# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Aims and Objectives  
For the whole eye tracking study at BT and Ulster University, BT initially provided a list of requirements of interest, intending to improve BT Player from the perspective of UI/UX evaluation. In this paper, we conduct our research by investigating 2 of them. These requirements targeted on specific pages on BT Player are listed below, with a brief explanation of each:  
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2. Purchase Flow Pages: BT is interested in how the user interacts with the TV on Demand service to improve the ease of use of the purchase flow (from initially choosing a TV show/film to going through with payment) to ultimately increase sales;
3. Content Discovery Pages: BT is interested in how the user interacts with the main pages of the BT Player, regarding searching for items, looking at menus and carousels (large images and descriptions on the screen to draw attention), to improve the user interface of these pages to increase sales.  
More specifically, there are comparatively representative sub-requirements under each requirement. Thus, we focus on one specific sub-requirement for each high-level one shown above as our research objectives defined by BT:  
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2. Content Purchase: \"When purchasing content (TV on Demand), what draws the eye? Is it the price, is it the quality, or is it something else?\"
3. Content Viewing: \"When a Content Discovery page first loads, what are customers viewing? Are they drawn to the hero carousel, the navigation or something else?\"  
In the study of a \"content purchase\" scenario using the BT Player, the eye-tracking data taken from the experiment will be analysed. With the use of the eye tracker software (Tobii Studio), various Areas of Interest (AOIs) can be highlighted on each screen, so the Purchase Flow screens can be analysed, and the AOIs chosen as certain sections or buttons on that screen that are more of interest than others. For example, the buttons to select a specific payment option, as well as any images or information relating to the content that is available on the screen. In general, the eye tracker records data about the user's gaze point on the screen, in addition to other relevant variables. This data will be analysed using mathematical modelling techniques to determine if certain features on the Purchase Flow pages of BT Player are of significant interest or if the user does not appear to notice other features. Likewise, the same methodology is also applied to the \"content viewing\" scenario, which is aimed at providing some insights into the gaze movement on the Content  
Figure 3: A complete view of the test environment [11].  
Discovery page in terms of customers' navigation paths, thereby generating feedback on how the user interface could be configured to be more accessible for users. Previously, there was some work related to this scenario published in October 2019 [16].  
Ultimately, our study aims to use eye-tracking technology to gain a better understanding of how users interact with BT Player in relation to the purchase of films and TV shows, as well as the discovery of content available throughout BT Player in terms of carousels, menus etc. The trajectory of gaze movement is analysed while participants are fixating on the purchase screens to highlight the areas on the screen that appear to be more of interest to the participants and attempt to gain some insights into why this is the case. Moreover, the analysis of gaze movement on the content discovery is to demonstrate the top eye-catching parts among participants, along with some preferences on the order of viewings, so that designated parts on the screen can be highlighted accordingly to draw more attention for various purposes. Consequently, the underlying process of the customer's eyes traversing over the screen is revealed.