ICTACT Journal on Management Studies

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Customer Satisfaction in SCRM with Key Performance Indicator System


Affiliations
1 Department of Computer Science, Manaonmaniam Sundaranar University, India
2 Department of Computer Science, Government Arts College, Karur, India
     

   Subscribe/Renew Journal


Customer relation management is a significant feature in the business development which assists for business peoples to get the knowledge about the customer's opinions and can establish the profitable environment. Customer opinions were utilized to enhance the product, so that customer fulfilment and profit will rise desirably. So it is necessary to execute the functional design of customer opinions regarding the products which depends on the time duration. In the earlier work, Customer Knowledge Management (CKM) is established to raise the profit level of industries by tracking the entire regarding the product which is demanded by the customers. Nevertheless, creating and managing the CKM for the huge volume of the customer is a complex task. It results in inaccuracy, if in case it is done manually with less customer information. This issue is rectified in the proposed research methodology by bringing-in the methodology such as Neural Network based Social Customer Relation Management (NN-SCRM). This algorithm is utilized to examine the following factors by gathering the knowledge of the customer review information: "Predict the future, most profitable customers, maintaining quality of product development, customer life time value, identify customers and their products". This is done according to the information of the customer review, which, in turn, obtained from the customers opinions disclosed by their comments regarding the product. This proposed research work is executed and examined in the MATLAB simulation environment from which it is confirmed that the proposed research framework tends to give the best output than the current CKM frameworks.

Keywords

Knowledge Management, Review Analysis, Pre-Processing, Customer Opinions, Profit.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Colleen Cunningham and Il-Yeol Song, “A Taxonomy of Customer Relationship Management Analyses for Data Warehousing”, Proceedings of 26th International Conference on Conceptual Modeling in Tutorials, Posters, Panels and Industrial Contributions, Vol. 83, pp. 97-102, 2007.

  • J. Wu, “Customer Relationship Management in Practice: A Case Study of Hi-Tech Company from China”, Proceedings of International Conference on Service Systems and Service Management, pp. 1-6, 2008.

  • B. Solis, “Engage: The Complete Guide for Brands and Businesses to Build, Cultivate, and Measure Success in the New Web”, John Wiley & Sons, 2010.

  • J.H. Kietzmann, K. Hermkens, I.P. McCarthy and B.S. Silvestre, “Social media? Get Serious! Understanding the Functional Building Blocks of Social Media”, Business Horizons, Vol. 54, No. 3, pp. 241-251, 2011.

  • A. Payne and P. Frow, “A Strategic Framework for Customer Relationship Management”, Journal of Marketing, Vol. 69, No. 4, pp. 167-176, 2005.

  • A. Neely, M. Gregory and K. Platts, “Performance Measurement System Design: A Literature Review and Research Agenda”, International Journal of Operations and Production Management, Vol. 15, No. 4, pp. 80-116, 1995.

  • S. Gupta and V. Zeithaml, “Customer Metrics and Their Impact on Financial Performance”, Marketing Science, Vol. 25, No. 6, pp. 718-739, 2006.

  • F.F. Reichheld, “The One Number You Need To Grow”, Harvard Business Review, Vol. 81, No. 12, pp. 46-55, 2003.

  • D.L. Hoffman and M. Fodor, “Can You Measure the ROI of Your Social Media Marketing?”, MIT Sloan Management Review, Vol. 52, No. 1, pp. 41-49, 2010.

  • B.D. Weinberg and E. Pehlivan, “Social Spending: Managing the Social Media Mix”, Business Horizons, Vol. 54, No. 3, pp. 275-282, 2011.

  • D. Chauvel and C. Despres, “A Review of Survey Research in Knowledge Management: 1997-2001”, Journal of Knowledge Management, Vol. 6, No. 3, pp. 207-223, 2002.

  • S.H. Liao, “Knowledge Management Technologies and Applications-Literature Review from 1995 to 2002”, Expert Systems with Applications, Vol. 25, No. 2, pp. 155-164, 2003.

  • M. Du Plessis, “Knowledge Management: What makes Complex Implementations Successful?”, Journal of Knowledge Management, Vol. 11, No. 2, pp. 91-101, 2007.

  • Z. Guo and J. Sheffield, “A Paradigmatic and Methodological Examination of Knowledge Management Research: 2000 to 2004”, Decision Support Systems, Vol. 44, No. 3, pp. 673-688, 2008.

  • Z. Ma and K.H. Yu, “Research Paradigms of Contemporary Knowledge Management Studies: 1998-2007”, Journal of Knowledge Management, Vol. 14, No. 2, pp. 175-189, 2010.

  • A. Serenko, N. Bontis, L. Booker, K. Sadeddin and T. Hardie, “A Scientometric Analysis of Knowledge Management and Intellectual Capital Academic Literature (1994-2008)”, Journal of Knowledge Management, Vol. 14, No. 1, pp. 3-23, 2010.

  • D.P. Wallace, C. Van Fleet and L.J. Downs, “The Research Core of the Knowledge Management Literature”, International Journal of Information Management, Vol. 31, No. 1, pp. 14-20, 2011.

  • Y.K. Dwivedi, K. Venkitachalam, A.M. Sharif, W. Al-Karaghouli and V. Weerakkody, “Research Trends in Knowledge Management: Analyzing the Past and Predicting the Future”, Information Systems Management, Vol. 28, No. 1, pp. 43-56, 2011.

  • M.R. Lee and T.T. Chen, “Revealing Research Themes and Trends in Knowledge Management: From 1995 to 2010”, Knowledge-Based Systems, Vol. 28, pp. 47-58, 2012.


Abstract Views: 265

PDF Views: 0




  • Customer Satisfaction in SCRM with Key Performance Indicator System

Abstract Views: 265  |  PDF Views: 0

Authors

P. Menaka
Department of Computer Science, Manaonmaniam Sundaranar University, India
K. Thangadurai
Department of Computer Science, Government Arts College, Karur, India

Abstract


Customer relation management is a significant feature in the business development which assists for business peoples to get the knowledge about the customer's opinions and can establish the profitable environment. Customer opinions were utilized to enhance the product, so that customer fulfilment and profit will rise desirably. So it is necessary to execute the functional design of customer opinions regarding the products which depends on the time duration. In the earlier work, Customer Knowledge Management (CKM) is established to raise the profit level of industries by tracking the entire regarding the product which is demanded by the customers. Nevertheless, creating and managing the CKM for the huge volume of the customer is a complex task. It results in inaccuracy, if in case it is done manually with less customer information. This issue is rectified in the proposed research methodology by bringing-in the methodology such as Neural Network based Social Customer Relation Management (NN-SCRM). This algorithm is utilized to examine the following factors by gathering the knowledge of the customer review information: "Predict the future, most profitable customers, maintaining quality of product development, customer life time value, identify customers and their products". This is done according to the information of the customer review, which, in turn, obtained from the customers opinions disclosed by their comments regarding the product. This proposed research work is executed and examined in the MATLAB simulation environment from which it is confirmed that the proposed research framework tends to give the best output than the current CKM frameworks.

Keywords


Knowledge Management, Review Analysis, Pre-Processing, Customer Opinions, Profit.

References