Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.