Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk
Keywords:decision, cross-selling, data warehousing, data mining.
This paper is about designing and implementing data warehousing and data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The database the company used is not supporting data analysis and decision-making. Therefore, it made a data warehousing design that could be used to keep data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement data warehousing and data mining which consists of literature study, company problem analysis, and data warehousing design, and testing result. The writing results are a data warehousing design and data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation data. The data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information.
Buja, A., & Lee, Y. (2001). Data mining criteria for tree-based regression and classification, Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco, CA, pp. 27-36.
Chang, C. C., & Chen, R. S. (2006). Using Data Mining Technology to Solve Classification Problems: a Case Study of Campus Digital Library. The Electronic Library Vol. 24 No. 3, pp. 307-321, Emerald Group Publishing Limited.
Chen, S. Y., & Liu, X. (2005). Data Mining from 1994 to 2004: an Application-Oriented Review. International Journal of Business Intelligence and Data Mining, Vol. 1 No. 1, pp. 4-11.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD Process for Extracting Useful Knowledge from Volumes Of Data. Communications of the ACM, Vol. 39 No. 11, pp. 7-34.
Goodman, J. (1992). Leveraging the customer database to your competitive advantage. Direct Marketing, 55(8), 26– 27.
Han, J., & Kamber, M. (2001). Data Mining: Concepts and Techniques. San Mateo, CA: Morgan Kaufmann.
Hand, D. J. (1998). Data mining: statistics and more? The American Statistician, Vol. 52 No. 2, pp. 112-8.
Kamakura, W. A., Wedel, M., Rosa, F., & Mazzon, J. A. (2003). Cross-Selling through Database Marketing: a Mixed Data Factor Analyzer for Data Augmentation and Prediction. International Journal of Research in Marketing, 20, 45-65.
Mallach, E. G. (2000). Decision Support and Data Warehouse Systems. United States: McGraw-Hill.
Wah, T. Y., Peng, N. H., & Hok, C. S. (2007). Building Data Warehouse. Proceedings of the 24th South East Asia Regional Computer Conference 2007, Bangkok, Thailand.
Yu, S. C., & Chen, R. S. (2001). Developing an XML Framework for an Electronic Document Delivery System. The Electronic Library, Vol. 19 No. 2, pp. 102-10.
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