Reducing Customer Attrition with Machine Learning
An international eCommerce organization had a customer attrition rate that exceeded the rate of acquisition – causing an overall negative growth rate. The team was tasked with increasing save rates by building a model that allowed targeted outreach to at risk customers.
A machine learning model was developed that assigned an attrition risk score to all the company’s customers. By identifying the customers with the greatest risk of leaving, the client was able to prioritize and design more effective marketing campaigns that dramatically reduced customer attrition and yielded positive growth for the first time in over a year.
The model incorporated customer demographics, prior purchases, and web analytics data. By pairing behavioral data with customer attributes, the model was able to learn the key behaviors customers exhibit leading up to a full exit from the company. While customer purchasing behaviors change with updated product lines and global market forces, the power of machine learning is that the model will automatically update and adapt to changing behavior over time.
Furthermore, a customer insights and attrition strategy dashboard was developed that allowed senior leadership to review the data and behavior of the highest risk customers. By continually taking a pulse on attrition, the client was able to improve key aspects of the company that improved customer engagement and decreased attrition.
Other Applicable Industries: Finance, Services, Suppliers, Media, Communication, Insurance
Skills: Value Mapping, Customer Segmentation, CLV, Attrition Modeling, Dashboarding