Just-in-time Customer Churn Prediction: With and Without Data Transformation

Adnan Amin, Babar Shah, Asad Masood Khattak, Thar Baker, Hamood Ur Rahman Durani, Sajid Anwar

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060177
Publication statusPublished - 28 Sept 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings


Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CityRio de Janeiro

Bibliographical note

Funding Information:
This research was supported by the Cluster Research Project Activity code # R18027, Zayed University, Abu Dhabi, United Arab Emirates.

Publisher Copyright:
© 2018 IEEE.


  • cross-company
  • customer churn prediction
  • data filter
  • just-in-time
  • log transformation
  • rank transformation


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