The knowledge of display sightings (both based on synthetic analytics and Tacita) enables us to create reports regarding the content visibility in addition to the ‘content impressions’ metric commonly used in signage and Web analytics. We note that in contrast to the Web domain, the fact that a content item was shown on a display does not mean that that the content was viewed, e.g. due to the lack of audience or viewers not paying attention to the display. The knowledge of display sightings, however, allows us to create more accurate estimations of content views. Additionally, we can disconnect content from displays and consider the visibility of content across the entire display network instead of a ‘per display’ basis.
4.2.3.1 Reports Based on Synthetic Analytics
Drawing on the synthetic analytics approach and using theRandom Building Navigatoras a baseline, we computed a mean number per content item per day of 217 total views (SD=507) and 116 unique views (SD=190). The most viewed content items include content that has been scheduled onto one of the most viewed displays on campus exclusively (Nuffield External) and content that is scheduled across the university campus. Content with the least views include, not unexpectedly, content that has been scheduled on single displays only (e.g. ISS).
Understanding in more detail how viewers experience content and view the same piece of content multiple times is particularly interesting for content that has been scheduled to show
4.2 Analytics Based on Viewer Data 91 100 101 102 103 x 10−2 10−1 100 P ( X > x )
(a)Random Building Navigator.
100 101 102 103 x 10−2 10−1 100 P ( X > x ) (b)On-Campus Student. 100 101 102 103 x 10−2 10−1 100 P ( X > x ) (c)Off-Campus Student.
Figure 4.4:Cumulative distribution function of frequencies of content views for the three synthetic analytics mobility models (initially published in [Mik+16]).
on multiple displays of the University campus. We can create reports that give us insights into the frequency of views of content items seen repeatedly by the same user. Content providers can benefit from such insights to optimise the design and distribution of content. For example, content providers might wish to design their content appropriately if it is viewed repeatedly, whilst other content providers might prefer to reduce the visibility of their content items across a signage network. To retrieve insights into repetitive views for the same content items, we have created a cumulative distribution function of the frequencies of content views (Figure4.4). Whilst theRandom Building Navigator(Figure4.4a) andOn-Campus Student(Figure4.4b) view the same pieces of content with similar frequencies, theOff-Campus Student(Figure4.4c) views items with a lower frequency with the exception of a single piece of content viewed with a noticeably higher frequency. This is likely due to the mobility patterns of the model: students arrive at the central bus stop on campus which is located near the Alexandra Square West display, typically only showing real-time bus timetables as the single piece of content, and is therefore seen by most students arriving and leaving from this bus stop.
Using our simulated location traces in combination with content views, we can additionally create reports to estimate the duration viewers spend looking at displays. We note, however, that this statistic is highly approximate due to a number of factors. Firstly, the views are an approximation themselves and based on probability and mobility models. Secondly, to compute the mean durations viewers spend glancing at displays, we make use of previous work focussing on measuring the mean time passers-by spend glancing at displays. Dalton, Collins, and Marshall [DCM15], for example, suggest based on an eye-tracking study that passers-by glance at displays for a mean duration of 0.318 seconds (SD: 0.261) whilst work
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Figure 4.5:Number of total requests per Tacita application per day (initially published in [Mik+18d]).
by Huang, Koster, and Borchers [HKB08] indicates based on observational studies a glance period of 1-2 seconds. Using both figures as the lower and upper bounds, we are able to compute mean view times between 1.15 and 5.43 minutes per content item per day aggregated across all agents. The mean total view duration per content items equates to 20.33-95.88 minutes.
4.2.3.2 Reports Based on Tacita
Tacita data can also be used to generate the same types of analytics as described above (Section4.2.3.1). However, in Tacita users request personalisable content (Trusted Content Providers) automatically when their presence is detected in proximity to a display. Using these display sightings, we can produce new analytics that consider the visibility of such personalisable content requests and therefore use it to provide insights about the usage of Tacita applications. For example, we can consider the total number of requests of personalisable applications issued by the users’ mobile devices when a display was detected in proximity to understand better which applications are more popular. Figure4.5shows the total number of requests per application per day across the University campus. Such a report can be used by both display owners and content providers to better understand the popularity of individual
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Bus WelcomeWeather Clock News TV Twitter Pics
Application
0 20 40 60 80 100Total Number of Unique Users
Figure 4.7:Total number of unique users per Tacita application (initially published in [Mik+18d]).
content items and applications shown on the display network without the focus on individual displays or users. Using content requests as a metric, the most popular applications include Bus Timetables (personalised travel), Weather and Clock, whilst we find Twitter (personalised social media) among the least requested displays. The spread and popularity of display applications may reflect the property ofpublicdisplays: users prefer not to retrieve highly personalised content such as a newsfeed from their social media.
Whilst Tacita only captures anonymised user interaction and content requests, we can use the anonymised unique user identifiers to further narrow down the reports and report
unique user metrics similar to what can be found in typical Web analytics. For example,
Figure4.6shows the number of unique users per day for each of the supported application. In contrast to reporting the total number of requests, the reports using unique users enables us to better understand the popularity of individual applications disconnected from the frequency or mobility patterns of users that may ultimately lead to high or low requests.
Moving away from reports regarding individual days, we are able to confirm these findings by creating reports considering thetotal number of unique users, i.e. users who have requested an application at least once in the lifetime of the deployment. Figure4.7 provides a bar chart ordered in descending order by the application with the highest number of unique users. The most popular application includes Bus Timetables, followed by the Welcome application. This result indicates that whilst a large number of users configures and activates other application, they choose not to deactivate the preconfigured Welcome application before the first appearance at a display.
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4.2.3.3 Stakeholder Analysis
The reports outlined above provide the following set of benefits to individual stakeholder groups.
Display Owner Display owners can gain a detailed understanding in the interaction patterns of users with their displays and the types of content viewers have been interested in across the entirety of the display network. In the context of Tacita, display owners get insights into the types of personalisable applications that have received the most interest among viewers.
Space Owner Space owners typically have only limited control over the displays de- ployed in their space. Providing insights into the visibility of content across the entire network (or across spaces owned by individual stakeholders) can provide crucial in- sights on the types of content that is shown in the space, and the popularity of different categories of content.
Content Provider The knowledge on the duration individuals viewed content items, and how often content was viewed across the entire display network can be valuable to content providers. For example, durations of content seen can be used to optimise the amount of information presented on a screen (e.g. include less content on the display if viewers see content less often or for shorter periods of time overall). Furthermore, content providers can use these analytics reports as confirmation that content has been scheduled and seen across a display network. In the context of personalisable applications, analytics reports described above can be used for monitoring purposes in order to understand whether certain applications receive viewer location information and are successfully scheduled onto displays.
Viewer Viewers benefit from content-related reports indirectly by improved content scheduling cycles and, more generally, an improved quality of the content presented. Individual content can be better tailored towards viewers if, for example, content that has been seen less frequently in relation to other content is prioritised across the display network to improve its visibility.