Retention analysis based on a logistic regression model: A case study

Mohammadhossein Ghahramani, Mengchu Zhou, Chi Tin Hon, Gang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Telecommunication data has provided new opportunities for both businesses and academia to analyze subscribers' behavioral patterns. Recently, there have been many changes in this industry, i.e., lessening of market regulations/restrictions in exchange for greater participation. New services, emerging technologies, and competitive offerings are factors causing customers to move to different companies. In this work, we intend to develop a logistic regression model tailored for a telecommunication company in Macau by forecasting potential subscribers intending to leave their current services. To implement such prediction we should assign a probability value to subscribers, based on a relationship between customers' historical data and their future behavioral pattern. Then customers with the highest propensity to leave can receive various marketing offers. To improve the analysis result we have utilized a combination of two datasets. Our experimental results show how such data aggregation can improve the model accuracy.

Original languageEnglish (US)
Title of host publicationICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538650530
DOIs
StatePublished - May 18 2018
Event15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018 - Zhuhai, China
Duration: Mar 27 2018Mar 29 2018

Publication series

NameICNSC 2018 - 15th IEEE International Conference on Networking, Sensing and Control

Other

Other15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
CountryChina
CityZhuhai
Period3/27/183/29/18

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Optimization
  • Modeling and Simulation

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