# Pattern-Based and Visual Analytics for Visitor Analysis on Websites
## 1 Introduction  
Analyzing and describing visitor behavior of an e-commerce site is of interest to web marketing teams, especially when assessing ad campaigns. Marketing teams are interested in quantifying their human visitors and characterizing them, for example, to discover the common elements of visitors who made a conversion (e-commerce purpose). Also, knowledge about visitor behavior on a website could benefit IT personnel, for example, as an instrument to identify bot visitors and combat click-fraud.  
One resource that can be used to analyze visitor behavior is web interaction data, such as mouse and keyboard usage; however, this type of data is not inherently available and requires the collection of information on the visitor side (which often is denied for privacy issues). Conversely, a web log file is a trace available in any server that hosts a website. The requests made by the visitors to the site are recorded in this log file, providing website owners with information about the resources requested, including details about the visitor and the resource itself. Thus, the information from web log files is a valuable asset in the analysis of visitor behavior.  
In this work, we present a new visualization that allows web marketing teams to understand how visitors navigate their site, which is key to analyze the success of a campaign or to redesign a website. Our visualization shows the user a general view of the visitors, pages, and their interactions, apart from some common web analytic measures. Moreover, we include a detailed view of a visit navigation path, which allows observing individual behavior. Yet, we do not intend to replace other visualization tools, rather we aim to complement them with interesting features.  
Furthermore, in order to help the visual model end-user understand what captures a type of visitor, we apply pattern mining techniques, in particular, contrast patterns. In this case, we are interested in seeing what separates two segments of visitors, so we extract contrast patterns from two classes. This yields patterns which outline what differentiates one segment from the other., enriching our visual model. Features could be used to define classes and form different groups of users that are of interest to characterize. For example, we can apply pattern mining to characterize the traffic that comes from a country of interest against that of the others, or what characterizes visits that yield a conversion. As use case scenarios, we present the characterization of human versus bot visitors, and an example of country segmentation.  
A summary of our contributions is as follows:  
* The design of an interactive visualization that allows users to have a comprehensive snapshot of visitors on a website, but also enables a fine-grained analysis by means of navigation graphs.
* The application of pattern mining techniques to extract patterns that characterize traffic segments of interest. The obtained patterns can aid in the selection of groups of users whose behavior would be interesting to observe. For example, we have been able to discover patterns that capture groups of interest, including (some types of) human and bot traffic. This last bit is of paramount importance, both because marketing can now give attention to clean and crisp segments of traffic, and because \(\Pi\) may block unwanted traffic, using for example firewall rules.  
The paper is organized as follows. First, in Section 2, we present a brief analysis of web analytic tools and summarize research efforts in web visualization. Section 3 describes the data and the pre-processing steps required for the visualization and pattern mining components of our approach. In Section 4, we describe our visualization design. Section 5 describes how this visualization can be enriched by the use of machine learning, specifically of pattern mining techniques. Finally, in Section 6, we discuss the applications of our work and possible extensions.