The churning of a customer in a company is a situation where a customer stops using their service or cancels their subscription. Primarily, studies on customer churn started from Customer Relation Management (CRM). Mozer, Michael C., et al. (2000) have proposed that, in terms of the net return on investment, marketing campaigns for retaining existing customers are more efficient than putting efforts to attract new customers[12]. Reichheld et al. (1996) have shown that a 5 percent increase in customer retention rate achieved 35 percent and 95 percent increases in the net present value of customers for a software company and an advertising agency, respectively [13]. As such, churn prediction can be used as a method to increase the retention rate of loyal customers and ultimately increase the value of the company.

The majority of the early studies on churn were conducted from a management perspective, especially CRM (Customer Relation Management) [14], [15]. The telecommunications industry accounts for the majority of previous studies on churn. The financial and insurance industries also predict customer churn. Zhang, Rong, et al. (2017) stressed the need to build churn prediction models and prevent churn, referring to high customer acquisition costs and high customer values in the insurance industry [16]. Moreover,  studies on churn have been actively conducted in the gaming field as in the telecommunication field. These services have a fast cycle of customer inflow and churn because of mass competition. If a single service is run for a long time, the service competition intensifies and the Customer Acquisition Cost (CAC) tends to increase. As the CAC gets larger, the technology to predict and prevent churn becomes more crucial. Milosevic, Milos, Nenad Živic, and Igor Andjelkovic. (2017) created a model predicting churn in the study on game churn, gave churn prevention incentives by finding out and dividing probable churn customers into A/B groups, and demonstrated actual effects statistically [17]. Additionally, research on churn was also conducted in the Internet service and newspaper subscription fields.

Churn prediction in today’s competitive world is an important issue for most of the company. Customer churn is even associated with the existing cycle of a business. So,  a company like Worldlink needs to understand their customers’ needs and requirements before they churn and also their probable reasons for churn.

The churning of customers who are using the services of a company negatively impacts the company’s growth and profitability. So, the company needs to work on minimizing the churn rate. Especially for a subscription-based company like Worldlink, it is equally crucial to attract new customers and retain old customers who are their Revenue Generating Units(RGUs). Traditionally, Telecom and Internet service companies keep an account of RGU data and they try to increase RGU flows. Implementation of modern technology in solving such industry problems has shown to be promising. In this project, academia is responsible to predict possible churn-risk customers and provide reasoning to the behavior of their churning.

A machine learning model is applied to predict the churn. The pipeline used for the Worldlink project consists of seven steps.

Industry-academia collaboration is a newly chartered field in Nepal. ACEM being first among others to step in this has boosted students’ morale in light of hands-on industry experience. The Worldlink Customer Churn project has acted as a milestone in achieving the industry-academia ecosystem. Due to a properly laid out framework, the industry-academia project has been able to run smoothly. Moreover, to accomplish the goal of the industry projects, assistance from field specialists has also been provided by RIU; enhancing the experience of students on industry-academia collaboration.