# A High-Level Representation of the Navigation Behavior of Website Visitors
## 5 Discussion
Most non-commercial approaches focus on finding clusters of visitors or improving the runtime performance, and proposed visualizations also provide a page-by-page detail that may lead to enormous graphs that are difficult to analyze. To highlight the advantages of the method that we propose, we compared the resultant visualization (Figure 8) with the visualization of Google Analytics (Figure 2) and a non-commercial approach (Figure 9). We will refer to the two last as CSW (commercial software) and NCA (non-commercial approach), respectively. Next, we list the most relevant differences:  
* Both CSW and NCA provide page-by-page detail. Considering that most commercial websites have hundreds of pages, a graph that shows all website sessions in a given period would be (1) extremely long in CSW and (2) uninterpretable in NCA. * Both CSW and NCA allow us to filter segments of visitors, but this is not enough to have an easy-to-interpret graph. CSW allows us to select the starting webpage in the graph. However, that leads to an incomplete graph. It allows for an analysis of engagement in the selected web pages but does not allow for a visualization of the complete path followed by visitors. * CSW allows tracking pre-configured events instead of web pages but without the context of all of the web pages visited by the related visitors. Conversely, our approach makes no assumptions about the visitor behavior and provides the rules (sequences of visited pages) in the context of the complete navigation paths. * Neither CSW nor NCA eases the identification of loops. Our proposed method clearly shows loops, in a single conversion and between different conversions. * In CSW and NCA, it is hard to visualize the navigation behavior of a whole class of visitors. Therefore, it would be more difficult to compare different classes of visitors. Conversely, our approach would provide a moderately small graph for each class of visitors, and these graphs are easier to compare. This comparison of classes allows us to answer specific business questions. For example, what is the most common sequence of visited pages on which visitors of two different classes leave the website? * Generally, achieving business objectives (conversions) involves the visiting of several pages. Some business goals may be partially identified as events in CSW, e.g., the sequence of web pages that a visitor must follow to make a payment. However, visitors may follow longer common sequences that help to reduce the number of nodes. In addition, some common navigation behavior is not predictable. For example, what do visitors who did not finish the payment process have in common? Our method helps to identify points of interest whose interpretation is enriched by the opinion of a business expert. This approach assists in answering business questions in the context of the navigation behavior of all visitors in a given class, which is opposite to existing solutions that mainly aim to analyze the web page performance in detail. Next, we mention some examples of findings that would be difficult to obtain in a graph with page-by-page detail:  
* The identification of unexpected loops or repetitions that could be avoided with website enhancements. For example, in Figure 7, we can see that 5% of the traffic in the node \"Modify product and start payment\" loops in this rule. The business expert could investigate the cause, e.g., a technical error, a non-intuitive site design, or a lack of clarity in the information shown. * Identification of processes in which web traffic is lost. For example, in Figure 7, we can see that there are three rules in which the payment process can start: (1) \"Modify product and start payment\", (2) \"Consult payment details\", and (3) \"Make payments query\". However, the dropout rate is four times higher in the first two and they have a loop. A call to action in the web pages of rules 1 and 2 could decrease their dropout rate. * Finding the relationship between two conversions, regardless of whether they are subsequent or not.