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 m obile 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]  Matti Rossi,et al.  Mobile banking services , 2004, CACM.

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

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

[4]  Robert C. Blattberg,et al.  Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions , 2009 .

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

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

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

[8]  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..

[9]  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.

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

[11]  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..

[12]  James F. Allen Natural language understanding , 1987, Bejnamin/Cummings series in computer science.

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

[14]  Mahesh S. Raisinghani,et al.  Mobile Commerce , 2002 .

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

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

[17]  Guoqing Chen,et al.  Building an Associative Classifier Based on Fuzzy Association Rules , 2008 .

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

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

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

[21]  Franz Lehner,et al.  Mobile und drahtlose Informationssysteme , 2003 .

[22]  D. Ruan,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..

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

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

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

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

[27]  Robert L. Glass Is this a revolutionary idea, or not? , 2004, CACM.

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

[29]  Atul Parvatiyar,et al.  Handbook of Relationship Marketing , 1999 .

[30]  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..

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

[32]  Laura Raiman Dupont The criteria: A looking glass to Americans' understanding of quality , 1997 .

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