The understanding of customer incidents and behaviour is crucial to the success of any organization. Evidence from literature shows a prediction pattern of products to customer. These studies predicted product characteristics leaving out the customers characteristics. To address this gap, this study aims to design datamining system and implement it on an electronic commerce organization website. The customer information and history (clickstreams) from the electronic commerce website was used to predict the customers’ behaviour. This will give meaningful and usable data patterns to organizations. Python programming language was used to design the datamining system, while PHP, HTML, and JavaScript were used for the e-commerce website. A brief description of the background of e-commerce and data mining, previous work of researchers who have worked on data mining in e-commerce settings, was reviewed and the relationship between their findings and this work was established. The data mining system utilizes consensus clustering technique and the clustering algorithm with a graphical-based approach. Furthermore, the interaction between the data mining system and the customer’s dataset on an ecommerce website was defined. Quantitative evidence for determining the number and membership of possible customer behavioural clusters within the dataset was generated.
References
[1]
Ismail, M., Ibrahim, M., Sanusi, Z. and Nat, M. (2015) Data Mining in Electronic Commerce: Benefits and Challenges. International Journal of Communications, Network and System Sciences, 8, 501-509. https://doi.org/10.4236/ijcns.2015.812045
[2]
Wang, J.C., David, C.Y. and Chris, R. (2002) Data Mining Techniques for Customer Relationship Management. Technology in Society, 24, 483-502. https://doi.org/10.1016/S0160-791X(02)00038-6
[3]
Kantardzic, M. (2003) Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Inc., New York.
[4]
Bucklin, R.E. and Sismeiro, C. (2009) Advances in Click-Stream Data Analysis in Marketing. Journal of Interactive Marketing, 23, 35-48. https://doi.org/10.1016/j.intmar.2008.10.004
[5]
Loudon, D.L. (1979) Customer Behaviour: Concepts and Applications. McGraw-Hill, New York.
[6]
Vallamkondu, S. and Gruenwald, L. (2003) Integrating Purchase Patterns and Traversal Patterns to Predict Http Requests in E-Commerce Sites. IEEE International Conference on E-Commerce, Newport Beach, CA, 24-27 June 2003, 256-263.
[7]
Michael, J.A.B. and Gordon, S.L. (1997) Data Mining Techniques: For Marketing and Sales, and Customer Relationship Management. 3rd Ed., Wiley Publishing Inc., Canada.
[8]
Zhao, X.S. and Ji, K.F. (2013) Tourism E-Commerce Recommender System Based on Web Data Mining. International Conference on Computer Science & Education, 2, 30-33.
[9]
Saloni, A. and Veenu, M. (2013) Importance of Domain Knowledge in Web Recommender Systems. International Journal of Computer Applications, 127, 10-12. https://doi.org/10.5120/ijca2015906643
[10]
Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2001) Item-Based Collaborative Filtering Recommendation Algorithms. International Conference on World Wide Web, Chiba, 10-14 May 2001, 285-295.
[11]
O’Brien, J.A. (2011) Introduction to Information Systems. The McGraw-Hill Companies, New York.
[12]
Monti, S., Tamayo, P., Mesirov, J. and Golub, T. (2003) Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Micro Array Data. Machine Learning, 52, 91-118.