# Identifying the Key Hazards behind Website Drop-Offs by Solving a Survival Problem
## 4 Exploratory Analysis
### Webpage Views  
The studied data removed \"bounce visits\" from the analysis. By definition, a bounce visit would represent events of a person entering the website and thereafter, instantly dropping off. This is often a symptom of a person mistakenly entering a website or very quickly determining upon landing on a website that it was not what the visitor was browsing for. In the survival problem, bounce visits would materially increase the model noise [21]. The owners of the studied website (a South African corporate), by design, \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline \multirow{2}{*}{SessTime} & The total time that a visitor spent on the website. \\  & Flags the visits that has survived. By definition, observations \\ \multirow{2}{*}{Survived} & were considered to have survived if they viewed three or \\  & more webpages within the visit on the studied website. \\ \multirow{2}{*}{Dist} & The Euclidean distance between the visitor’s coordinates and \\  & the corporate’s geolocation. \\ \multirow{2}{*}{DaysSinceLastSession} & The count of the days since the visitor last visited the website. \\  & A value of \(-1\) is assigned if the visitor has not visited the \\ \multirow{2}{*}{OperatingSystem} & The operating system of the device, e.g., Android, etc. \\ \multirow{2}{*}{DeviceCategory} & The device used within the visit: mobile device, tablet, \\  & or computer. \\ \multirow{2}{*}{Browser} & The browser type used within the visit (e.g., Google Chrome, \\  & Microsoft Edge). \\ \multirow{2}{*}{OrganicSearches} & A flag to indicate if the visit originated from an organic search \\  & or if a link was clicked. \\ \multirow{2}{*}{PageCount} & The count of the pages viewed within the visit. \\ \multirow{2}{*}{Hits} & The total number of hits/actions on the webpage during \\  & the visit. \\ \multirow{2}{*}{H} & Flagged as 1 if the first page viewed was the ’Home’ page. \\  & CR & Flagged as 1 if the first page viewed was the ’Courses’ page. \\ \multirow{2}{*}{T} & Flagged as 1 if the first page viewed was the ’Trade-test’ page. \\  & Flagged as 1 if the first page viewed was the \\ \multirow{2}{*}{CU} & ’Contact-Us’ page. \\  & Flagged as 1 if the first page viewed was the \\ \multirow{2}{*}{SC} & ‘Short-Course’ page. \\  & Flagged as 1 if the first page viewed was the \\ \multirow{2}{*}{AP} & ‘Apprenticeships’ page. \\  & Flagged as 1 if the first page viewed was the \\ \multirow{2}{*}{ET} & ‘Engineering-Trade’ page. \\  & Flagged as 1 if the first page viewed was the \\ \multirow{2}{*}{EA} & ‘Engineering-Academic’ page. \\ \multirow{2}{*}{U} & Flagged as 1 if the first page viewed was the \\  & ‘University-of-technology’ page. \\ \multirow{2}{*}{AC} & Flagged as 1 if the first page viewed was the \\  & ‘Acctifications’ page. \\ \multirow{2}{*}{CE} & Flagged as 1 if the first page viewed was the \\  & ‘Customised-Engineering’ page. \\ \multirow{2}{*}{AU} & Flagged as 1 if the first page viewed was the ‘About-us’ page. \\ \multirow{2}{*}{L} & Flagged as 1 if the first page viewed was the \\  & ‘Learnership’ page. \\ \multirow{2}{*}{CL} & Flagged as 1 if the first page viewed was the ‘Clients’ page. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features explored within the survival problem. intended that visitors should view a minimum of three pages per visit (post exclusion of bounce visits). Figure 2 depicts the censored page-view distribution. Each person entering the studied website, at a given instance, can view any number of webpages (such as home, contact us, about us, etc.). However, according to the observed data, roughly 22% of the visitors would enter the website and only view two webpages within that website and thereafter drop off. This implies that more than one in five visits did not survive to have viewed more than two web pages per visit. This paper sought to identify the hazards that contribute to the material portion of visits that failed to survive through the use of a Cox proportional hazard model and a survival random forest model.