Evaluation of Call Center Efficiency Using Text Mining Approach
In many business fields text mining methodologies are applied to analyze market, products, trends, quality, etc. Today, customer call center data are very valuable to understand customer needs and complaints, increase effectiveness and efficiency in technical services and customer loyalty, improve product quality and brand images. This study presents an application of text mining methods for customer call centers in a home appliance company. The dataset is provided by a home appliance company and includes 35 different country call center data. Random Forest and CART algorithms are applied to the recorded text which customers say directly to agents. According to the results, products’ error causing parts are determined with a range of 49%-59% accuracy rate for different countries. The most used words prepared as a table and presented as a recommendation for a home appliance companies. As a result, this study identifies root cause of problems of call centers and how agents can deal with them to provide better services.
Editor: H. Kemal İlter, Ankara Yıldırım Beyazıt University, Turkey
Received: August 19, 2018, Accepted: October 18, 2018, Published: November 10, 2018
Copyright: © 2018 IMISC Üzüm et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.