Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry

TL;DR: The important processes of developing MOD customer churn prediction models by data mining techniques are presented and the experimental results show that using association rules allows the DT and NN models to provide better prediction performances over a chosen validation dataset.

TL;DR: The study statistically evaluates the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry, and provides guidelines on how to apply social networks analytics for churn prediction in the telecommunications industry in an optimal way.

TL;DR: In this article, the authors identify factors that affect customer churn, the single most valuable of an organization's assets, to survive in the challenging environment of a global market, organizations must recognize and analyze customer attitudes.

TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.

TL;DR: The Loyalty Effect as mentioned in this paper shows that even a small improvement in customer retention can double profits in a company, and it is shown that loyalty can even increase profits in the long run.

TL;DR: This one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework helps you understand the principles of data warehousing and data mining systems and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible.

TL;DR: These experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier.