Customer Churn Risk Scoring using Machine Learning

Published by Drew Clancy on

Mosaic utilized different machine learning approaches to help this retail energy company combat customer churn.

This case study builds off segmentation work Mosaic performed for the same customer.

The Case for Identifying Customer Churn & Promoting Customer Retention 

Retaining customers is a must for a company’s bottom line. A company’s customers are its greatest asset, impacting business now, and becoming more valuable over time as they continue to invest in products and services. Customer churn can be costly, or even devastating, to growing and established organizations alike. The true cost of churn is often higher than business leaders generally estimate. Not only does it lead to lost revenue in the near term, but it also means your team must double down on acquiring new customers to fill those revenue streams to ensure continued success in the future. It is widely accepted that it can cost up to 5 times as much to acquire a new customer as it does to retain a current customer.  

Mosaic’s client, a leader in the propane industry, had seen a sharp rise in customer attrition. Recognizing the implications to their business in having to win new customers at a steep cost, they wanted to prevent further customer loss by learning why customers were terminating service. They turned to Mosaic, a trusted partner who had helped them identify regions to successfully target new customers using unsupervised learning techniques. Based on our prior work on this customer segmentation project, Mosaic was tasked with proving the value of applying machine learning to combat customer churn.

Churn Prediction

Mosaic leveraged historical data used in a previous project and used real examples of customers deciding to leave to learn the attributes and behavior that typically precede customer turnover. Mosaic’s data scientists interviewed subject matter experts to incorporate their expertise in developing a working ML model. 

Data-driven organizations like the propane firm typically use customer segmentation as a foundation for other value-added analytics. Customer churn is a natural next step since it leverages the knowledge and data of the customer segmentation project. Churn prediction enables targeted marketing and direct intervention for customers most likely to leave, streamlining use of the marketing budget. 

Mosaic’s ML Approach

customer churn modeling approach

Feature Engineering 

For any ML approach to be effective, substantial effort must be made to en