Modeling historical user behavior for enhanced ad targeting

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Online advertising is one of the main revenue streams of many web based businesses. Third parties advertise their products and services on Globe and Mail's web site. Currently the system treats all the ads and user needs and behavior as of equal importance. Therefore a standard ad rate is applied without considering the future user behavior. Globe and Mail would like to understand historical user behavior and their interactions with ads on their web site to predict the purchase behavior of their customers. Such a prediction would enhance ad targeting and allow them to charge higher ad rates.Previous shopping behavior has significant effect on future shopping behavior. Understanding and predicting visitor's purchasing behavior during the current visit is of great importance. It is important to be able to predict purchasing behavior early during the visiting process, in contrast to previous research/ models that predicted purchase behavior at the end of the visit. In this project we aim to build a prediction model that predicts the purchase behavior throughout the clickstream. Given that predicting online purchasing behavior throughout the visitor's clickstream is a relatively new area, this project will provide more insight into this topic. Although consumer behavior in traditional offline stores has a large scientific base, it is in many ways different from online consumer behavior. In this project we will use visit level aggregated clickstream data, and pageview level disaggregated clickstream data to incorporate more detailed information to model purchasing behavior throughout the clickstream.