Measuring mobile video audiences and associated ad engagement is one of the greatest challenges facing the pay TV industry, with big rewards for getting it right. Mobile video has surged over the last year, with phones and tablets accounting for 46 per cent of all online viewing globally during Q4 2016, up from 34 per cent a year earlier, according to video technology vendor Ooyala. Ad spending is moving with the eyeballs and in the UK for example more of it will be on mobile than mainstream TV for the first time this year, £4.58 billion ($7 billion) against £4.18 billion ($6.39 billion), according to eMarketer.
While some pay TV operators may have reasonable visibility over viewing on desktops, mobile devices raise complexity to another dimension. On desktops access to web sites and services is almost all via browsers, but on mobiles these only account for a minority of viewing. It is true that the majority of web sites are accessed from mobiles too via the browser, for obviously individual users only have room for a certain number of apps on their devices. But apps account for the great majority of time spent on mobiles and also for most traffic, because users tend to hang out in just a few places. Those places are accessed via apps rather than the browser, including the likes of Facebook, Google Maps and WeChat. However an interesting and relevant trend for operators during 2016, which has been highlighted by analyst group Forrester, is that users are increasingly turning towards aggregation apps to access the content they want.
When access is predominantly via a browser as on the desktop PC cookies can be used to track viewing activity and measure ad engagement. But cookies do not work well in the mobile world because activity is partitioned between the mobile browser and the various apps isolated from each other via sandboxing, which is a fundamental property of both the dominant mobile OSs, Android and Apple iOS. Web sites accessed within apps open via dedicated custom browsers which means that they cannot interact with persistent cookies on the device, which precludes use of proven desk top measurement tools. In the case of iOS devices, the situation is just as bad even for sites accessed via the mobile browser because Apple prohibits use of third party cookies.
There are also higher level challenges for mobile TV advertising such as defining how long people should watch an ad for it to count as having been viewed, given that attention spans are shorter on small screens. The situation is similar for the actual TV content, where the value of mobile viewing can depend on context, being particularly high when there is synergy with the big screen for example to resume watching something started earlier.
The overall challenge then is to integrate audience measurement and analytics across all screens including mobile to deliver consistent information that takes account of differences in context and engagement across the different platforms. There are now plenty of tools available for tracking activity on the mobile side, but integrating them within a coherent end to end measurement and analytics system is highly complex. Some big operators are attempting to do this in-house but increasingly even they are turning to specialist TV audience companies to enable the integration.
Another TV analytics company TVbeat, also UK based, has moved in a similar direction, in this case through a partnership with a dedicated TV app company Metrological. This has enabled TVbeat to meld set top data with mobile device return path and app consumption information from Metrological’s Application Platform.
Such developments ease the pain of mobile audience measurement for pay TV operators and we expect to see more that have previously relied solely on in-house development to at least consider working with one of the specialist analytics companies that are in a better position to aggregate data from many sources. With mobiles accounting for a rapidly increasing proportion of both viewing and ad budgets, operators need to embrace that with their existing actionable data analytics.