# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 5 Discussion  
Throughout the eye-tracking experiment, there were elements that were successful and went well, while there were also areas that were difficult and proved more challenging to complete. In other words, working with data involves a significant amount of understanding and then cleaning, or manipulation before the actual analysis can begin. This involved time to both understand the data and find a method that could be used to model it. For instance, a good level of prior knowledge about the raw data (e.g., gaze-point  
Figure 23: “Content viewing” screen–DTMC visualisation: (**a**) R plotting; (**b**) D3.js plotting [16]. Figure 22: Most likely gaze trajectory–“content viewing” screen [16]. coordinates and timestamps) from Tobii Studio was required for the analysis tasks. Meanwhile, the format of raw data brought some difficulties before the data modelling stage. Different requirements and tasks in relation to the experiment were also provided along with the statistical analysis based on these, and it was only after understanding the nature of the data that it became clear that gaze point co-ordinates in the raw Tobii data would need to be converted into AOI regions. As discussed in Section 3.4, a Java program was developed for:  
* Capturing coordinates of AOI regions on the screen;
* Converting gaze point to AOI block letters;
* Raw data cleaning and transferring from Mongo DB to MySQL Workbench. After a significant amount of research into data-mining algorithms such as Apriori and other methods, Markov-Chain Models were shown to be an effective technique to analyse this discrete time-sequential data as the raw data is recorded every 00:00:00.165 s, making it appropriate for the Discrete Time Markov Chain (DTMC) model. MATLAB and R Studio provide powerful functions for Markov modelling, from simply plotting a Markov model with a directed graph to a greater magnitude of information available for Markov models, such as first passage time, mean time to recurrence, absorption and the extent of the statistical knowledge it would take to understand all of these terms and how to apply them. They have been used effectively to calculate the steady states of different matrices and the expected time to absorption for the Payment state; however, in hindsight, it would have been beneficial to better understand these complexities at the outset, which may have allowed the computation of each of these aspects to run more smoothly, rather than having to research more each time. The workflow of Markov modelling allowed for easy adaptation once the first screen transition matrix had been completed, as the same process just needed to be carried out on another screen and then implemented in the same manner on MATLAB/R Studio. This was beneficial as it was quite an efficient process, allowing for changes to be made if necessary. The study was overall successful, and a lot was learned with regard to working with eye gaze movement data, exploring in-depth information for BT Player and mining the process of how participants interact with the UI on the screen. Specifically, the analysis of eye tracking data has enabled the two key research questions to be answered:  
* **\"Content purchase\"** scenario: when purchasing content (TV on Demand), what draws the eye? Is it the price, is it the quality, or is it something else? It can be said that the price is of interest to the user; however, they are particularly focused on the \"Buy to Own HD\" option, potentially due to the fact that it is highlighted in a different background colour than the other price options. As it is also on the left-hand side of the page, it can be said that the user is more likely to fixate on the HD than the SD buttons, which are on the right-hand side. Taking into account several results from other aspects of the eye tracking experiment which are not covered in this study, a more general recommendation for Purchase Flow could be \"Put important information in the top position of a screen to help the reading flow\".