Evaluating customer satisfaction: linguistic reasoning by fuzzy artificial neural networks
Customer satisfaction is a measure of how a company meets or surpasses customers' expectations. It is seen as a key element in business strategy; and therefore, enhancing the methods to evaluate the satisfactory level is worth studying. Collecting rich data to know the customers’ opinion is oft...
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| Main Authors: | , , , , |
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| 格式: | Article |
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Springer
2015
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| 主题: | |
| 在线阅读: | http://eprints.utm.my/55070/ http://eprints.utm.my/55070/ http://eprints.utm.my/55070/ |
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| 总结: | Customer satisfaction is a measure of how a company meets or surpasses customers' expectations. It is seen as a key element in business strategy; and therefore, enhancing the methods to evaluate the satisfactory level is worth studying. Collecting rich data to know the customers’ opinion is often encapsulated in verbal forms, or linguistic terms, which requires proper approaches to process them. This paper proposes and investigates the application of fuzzy artificial neural networks (FANNs) to evaluate the level of customer satisfaction. Genetic algorithm (GA) and back-propagation algorithm (BP) adjust the fuzzy variables of FANN. To investigate the performances of GA- and BP-based FANNs, we compare the results of each algorithm in terms of obtained error on each alpha-cut of fuzzy values |
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