# A High-Level Representation of the Navigation Behavior of Website Visitors
## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Author** & **Differences with Our Approach** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Commercial \\ software \\ \end{tabular} } & Google & They provide graphs with page-by-page detail. This helps to measure web page engagement; for example, to find pages where traffic is lost. However, this detail makes it difficult to visualize a trajectory with numerous pages or to visualize 100 percent of visitors. It is possible to track events instead of web pages, but since events are pre-configured, they do not necessarily represent the natural navigation behavior of visitors. In addition, GA does not allow you to analyze data generated prior to its use. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Non \\ commercial \\ software \\ \end{tabular} } & [PERSON] & Its goal is to find the expectation of the next page using agglomerative clustering. Visitors are classified based on previous web pages they accessed, but web pages are not clustered or associated with conversions. Therefore, classes are not necessarily meaningful to a business expert and do not provide a high-level representation of the navigation behavior. It is useful in an online implementation to improve web response time, but is not intended to facilitate analysis for MKT experts. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Nont \\ software \\ \end{tabular} } & [PERSON]. [PERSON] & Its purpose is to find the longest common sub-sequence of clickstreams using a dynamic programming algorithm. They use a similarity graph to find clusters of visitors based on the time spent on each page. In our approach, the longest path is not necessarily the most relevant in terms of business goals. In fact, we found that, in general, visitors who make a payment visit fewer pages than those who do not. In our approach, the time spent on each webpage is not relevant. Finally, the similarity graph that they build is aimed to cluster visitors but is not to be used by marketing experts for further analysis. \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] \\ et al. [21; 22] \\ \end{tabular} } & They present a novel data structure (pseudo-IDList) suitable for clickstream pattern mining. They also propose using the average weight measure for clickstream pattern mining and present an improved method named Compact-SPADE. Both approaches focus on improving runtime or memory consumption for clustering visitors, but no business knowledge is incorporated to create those clusters. In addition, they do not create a high-level graph of the clusters for further analysis by business experts. \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} [PERSON] \\ [34] \\ \end{tabular} } & He presents a visualization tool for analyzing website traffic. It aims to distinguish bots from human visitors based on their navigation path. Like commercial software, it provides a page-by-page detail, which makes it difficult to analyze numerous pages. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} [PERSON] \\ [35] \\ \end{tabular} } & They combine visualization and machine learning techniques for analyzing web log data. The visualization can be used by business experts to obtain insights by looking at key elements of the graph. However, visitors are not classified in terms of business goals. Furthermore, the visualization provides page-by-page detail, which makes it difficult to analyze visits with numerous nodes. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Differences between our method and previous approaches. Web analytics software is essential for measuring website traffic and follow-up marketing campaigns. Nevertheless, its standard configuration and reporting options make it hard to extract high-level knowledge about the navigation behavior of different classes of visitors, especially due to the high amount of website traffic and the diversity of paths that visitors could follow.