# Identifying the Key Hazards behind Website Drop-Offs by Solving a Survival Problem
## 6 Conclusions  
The shareholders of the studied website were concerned with the high degree of premature drop-offs. According to shareholder expectations, visitors should view at least three or more web pages per view. A low page view website implies that the marketing cost invested in the website is not being justified according to the intended purpose. This study employed a traditional survival model (through the use of the Cox-proportional hazard model) and a machine learning model (survival random forest model) to identify the underlying hazards that drove the high degree of drop-offs observed. The Cox-proportional hazard model was able to indicate features that were statistically significant in contributing to survival rates, whilst the survival random forest model was able to identify the features that are important in predicting the survival rates. Although the models employed are fundamentally different, the features identified as hazardous were understandable and evident in the empirical data. Table 2 below depicts the features that were identified as hazardous under each model. Table 2 marks the features that were identified as possible hazards by the Cox-proportional hazard model and the survival random forest model. Whilst both models are very different in theory, it was observed that a material overlap existed in the features identified as hazards. The features, \"Browser\", \"ET\", \"Hits\", and \"OrganicSearches\", were identified as hazardous by both the Cox-proportional hazard model and the survival random forest model. The survival random forest model has been shown to better identify the hazardous features that were non-linear, such as \"Dist\" and \"DaysSinceLastSession\". Furthermore, the Cox-proportional hazard model identified the feature, \"deviceCategory\", whilst the survival random forest model did not. The study illustrates how the hazards of the studied website could be successfully identified through a survival problem. The hazard, \"Browser\" (such as Google Chrome, Explorer, Firefox, etc. ), indicates that there potentially could be a compatibility flaw on certain browsers that results in visitors prematurely dropping off. The developers of the website would need to re-test the website loading on the browsers of concern. The feature, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Cox-Proportional Hazard Model** & **Survival Random Forest Model** \\ \hline AC & & \\ AP & & \\ AU & & \\ Browser & ✓ & ✓ \\ CE & & \\ CL & & \\ CR & & \\ CU & & \\ daysSinceLastSession & & \\ deviceCategory & ✓ & \\ Dist & & \\ EA & & \\ ET & ✓ & ✓ \\ H & & \\ Hits & ✓ & ✓ \\ L & & \\ OperatingSystem & & \\ OrganicSearches & ✓ & ✓ \\ SC & & \\ T & & \\ U & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Web survival hazards comparison between models. \"ET\", was detected as hazardous and thereby, indicated that visitors landing onto the website through this webpage were perhaps discouraged by this webpage. Further tests would need to be conducted to isolate the underlying reason (through random AB testing). Visitors could have either dropped off due to the wording, due to the navigation to other webpages from this webpage, due to the imagery, etc. The feature, \"daysSinceLastSession\", was detected as a non-linear hazard. Exploratory analysis has shown that visitors who were new (first time visitor) or visited a very long while ago have been shown to have had a higher survival rate. This implies that people who recently visited the website previously either entered again mistakenly or came on looking for specific information and thereafter dropped off. The \"deviceCategory\" feature was selected as a hazard as visitors using a \"desktop PC\" had a higher tendency to drop off relative to \"tablet\" and \"mobile\" device users. Further research needs to be conducted to understand this behavior. Further exploratory analysis has indicated that visitors from foreign countries enter the studied website and shortly dropped off.