBoost machine learning model and how it can be used to predict churn is presented. This post demonstrates the code and some results for real data. Below, chapter 9 listing 6 from the book (Fighting Churn With Data) shows XGBoost cross-validation for regression in python. (All the code from Fighting Churn With Data can be found at…
This post discusses forecasting churn risks using machine learning algorithms. In this article, I’m going to introduce the basic ideas of machine learning (ML) and a particular algorithm called XGBoost. If you are already familiar with these topics then skip to my next post which dives into the details of using these methods to forecast churn. Forecasting churn risk with machine learning You can forecast churn with a regression in which predictions are made by…
Calculating churn probability is an important part of fighting churn because of three key use cases: Evaluating which behaviors are most important for engagement Calculating customer lifetime value Segmenting customers for high cost interventions In this post I give an introduction to logistic regression: Logistic Regression is the most common and versatile way to calculate the churn probabilities. I assume you have calculated metrics and created a data set, following the instructions in my previous…