An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications

Handling customer complaints is a decision-making process that inherently involves a classification problem where each complaint should be classified exclusively to one of the complaint categories before a resolution is communicated to customers. Previous studies focus extensively on decision suppor...

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Bibliographic Details
Main Authors: Chen, Y.-W (Author), Xu, D.-L (Author), Yang, J.-B (Author), Yang, Y. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2018
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02797nam a2200397Ia 4500
001 10.1016-j.knosys.2018.09.029
008 220706s2018 CNT 000 0 und d
020 |a 09507051 (ISSN) 
245 1 0 |a An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications 
260 0 |b Elsevier B.V.  |c 2018 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.knosys.2018.09.029 
520 3 |a Handling customer complaints is a decision-making process that inherently involves a classification problem where each complaint should be classified exclusively to one of the complaint categories before a resolution is communicated to customers. Previous studies focus extensively on decision support systems (DSSs) to automate complaint handling, while few addresses the issue of classification imprecision when inaccurate or inconsistent information exists in customer complaint narratives. This research presents a novel DSS for handling customer complaints and develops an evidential reasoning (ER) rule-based classifier as the core component of the system to classify customer complaints with uncertain information. More specifically, textual and numeric features are firstly combined to generate evidence for formulating the relationship between customer complaint features and classification results. The ER rule is then applied to combine multiple pieces of evidence and classify customer complaints into different categories with probabilities. An empirical study is conducted in a telecommunication company. Results show that the proposed ER rule-based classification model provides high performance in comparison with other machine learning algorithms. The developed system offers telecommunication companies an informative and data-driven method for handling customer complaints in a systematic and automatic manner. © 2018 Elsevier B.V. 
650 0 4 |a Classification 
650 0 4 |a Classification (of information) 
650 0 4 |a Customer complaint handling 
650 0 4 |a Customer complaints 
650 0 4 |a Decision making 
650 0 4 |a Decision making process 
650 0 4 |a Decision support system 
650 0 4 |a Decision support system (DSSs) 
650 0 4 |a Decision support systems 
650 0 4 |a Evidential reasoning 
650 0 4 |a Evidential reasoning rule 
650 0 4 |a Inconsistent information 
650 0 4 |a Learning algorithms 
650 0 4 |a Machine learning 
650 0 4 |a Mobile telecommunications 
650 0 4 |a Rule-based classification 
650 0 4 |a Sales 
650 0 4 |a Telecommunication companies 
650 0 4 |a Telecommunication industry 
700 1 |a Chen, Y.-W.  |e author 
700 1 |a Xu, D.-L.  |e author 
700 1 |a Yang, J.-B.  |e author 
700 1 |a Yang, Y.  |e author 
773 |t Knowledge-Based Systems