# Pattern-Based and Visual Analytics for Visitor Analysis on Websites
## 2 Related Work  
There are many tools available for measuring digital content. Nevertheless, they all display web analytics in a similar manner; goal reports, conversions and site performance usually are still displayed as tables, big score counters or line plots. Next, we mention five popular web analytic tools and provide a brief discussion about them. Then, we mention the research proposals related to the visualization of web behavior. Google Analytics (GA) is Google's main product for getting reports and analyzing the traffic on a website. It can be configured to import and track ad campaigns from Ad Words [1] and Double Click [2] (Google's web advertising products). It allows segmenting the traffic from many sources and by applying several filters. Also, it has the advantage of being widely known by marketing experts and people from other domain areas. ComScore [3] is an American company which provides services to evaluate media across different platforms, it has a big presence not only on the Internet but also in the TV industry, newspapers, health care, and others. Unfortunately, we could not get a further analysis of their tools because they are paid services. Despite this fact, comScore has been very open with their current research and has been publishing some reports in a periodic way [4; 5]. KissMetrics [6] provides analytic reports and email tools to increase user engagement. They provide a more tailored experience, focusing on consulting and teaching their customers on how to configure the tool and interpret the results. It also allows segmenting the traffic using filters. KissMetrics is also a paid service. Matomo [7], formerly named Piwik, it is one of the most popular and robust tools. It can be self-hosted or as Software as a Service (SaaS) in the cloud. Matomo is a company that focuses on giving their users complete control of everything, meaning that you get full reports (no data sampling, in contrast with GA). It is developed in PHP and also provides an HTTP API for consulting reportssuch as the visitor's information, goals, and pages performance, user segments, live visits information, among others. Although the Open Web Analytics (OWA) [8] project has not published any new version since 2014, it is still popular in legacy websites. It was integrated into former versions of Content Management Systems (CMS) like Wordpress or Media Wiki. It can be tested only by installing it on a personal server. One of the features included is a heatmap that shows the hottest (most clicked) sections of a website page, which can be used to optimize the placement of page information. As mentioned before, many of the web analytics reports provided by the solutions described have not changed significantly in the past few years. Such reports do not provide, for example, how visits interact with the website pages individually; most often, results are aggregated and spread in multiple reports. Figure 1 shows a common report to display goal performance and conversion counters. Although these kinds of reports provide a quick way to compare time series, they could be improved, for example, by integrating information and adding interactive controls. Regarding visitor's navigation, the common report is a table of sequential actions or as aggregated traffic flow (funnels). However, when marketers sometimes need to explore individual traces of navigation from their users. Information from individual traces, when available, is represented through simple text reports, as shown in Figure 2. Few platforms have implemented features for automatic customer segmentation. Typically, an expert manually creates filters based on arbitrary parameters such as the visitor's country, user type, visitor's language, among others. ; although this is not a problem per se, it is not trivial to select a relevant segment. We believe that machine learning can improve this process by suggesting such filtering parameters through the use of pattern mining [9; 10; 11]. Such patterns represent true segments found in the data itself.