lity of electronic means for capturing demographic and psychographic data on customers and their preferences. Thanks to such advances in web-based technologies as intelligent agents, ôshopping bots,ö and ôbidding elvesö that facilitate ôautomated commerce,ö online merchants can learn much more about their customers and their preferences than can the owners of bricks-and-mortar stores (Vulkan, 1, 13). These web-based technologies consist of software programs that make it possible for consumers to perform automated searches and price comparisons on merchant websites that at the same time allow online sellers to learn their identity, access background information on them in real-time and adjust their prices and offerings accordingly, almost as if they could read their customersÆ minds and offer the most appropriate products and prices for each one (Vulkan, 1). Personalization technologies such as those used by Amazon.com allow merchants to tailor what customers see on their online website according to the preferences mined through their activities on the site.
Data mining, or ôthe automated extraction of hidden predictive information from databases,ö uses sophisticated database applications that analyze customer data to find hidden patterns and predict future purchasing behavior from them (Thearling). Using data mining techniques, merchants can find customers with common interests, offer products that a particular customer will be likely to buy, and use highly specific market targeting to make the optimum use of their marketing dollars (ôData Miningö). Data mining applications do not merely present data in a novel way but actually discover ôpreviously unknown relationships among the dataö (ôData Miningö). Such applications provide a huge advantage to merchants, who can not only save marketing dollars but increase sales simply by offering the right products to the right customers.
Data mining of proprietary mark...