Developing a Mobile Service-Based Customer Relationship Management System Using Fuzzy Logic

Customer relationship management (CRM) has gained lately widespread popularity in many industries. With the development of economy and society, customers are unsatisfied with the stereotyped products. As customers usually describe their demands in nature language, the demands are often conflicting with each other and are often imprecise. The paper studies the operation process of handling customer demand for modern service systems based fuzzy logic methods. While in this mobile medium times, mobile service and CRM are rarely taken into unite study. This paper overviews the related theory likes business engineering, relationship marketing and mobile business, which can be used in mobile CRM (mCRM) and in the implement of mobile CRM. The paper analyzes the underlying technology and marketing issues of the initiation of mCRM and integrat es various issues of mCRM. Moreover, the paper discusses the characteristics of useful mCRM as the implement of mCRM will help the enterprise enhance the customer relationship and customers’ loyalty will also gain more profit. A new stability criterion for the extended singular dynamic input-output model is given to ensure the stability of input-output model.

[1]  Anders Henten,et al.  Mobile and wireless communications: Technologies, applications, business models and diffusion , 2009, Telematics Informatics.

[2]  Jacky Montmain,et al.  A possibilistic-valued multi-criteria decision-making support for marketing activities in e-commerce: Feedback Based Diagnosis System , 2009, Eur. J. Oper. Res..

[3]  Macarena Espinilla,et al.  A Linguistic Multigranular Sensory Evaluation Model for Olive Oil , 2008, Int. J. Comput. Intell. Syst..

[4]  Klaus Turowski,et al.  Mobile commerce : Grundlagen und Techniken , 2004 .

[5]  Chulhyun Kim,et al.  A systematic approach to new mobile service creation , 2008, Expert Syst. Appl..

[6]  Guoqing Chen,et al.  Building an Associative Classifier Based on Fuzzy Association Rules , 2008, Int. J. Comput. Intell. Syst..

[7]  Chun-Che Huang,et al.  Rough set-based approach to feature selection in customer relationship management , 2007 .

[8]  Alexandra J. Campbell.  Creating customer knowledge competence: managing customer relationship management programs strategically , 2003 .

[9]  P. Torasso,et al.  Description and Configuration of Complex Technical Products in a Virtual Store , 2000 .

[10]  Tolga Kaya,et al.  Multi-attribute Evaluation of Website Quality in E-business Using an Integrated Fuzzy AHPTOPSIS Methodology , 2010, Int. J. Comput. Intell. Syst..

[11]  Tang Bing-yong.  Stability of extended uncertain Leontief input-output model on mobile services , 2010 .

[12]  Martin Christopher,et al.  Relationship Marketing: Creating Stakeholder Value , 2002 .

[13]  L. Ryals,et al.  Cross-functional issues in the implementation of relationship marketing through customer relationship management , 2001 .

[14]  Ravi Kalakota,et al.  e-Business: Roadmap for Success , 1999 .

[15]  Lutz Kolbe,et al.  M-commerce at Helsana Health Insurance: mobile premium calculator , 2003, 14th International Workshop on Database and Expert Systems Applications, 2003. Proceedings..

[16]  Pennie Frow,et al.  The role of multichannel integration in customer relationship management , 2004 .

[17]  D. Jain,et al.  Customer lifetime value research in marketing: A review and future directions , 2002 .

[18]  Jun Liu,et al.  A Linguistic-Valued Weighted Aggregation Operator to Multiple Attribute Group Decision Making with Quantitative and Qualitative Information , 2008, Int. J. Comput. Intell. Syst..

[19]  Chin-Tsai Lin,et al.  Mining the text information to optimizing the customer relationship management , 2009, Expert Syst. Appl..

[20]  H. Bouwman,et al.  Mobile Service Innovation and Business Models , 2010 .

[21]  Adrian Payne,et al.  Customer relationship management in financial services: towards information-enabled relationship marketing , 2001 .

[22]  Evert Gummesson,et al.  Total Relationship Marketing , 1999 .

[23]  Joseph S. Valacich,et al.  Emerging business models for mobile brokerage services , 2004, CACM.

[24]  Matti Rossi,et al.  Mobile banking services , 2004, CACM.

[25]  M. Tahar Kechadi,et al.  Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications , 2010, Expert Syst. Appl..

[26]  Horng-Jinh Chang,et al.  An anticipation model of potential customers' purchasing behavior based on clustering analysis and association rules analysis , 2007, Expert systems with applications.

[27]  Michael A. Mullens,et al.  AN AHP FRAMEWORK FOR PRIORITIZING CUSTOMER REQUIREMENTS IN QFD: AN INDUSTRIALIZED HOUSING APPLICATION , 1994 .