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Improved Latent Semantic Indexing-Based Data Mining Methods and an Application to Big Data Analysis of CRM

  • Jianxiong Yang
  • Junzo Watada
Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

The rapid growth of services industry over these years has led to an increased number of research works in the improvement of service quality by data mining. However, analyzing service quality and determining the factors in influencing consumer’s perception of service quality are a challenging issue. In this paper, we introduce some data mining methods from a basic one to an advance one. Finally, we use these methods to resolve Customer Relationship Management (CRM) cases and compare their efficiency. We apply statistical and machine learning techniques to study the dynamic customer level between the occurrence frequencies of events in users’ feedback and the corresponding Customer Satisfaction Index (CSI). Based on our analysis we observed that in the context of customer support centers, service experience has strongly influence on perceived customer satisfaction and service quality. Based on the research results an improved approach for innovative CRM is presented. The thesis proposes three methods and explains an application to big data analysis for CRM at the end.

Keywords

Web mining Rough set Attribute reduction matrix (ARM) Singular value decomposition (SVD) Latent semantic indexing (LSI) Customer relationship management (CRM) 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan

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