Designing a Customer Relationship Management System in Online Business


  • Negin Ziaei Mazandaran University of Science and Technology
  • Hamed Fazlollahtabar Damghan University


Business intelligence(BI), Customer relationship management(CRM), Online shopping


With the advancement of online shopping technology, it has become the first choice for most consumers. The activity of online stores in this competitive business space should be in line with the expectations of their customers. Understanding, collecting, maintaining and organize data in online stores makes it easier for managers to decide. So, in this research, we examine the textual and non-textual of user opinions and reviews. We use rapid miner software and text mining. In this research, the processes are aimed at finding active users, analyze the user type and their suggestions, analyzing the strengths and weaknesses of the products, and categorizing them with the K-NN and Naïve Bayes algorithms.  Finally, suggestions were made to increase loyalty and improve business using the results obtained from the processes.


Download data is not yet available.

Author Biographies

Negin Ziaei, Mazandaran University of Science and Technology

Department of Information Technology Engineering, Mazandaran University of Science and Technology, Babol, Iran

Hamed Fazlollahtabar, Damghan University

Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran


Bashar, A., & Wasiq, M. (2013). E-satisfaction and E-loyalty of Consumers Shopping Online. Global Sci-Tech, 5(1), 6-19.

Chao, C., Jen, W., Chi, Y., & Lin, B. (2007). Determining technology trends and forecasts of CRM through a historical review and bibliometric analysis of data from 1991 to 2005. International Journal of Management and Enterprise Development, 4(4), 415-427.

Chen, C. C., & Tseng, Y.-D. (2011). Quality evaluation of product reviews using an information quality framework. Decision Support Systems, 50(4), 755-768.

Dixit, S., & Kr, S. (2016). Collaborative Analysis of Customer Feedbacks using Rapid Miner. International Journal of Computer Applications, 142(2).

Faed, A., Hussain, O. K., & Chang, E. (2014). A methodology to map customer complaints and measure customer satisfaction and loyalty. Service Oriented Computing and Applications, 8(1), 33-53.

Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498-1512.

Gohary, A., Hamzelu, B., & Alizadeh, H. (2016). Please explain why it happened! How perceived justice and customer involvement affect post co-recovery evaluations: A study of Iranian online shoppers. Journal of retailing and Consumer services, 31, 127-142.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques: Elsevier.

Hsiao, M.-H. (2009). Shopping mode choice: Physical store shopping versus e-shopping. Transportation Research Part E: Logistics and Transportation Review, 45(1), 86-95.

Jack, L., & Tsai, Y. (2015). Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues. Paper presented at the Proceedings of the International Conference on Data Mining (DMIN).

Khan, A., Ehsan, N., Mirza, E., & Sarwar, S. Z. (2012). Integration between customer relationship management (CRM) and data warehousing. Procedia Technology, 1, 239-249.

Kim, J., Jin, B., & Swinney, J. L. (2009). Therole of etail quality, e-satisfaction and e-trust in online loyalty development process. Journal of retailing and Consumer services, 16(4), 239-247.

Kim, M.-J., Chung, N., & Lee, C.-K. (2011). The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management, 32(2), 256-265.

Kımıloğlu, H., & Zaralı, H. (2009). What signifies success in e-CRM? Marketing intelligence & planning, 27(2), 246-267.

Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer: Morgan Kaufmann.

Ling, R., & Yen, D. C. (2001). Customer relationship management: An analysis framework and implementation strategies. Journal of computer information systems, 41(3), 82-97.

Małecki, K., & Wątróbski, J. (2017). The classification of internet shop customers based on the cluster analysis and graph cellular automata. Procedia Computer Science, 112, 2280-2289.

Miner, G., Elder IV, J., & Hill, T. (2012). Practical textmining and statistical analysis for non-structured text data applications: Academic Press.

Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.

Ngai, E. W. (2005). Customer relationship management research (1992-2002) An academic literature review and classification. Marketing intelligence & planning, 23(6), 582-605.

Park, C.-H., & Kim, Y.-G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International journal of retail & distribution management, 31(1), 16-29.

Reichheld, F. F., Markey Jr, R. G., & Hopton, C. (2000). E-customer loyalty-applying the traditional rules of business for online success. European Business Journal, 12(4), 173.

Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: an exploration of its antecedents and consequences. Journal of retailing, 78(1), 41-50.

Van Riel, A. C., Liljander, V., & Jurriens, P. (2001). Exploring consumer evaluations of e-services: a portal site. International Journal of Service Industry Management, 12(4), 359-377.

Warrington, P. T., Hagen, A., & Feinberg, R. (2009). Multi-channel retailing and customer satisfaction Comparison-Shopping Services and Agent Designs (pp. 165-177): IGI Global.

Wei, J.-T., Lee, M.-C., Chen, H.-K., & Wu, H.-H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. ExpertSystems with Applications, 40(18), 7513-7518.

Xu, X., & Li, Y. (2016). The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International journal of hospitality management, 55, 57-69.

Xu, Y., Yen, D. C., Lin, B., & Chou, D. C. (2002). Adopting customer relationship management technology. Industrial management & data systems, 102(8), 442-452.

Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799-822.

Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial management & data systems, 114(9), 1344-1359.

Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews–A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115.

Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56, 211-222.



How to Cite

Ziaei, N., & Fazlollahtabar, H. (2018). Designing a Customer Relationship Management System in Online Business. Computer Reviews Journal, 2, 299-312. Retrieved from



Research Articles

Most read articles by the same author(s)