Model Selection for SaaS Churn Prediction Using Machine Learning

This is a post in a series about churn and customer satisfaction. If you want churn prediction and management without more work, checkout Keepify. If you want more details, email away.

Recently I have been developing machine-learning systems that will predict SaaS churn. Churn prediction has many desirable business benefits and applications, but here I will focus on the technical details of selecting a durable model for predicting churn and some of the lessons I’ve learned along the way.


Most learning problems should be attacked initially with a linear model. I tried two versions of a linear approach in the early days. The first was an attempt to predict the number of months a user would stay using linear regression. This was a terrible failure. It was essentially 90% wrong. The root mean square error was absurdly high. I think this was the wrong approach with the wrong data, but it was a fun initial experiment to get some momentum. The last was an attempt to classify users as churners or non-churners using logistic regression. I’ll address that one more in the next few sections.

Literature Review

After my initial failure, I decided to fire up Google Scholar like my old days in graduate school and try to find some meaningful research on a similar subject. It turns out that a lot of subscription-based services like cable, Internet, and periodical publications fund both academic and industry research in churn prediction. There isn’t any apparent research on SaaS specifically, but the foundations of predicting churn for a newspaper subscription should be similar. In fact, I thought that SaaS should have far superior data to use in prediction.

The research says that the most successful models are Logistic Regression and Random Forests. Many people have shown the efficacy of Support Vector Machines to fall in between these popular options. Neural networks are another popular option with varied, but solid results [1]. My later experiments tried to use some of this insight and focus on models that had the most promise.


I decided to use Weka to try a lot of different experiments quickly on the same data set. I was careful about separating my data into strict train and test segments, but I was happy use various datasets to experiment with different learning hypotheses. Weka performed beautifully for me and came with an additional benefit, the JVM. I was processing some of the data transformation in Ruby and I wanted to integrate this system into a Rails application. JRuby made working with Weka and Rails incredibly easy.

It was easy to transform my existing data to ARFF file formats for Weka and I managed to test out nearly all of the relevant classifiers that Weka supports. I have not used SVM or Neural Networks for reasons I explain in the next section. Bayesian Nets and AdaBoost show promise as classifiers for churn prediction in my experiments, but they don’t show up much in the literature.

Classifier Comparisons and Selection

Random Forests dominate the research landscape as the model of choice and my experiments bear that out. Random Forests win. A lot. The intuition to explain why is two-fold. Random Forests are extremely robust without performing feature selection. They do their own version of feature selection that works well for this problem. Random Forests are based on decision trees that classify data pretty well across a small number of known classes. They’re especially effective when certain feature values correlate highly with certain classes. Decision trees (and Logistic Regression) share a final benefit. They show how the classifier works internally in an understandable way. If your customers churn when they use feature X only once per month then you can see that in how the decision tree is structured. This is powerful insight.

Logistic Regression works really well, if not quite as well as Random Forests. It not only presents a model that explains how it works, but it does so with more emphasis on how sure it is whether a customer falls into one class or another.

I didn’t use Neural Networks in any experiments in large part because it isn’t something I could do out of the box with my data in Weka and it famously does not lend any insight into how the classification works. Neural Nets are a black box. Ideally, my classification engine for Keepify will be able to provide more insight for customers than classification alone.

Support Vector Machines are a very cool combination of linear classifiers that optimize a hyperplane. They are a sexy choice, but the performance is not quite so nice as Random Forests, they don’t show their work like Neural Nets, and they are really slow. I can generate predictions for thousands of customers with hundreds of features using a Random Forest in less than a few seconds. SVM might take minutes or worse.

In the end, I decided to use Random Forests and Logistic Regression. I do plan to experiment further with AdaBoost, however, as it is effective at eliminating bias from data sets that have classes with low prevalence.


2 thoughts on “Model Selection for SaaS Churn Prediction Using Machine Learning”

  1. I have a dataset that has records of churned customers for every consecutive month upto 10 months. I am planning to label them as churn and non-churn labels and then use the classification labels. Can you please tell me if I can use data of 1 month as churned label and the rest as non-churned label and then build a classification model based on this. Then I will try to classify the data for 1 month if the user churns or not. I will then take data for 2nd month and then treat the rest as non-churn data. I want to expand till last 9th month. I need to find the probability of users churned every month. I want to use logistic regression, Bayesian for this. I want your input on this. Do you think this is the correct strategy.

    1. I’d be happy to discuss it. Why don’t you send me an email with a little more information about how you plan to setup the model and organize the data? Check the contact page for an email address.

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