Predicting customer churn in mobile industry using data mining technology

TLDR

Churn prediction studies typically rely on subscriber data, but this work diverges by exploring word exposure effects. 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. Data mining methods such as logistic regression, decision trees, neural networks, and partial least squares were employed to predict churn from word exposure, with decision trees and PLS used to identify words with positive or negative influence. Prediction rates were comparable to subscriber data‑based analyses, and the identified influential words suggest opportunities for churn prediction, advertising impact, and psychological research, offering concrete ideas to enhance corporate competitiveness and industry efficiency.

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.