Predicting customer churn in mobile industry using data mining technology

Abstract

Purpose

The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data.

Design/methodology/approach

This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model.

Findings

This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences.

Originality/value

These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

Citation

Lee, E.-B., Kim, J. and Lee, S.-G. (2017), "Predicting customer churn in mobile industry using data mining technology", Industrial Management & Data Systems, Vol. 117 No. 1, pp. 90-109. https://doi.org/10.1108/IMDS-12-2015-0509