Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection
For ¥0.57 per day, unlimited downloads CREATE MEMBERSHIP Download

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

Predicting customer churn in mobile industry using data mining technology

Author:
Lee, Eui-Bang  Kim, Jinwha  Lee, Sang-Gun  


Journal:
INDUSTRIAL MANAGEMENT & DATA SYSTEMS


Issue Date:
2017


Abstract(summary):

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.


Page:
90---109


VIEW PDF

The preview is over

If you wish to continue, please create your membership or download this.

Create Membership

Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads