| Resumo: | Nowadays, traditional business techniques are almost deprecated due to the insurgence of the world of online virtual shopping, the so-called e-commerce. This new, in many ways, uncharted territory poses difficult challenges when it comes to apply marketing techniques especially traditional methods, as these are not effective when dealing with online customers. In this context, it is imperative that companies have a complete in-depth understanding of online behavior in order to succeed within this complex environment in which they compete.The server Web logs of each customer are the main sources of potentially useful information for online stores. These logs contain details on how each customer visited the online store, moreover, it is possible to reconstruct the sequence of accessed pages, the so-called clickstream data. This data is fundamental in depicting each customer's behavior. Analyzing and exploring this behavior is key to improve customer relationship management. The analysis of clickstream data allows for the understanding of customer intentions. One of the most studied measures regards customer conversion, that is, the percentage of customers that will actually perform a purchase during a specific online session. During this dissertation we investigate other relevant intentions, namely, customer purchasing engagement and real-time purchase likelihood. Actual data from a major European online grocery retail store will be used to support and evaluate different data mining models.
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