Oracle Personalization

Analyzing page viewing behavior and creating custom web page content on a busy eCommerce site is a formidable computing challenge. To address these issues, Oracle has developed the Oracle9iAS personalization and the Oracle data mining suite. Oracle personalization is extremely sophisticated and relies on internal data about end-users web-page visits, web-page clicks, and referrer statistics.  Even more powerful, Oracle personalization allows for the incorporation of external metadata such as customer demographics.  It is worthwhile to note that Oracle has several competitors in the web personalization market, notably Blue Martini, Vignette and Personify.

The goal of Oracle9iAS personalization is to accurately identify classes of end-users and correlate their behavior with the behavior of other known groups of end-users.  Using sophisticated multivariate correlation techniques; web-page contact can be customized according to predictions about each end-users preference for web page content.  The nature of this analysis is very resource intensive, and almost all large Oracle9iAS shops devote large servers exclusively for developing these predictive recommendations.

IT marketing professionals know that it is critical of get the right products onto a custom web page. To be successful, Oracle9iAS must be able to accurately predict your propensity to buy a product, based on prior buying and browsing patterns, and buying patterns of like-minded customers (customer profiling).  The challenge in developing these predictive models is accurately placing visitors into consumer groups. A consumer group is a group of customers with similar demographics and buying patterns.

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