Developing and Deploying a Churn Prediction Model with Azure Machine Learning Services - CSE Developer Blog

For a subscription service business, there are two ways to drive growth: grow the number of new customers, or increase the lifetime value from the customers that you already have by retaining more of them. Improving customer retention requires the ability to predict which subscribers are likely to cancel (referred to as churn), and to intervene with the right retention offers at the right time. Recently, the use of deep learning algorithms that learn sequential product usage customer behavior to make predictions have begun to offer businesses a more powerful method to pinpoint accounts at risk. This understanding of an account's churn likelihood allows a company to proactively act to save the most valuable customers before they cancel. CSE recently partnered with the finance group of Majid Al Futtaim Ventures (MAF), a leading mall, communities, retail and leisure pioneer across the Middle East, Africa and Asia, to design and deploy a machine learning solution to predict attrition within their consumer credit card customer base. MAF sought to use their customer records – including transaction and incident history plus account profile information – to inform a predictive model.