Decomposition of a term-document matrix representation for faithful customer analysis

Jianxiong Yang, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The recent rapid growth of the services industry has led to an increase in the number of service quality improvement research studies. However, analyzing service quality and determining the factors influencing consumers' perceptions of service quality are a challenging problem. The objective of this paper was to apply a data mining method to the current problems of Customer Relationship Management (CRM), analyze corporate communications systems and then identify possible data mining applications. We apply simple statistical and machine-learning techniques to study the dynamics of occurrence frequencies of events by scrutinizing user comments and corresponding customer satisfaction scores. Our analysis revealed that in the context of customer support centers, the service experience of customers strongly influences the satisfaction and service quality that the customers experienced. As a result of this study, we have identified a method of capturing the hearts of faithful customers.

    Original languageEnglish
    Title of host publicationFrontiers in Artificial Intelligence and Applications
    Pages168-177
    Number of pages10
    Volume255
    DOIs
    Publication statusPublished - 2013

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    Volume255
    ISSN (Print)09226389

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    Keywords

    • Customer Relationship Management
    • Data Mining
    • LSI

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Yang, J., & Watada, J. (2013). Decomposition of a term-document matrix representation for faithful customer analysis. In Frontiers in Artificial Intelligence and Applications (Vol. 255, pp. 168-177). (Frontiers in Artificial Intelligence and Applications; Vol. 255). https://doi.org/10.3233/978-1-61499-264-6-168