A Study on the System for Customer Feedback Integration Inference in B2C Service Industries
Recently, due to the rapid distribution of the smart phone, real-time SNS such as Twitter and Facebook has been growing exponentially, and the service provider sales are being affected by the customer feedback (comments from blogs, cafes, SNS etc.) as the customer stance has changed from passive to active as the era of social media arrived. However, the current B2C (Business to Customer) service industry lacks both qualitative and quantitative assessments for services provided to customers, causing the same problems to occur repeatedly and periodically. There are relatively low portion of standard process and key performance index because of too many companies and differences of their sizes in these industries. One of the proof of this fact is the need for SSME (Service Science, Management and Engineering) studies in this business area. This paper suggests a method of efficient customer feedback integration for the B2C service industry. The aim of this study is the standardization of process and the development key performance of index, and to develop algorithm about these assessment factors. This method involves collecting and analyzing the customer feedback in various ways (process mining, text mining, direct survey, and face recognition) as well as inferring systematically based on the real-time feedback for satisfaction and personal requirements for providing personalized and customized services. The result of this study is the development of general purpose platform for B2C service process improvement.
KeywordsB2C (Business to Customer) Customer feedback SNS (Social Network Service) Process mining Ontology Integration inference
This work was supported by the Industrial Strategic Technology Development Program (10040142) funded by the Ministry of Knowledge Economy (MKE, Korea).
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