Customer retention and churn prediction in the telecommunication industry: a case study on a Danish university

In this study, we explore the possible factors affecting churn in the Danish telecommunication industry and how those factors connect with retention strategies. The Danish telecommunication industry is experiencing a saturated market regarding the number of customers, but the number of service providers has increased significantly in recent years. Due to the high costs of acquiring new customers, the telecommunication industry put great emphasis on retaining customers in such an intensely competitive industry. We employ five machine learning algorithms: random forest, AdaBoost, logistic regression, extreme gradient boosting classifier, and decision tree classifier on four datasets from two geographical regions, Denmark and the USA. The first three datasets are from online repositories, and the last one contains responses from 311 students from Aalborg University collected through a survey. We identify key features extracted by the best-performing algorithms based on five performance measures. Based on that, we aggregate all the features that appear important for each dataset. The results demonstrate that customers’ preferences are not aligned. Among the prominent drivers, we find that service quality, customer satisfaction, offering subscription plan upgrades, and network coverage are unique to the Danish student population. Telecommunication companies need to integrate the sociohistoric milieu of the Nordic countries to tailor their retention policies to different consumer cultures. Article highlights Five machine learning algorithms were used on four datasets to extract the key factors reflecting the preferences of customers in two regions. Unique churn prediction and customer retention strategies are necessary for each region. Network coverage, customer satisfaction, service quality and subscription upgrades affect Danish students’ churn.