Predicting Customer Churn at Qwe Inc

Abstract

This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood. Excerpt UVA-QA-0806 Rev. Apr. 2, 2014 PREDICTING CUSTOMER CHURN AT QWE INC. QWE Inc. helped small- and medium-size businesses manage their online presence through a subscription service. As with many successful dot-com start-ups, QWE experienced fast growth initially but, as the company and its business model matured, management realized the need for deeper analytical insight into some key business processes, one of which was customer retention. QWE customer contracts ran either month to month or for a fixed period of 6 or 12 months. Thus, depending on the type of contract, a customer was free to leave either at the end of each month or at the end of Month 6 or Month 12 of its relationship with QWE. Up until 2012, QWE took a reactive approach to churn, or attrition: if a customer called with a request to cancel his or her contract, the QWE customer service representative would try to convince the customer to extend the contract, most often by offering free services or discounts on existing services. In February of that year, Richard Wall, VP of customer services at QWE, wondered if his team could develop a more proactive approach. . . .