# A High-Level Representation of the Navigation Behavior of Website Visitors
###### Abstract  
Knowing how visitors navigate a website can lead to different applications. For example, providing a personalized navigation experience or identifying website failures. In this paper, we present a method for representing the navigation behavior of an entire class of website visitors in a moderately small graph, aiming to ease the task of web analysis, especially in marketing areas. Current solutions are mainly oriented to a detailed page-by-page analysis. Thus, obtaining a high-level abstraction of an entire class of visitors may involve the analysis of large amounts of data and become an overwhelming task. Our approach extracts the navigation behavior that is common among a certain class of visitors to create a graph that summarizes class navigation behavior and enables a contrast of classes. The method works by representing website sessions as the sequence of visited pages. Sub-sequences of visited pages of common occurrence are identified as \"rules\". Then, we replace those rules with a symbol that is given a representative name and use it to obtain a shrinked representation of a session. Finally, this shrinked representation is used to create a graph of the navigation behavior of a visitor class (group of visitors relevant to the desired analysis). Our results show that a few rules are enough to capture a visitor class. Since each class is associated with a conversion, a marketing expert can easily find out what makes classes different.  
web analytics; web log mining; clickstream analysis; sequence mining; sequitur; graph techniques +
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are familiar. Examples of website conversions are: to pay for a product or a service, to fill in a form with contact details, or to post a positive product review.  
To describe the navigation behavior of a whole class of visitors, we started by representing sessions as a sequence of visited pages. From the sequences representing sessions of a given class, we extracted the most frequent subsequences of pages. We called those sequences \"rules\". Each rule is formed by different pages and represents visitors actions. For example, making a payment or searching for the availability of a product. We named the rules and used them to obtain a reduced representation of each session; session reduction is a result of replacing sub-sequences (of length greater than or equal to two) for the name of the corresponding rule. The representation of sessions with rules drastically reduces the length of sessions (for example, from forty pages to two rules). Various types of analyses can be performed with the reduced representation of sequences. For example, comparing the frequency of a given rule in two different classes of visitors. We show the result of our analysis in a graph to facilitate the explanation oriented to marketing experts. Using the described method, we reduce hundreds of nodes and edges into a simplified graph that captures the navigation behavior of a whole class of visitors. The graph also assists in the comparison of the navigation behavior of different classes of visitors. It prevents the marketing expert from analyzing huge graphs to understand the navigation behavior of visitors. Our four-step methodology is shown in Figure 1.