DEEP LEARNING-BASED CUSTOMER COMPLAINT MANAGEMENT

ABSTRACT

In recent years, managing customer complaints poses a problem for companies due to the increasing market and customer base. One of the most effective ways to speed up the handling of complaints is to categorize customer issues and automatically forward complaints to relevant officers or departments. This reduces the response time to complaints and ensures that specific complaints are being handled by the people with the right expertise. Also, the companies can create a strategy exclusively for certain types of problems, which will hasten the problem resolution. In this article, we propose an intelligent customer complaint management system (CCMS) for financial services organizations. We described a pre-processing technique for Turkish agglutinative language using deep learning algorithms and it was not previously considered in the literature. Furthermore, the performance of the algorithm has been significantly increased by choosing the appropriate combinations of pre-processing tasks. The proposed method not only greatly increases text classification’s utility for a broader range of customer complaints, but it also yields improved overall performance, recorded with a 96% accuracy score. The findings of the experiments show that the proposed approach is more effective than the other state-of-the-art strategies.

KEYWORDS:

• Customer complaint
• text classification
• complaint management
• artificial intelligence
• deep learning
• agglutinative language
• Turkish text classification

Acknowledgments

This study did not receive any funding support from the public or private sectors.

Disclosure statement

The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this study.

Additional information

Notes on contributors

Yildiray Anagun

Yildiray Anagun was born in 1980. He received the B.Sc. degree in Computer Engineering Department from Çankaya University, Turkey, in 2004, the M.Sc. degree in Electrical and Electronics Engineering from Kutahya Dumlupınar University, Turkey, in 2007 and the Ph.D. degree in Electrical and Electronics Engineering from Eskisehir Osmangazi University, Turkey in 2018. Currently, he is working as Assistant Professor Doctor at the Computer Engineering Department, Eskisehir Osmangazi University, Turkey. His research interests are in signal and image processing, machine learning, and deep learning.

Nur Sultan Bolel

Nur Sultan Bolel was born in Kırıkkale, Turkey. She received the B.Sc. in Computer Engineering from Eskisehir Osmangazi University, Turkey in 2020. Currently, she is a Software Development Specialist at İnnova Bilişim Çözümleri A.Ş, Turkey. Her research interests are micro services, secure coding, and deep learning.

Sahin Isik

Sahin Isik received B.Sc. M.Sc. and Ph.D. degrees in Computer Engineering from Anadolu University, in 2012, 2014 and 2018, respectively. He is presently an Assistant Professor Doctor at the Computer Engineering Department, Eskisehir Osmangazi University. His research interest concerned with applications of image processing and pattern recognition. Currently, he focuses on the intersections of deep learning methods and real time image or video processing.

Serif Ercan Ozkan

Serif Ercan Ozkan was born in Kütahya, Turkey. He received the B.Sc. in Computer Engineering from Eskisehir Osmangazi University, Turkey in 2020. Currently, he is a Software Development Specialist at Turkcell, Turkey. His research interests are micro services, image processing, and deep learning.