Example Reports and Visualisations

Using Pheme, we are able to put analytics into existing digital signage deployments and utilise Web analytics as an example for third-party visualisation and reporting purposes through the mapping described above. We note that the visualisations and aggregations in the following subsections were solely the result of the mapping and did not require additional fine tuning of the dashboard – further emphasising the conceptual similarities between the Web and digital signage analytics domains. The visualisations draw on the data modelling described as part of the display-oriented data collection in Section3.3.2(Client-side Data Collection, p. 60).

4.3.3.1 Display-oriented Performance Reports

Figure4.12shows the incoming data stream as part of the Google Analytics real-time dash- board. Digital signs reporting content appear as ‘active users’ and referrers in the dashboard together with the currently shown piece of content in the form of a string. Due to the mapping of display identifiers onto the user identifier attribute, the number of ‘active users’ directly

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Figure 4.12:Pheme real-time reports produced through Google Analytics.

maps onto the number of currently active displays (i.e. displays that are showing content). Additionally, the list of referrals shows the names and identifiers of active displays. Standard features of the dashboard can be used: clicking on a referrer results in a filtering of real-time insights about the selected display.

As described in Section3.3.2(Client-side Data Collection, p. 60), we additionally utilise the custom Events hit type to describe the physical power state of displays to detect mal- functioning displays and signage players. As shown in Figure4.13, the analytics dashboards provides an overview of reported event types (e.g. ‘unresponsive’) and any other customisable event type. The analytics dashboard consists of the ability to filter for any custom event type and value and retrieve the set of referrals which directly map onto the corresponding displays. The ability to create aggregates and historical reports can be used to, for example, capture the reliability of a signage network over time. Such insights are crucial for the success of a display network as they allow network administrators and providers to easily determine malfunctioning devices.

4.3.3.2 Specific Reports for Content and Service Providers

In open display network scenarios, content providers do not necessarily know the distribution of their content items across display networks. However, understanding the content display patterns for individual content items across single or multiple display networks is a crucial piece of information for both display owners and content providers – helping to inform the

4.3 Using Web Analytics Engines for Display Analytics Reporting 108

Figure 4.13:Pheme event reports produced through Google Analytics.

4.4 Summary 109

Figure 4.15: Detailed report of displays showing a particular piece of content produced through Google Analytics.

understanding of both the reach of content and the general performance of a signage network. Such reports are particularly important for content providers (as described in Section2.4.1) to verify that their content has been played across a signage network in line with agreements with display owners. By applying the example of a basic mapping of the previously described pageviews, we are able to produce comprehensive content reports and aggregations throughout the entire lifespan of a display and piece of content. Figure4.14 shows the overview of displays (i.e. referrers) that were reported showing content, allowing administrators to drill down on a per-display basis or filter for specific content items instead to identify which display has been showing a particular piece of content at which times. For example, Figure4.15 shows an overview of the distribution and impressions of a particular piece of content across the entire digital signage network – allowing both display owners and content providers to understand the visibility scope of particular pieces of content.

4.4

Summary

In this chapter, we introduced a set of novel viewer-centric analytics reports for digital signage based on our approach of combining display-oriented analytics with viewer mobility data. Concretely, we have made the following contributions:

1. We have presented a set of novel viewer-centric analytics reports that illustrate the levels of insights that can be gained when considering viewer sightings of displays across a network of displays – including reports showing the effectiveness of displays, the

4.4 Summary 110

visibility of content across the display network, and the visibility of content to viewers. Our analytics reports were drawing on both synthetic mobility traces and the display sightings captured through Tacita.

2. We used Tacita as a use case to presented a set of analytics reports specific to supporting a display personalisation system. In particular, such reports included reports regarding usages and interactions, and an overview of using ‘retention rates’ (and its limitations). 3. We illustrated that leveraging existing Web analytics engines enabled us to create a

set of novel sign-oriented analytics reports. In particular, we presented a mapping from signage to Web analytics terminology, and designed and developed an appropriate injection module for Pheme that allowed us to report display and content related reports for the e-Campus display network.

In many analytics systems the end product is the set of reports provided. However, analytics data can also be used to inform the content shown and the behaviour of the signage network. In the following chapter, we describe the automated use of collected and computed analytics data on pervasive displays, e.g. to improve the quality of the network and the viewer experience.

Chapter 5

Automated Use of Pervasive Display

Analytics

5.1

Overview

In the previous chapters we focussed on data capture (Chapter 3) and reporting aspects (Chapter4) of next generation display analytics systems. However, analytics insights could also be used to drive content scheduling decisions on digital signs. In this chapter, we focus on the design and development of a novel content scheduling system and present the Lottery Scheduler, a new approach to dynamic content scheduling that supports both traditional content scheduling and provides the ability for context- and event-based scheduling. In particular, we describe how the Lottery Scheduler can be used as a solution given the high number of potentially conflicting content scheduling constraints and requirements that are likely to emerge in future open pervasive display networks that use analytics data to inform the content selection.

Excerpts of this chapter have been published in the following peer-reviewed publication: 1. Mateusz Mikusz, Sarah Clinch, and Nigel Davies. “Are You Feeling Lucky?: Lottery- based Scheduling for Public Displays”. In: Proceedings of the 4th International

Symposium on Pervasive Displays. PerDis ’15. Saarbruecken, Germany: ACM, 2015,

pp. 123–129. ISBN: 978-1-4503-3608-6. DOI: 10 . 1145 / 2757710 . 2757721. URL: http://doi.acm.org/10.1145/2757710.2757721