Ayanso, A., & Yoogalingam, R. (2010). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce, 14(1), 79-113. doi:10.2753/JEC1086-4415140103 This article introduces the utilization of cluster analysis as a data mining tool. E-commerce has forced traditional businesses to reform their decision making processes and conduct its affairs based on activities occurring online. Monitoring web traffic is not a sufficient metric tool to measure success and therefore a system of conversion rates is utilized to determine profitability. Not everyone who visits a website purchases a product and the author describes several factors that lead to an unsuccessful visit to sales ratio. Retailers use websites to garner insight into customer activity and base decisions, but lack of sales conversions has prompted the author to conduct a cluster analysis between retailers that are solely web based and those that conduct business both from a storefront and online. Cluster analysis is a data mining technique that divides information into specific groups that provide insight and information for customer relationship management systems. The authors of the article are Assistant Professors at Brock University with Doctorates in Information Systems and they demonstrate a mastery of how to properly perform a cluster analysis. The article was fairly comprehensive and easy to read and the intended audience of the article are business owners that wish to gain insight as to why their e-commerce activities are underperforming. Cluster analysis is a technique of data mining and I believe the step by step analysis conducted by the authors will serve as a practical guide to assist me in gaining fuller understanding of the process. Baicoianu, A., & Dumitrescu, S. (2010). Data mining meets economic analysis: opportunities and challenges. Bulletin of the Transilvania University of Brasov. Economic Sciences. 5(3), 185-192. Retrieved July 21, 2011, from ABI/INFORM Global. (Document ID: 2379790851). This article introduces the concept of how data mining enables companies to determine the relationships between internal factors such as price and product positioning in comparison to external factors such as competition and economic environment. The paper elaborates how data mining can provide a competitive advantage by discovering information about business processes, customers and market behavior. The authors provide a broad overview of techniques available to conduct data mining as well as selective tasks it helps solve such as prediction, classification, detection or relations, modeling, clustering, market basket analysis and deviation detection. The authors applied their research on data mining within the context of financial application and economic analysis. The intended audience of the article would be those who are interested in developing hybrid data mining vehicles that can collect massive amounts of data from multiple sources to assist financial decision makers in exploiting opportunities as well as avoid potential pitfalls. The article was fairly easy to read and comprehensive while providing great insight into the how data mining techniques can be utilized for predictive analytics in the financial industry. Blacker, S. (2011). Emerging new customer data is a gold mine for marketers. Media Industry Newsletter, 64(26), 4. Retrieved from EBSCOhost. The author introduces the idea of an information economy and utilizing data as a raw material. Enhanced innovation in tracking of shipments, sales, web traffic and social media correspondence demonstrates the wave of information that needs to be sifted through. Marketers who have conducted thorough data mining will be able to predict consumer behavior and shift their strategies in accordance to confirmed deviation. Exploitation of internet and consumer behavior could reveal new and untapped...
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