Data Warehousing and Data Mining Lessons for EC Companies

Data Warehousing and Data Mining Lessons for EC Companies

Neerja Sethi (Nanyang Technological University, Singapore) and Vijay Sethi (Nanyang Technological University, Singapore)
DOI: 10.4018/978-1-59904-951-9.ch027
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Abstract

Internet companies are now in the second stage of evolution in which the emphasis is on building brands (Campman, 2001) and retaining customers rather than just transactions. There is also an imperative for multidimensional Web performance monitoring (Earls, 2005) and a continual fine-tuning of sites for optimal navigation, increased stickiness and transactional efficiency. Such research as the relationship between customer profiles and navigational characteristics (Garatti, Sergio, Sergio, & Broccab, 2004) and techniques for seamlessly aggregating Web data with corporate data (Wood & Ow, 2005) also testify to the importance of holistic data analysis for knowledge discovery. The technologies that are becoming critical in this fight for customer retention are data warehousing, data mining and customer relationship management. This article presents two case studies, one on data warehousing and the other on data mining, to draw some very specific lessons about management support, organizational commitment and overall implementation of such projects. These lessons complement past recommendations that these technologies are more about organization change (Kale, 2004), about a single unified view of the business and, ultimately about building a shared data model of the enterprise. We start with a brief overview of data warehousing and data mining. The two cases are discussed next, using a similar analytical structure to facilitate comparison among them. In the conclusion, we describe the key lessons learned from the two cases and implication

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