Ave Daily Mins of <strong>Unmatched</strong> (All Viewers) x Device 2 , 2016 Games Console TV set TV service Blu Ray/DVD/VHS Other <strong>Unmatched</strong> viewing occurs across a variety of platforms. 60 50 40 30 20 10 0 4 7 1 2 5 7 9 2 6 4 6 13 2 2 14 8 6 7 11 2 4 9 12 7 2 10 22 7 17 18 1 8 5 8 6 4 4 1 0 4+ 4-15 16-24 25-34 35-44 45-54 55-64 65+ 31% of unmatched is delivered to the screen by a games console, 28% by a dedicated TV service (Sky, Virgin, Freeview etc.) and 27% by the TV set itself (integrated tuner/smart TV apps). What we find interesting is that Virgin services makes up 10% of all unmatched and Sky only 6% (compared to 14% and 36% for Total TV). Sky, unlike Virgin, does not allow Netflix or Amazon Prime Apps within its service – perhaps what we’re seeing here is viewing on SVOD services? (This would need more work to be validated however). Please go to the Appendix (p.10) for a breakdown by device! A new method to more effectively analyse viewer cohorts Traditional forms of segmenting TV audience may break down audiences into cohorts of equal size based on their levels of viewing. However this approach has one relatively problematic side-effect. With TV viewing, there are a small number of viewers who watch a lot of content, and then a large proportion of panel members who watch significantly less. The traditional form of audience segmentation therefore has a tendency to collate the heaviest viewers alongside those with completely different viewing behaviour. In this sense a panel member who views 10 hours a day, could be grouped alongside a panellist who views 2 hours a day . Is this a true segmentation? We’ve come up with a new methodology which groups people together more accurately based on their consumption behaviour. It works by splitting audiences based on their equal contribution to daily viewing rather than equal contribution in terms of respondent volume. This is done by ranking all panel members on their average daily viewing behaviour and segmenting them in terms of a 20% contribution to all average daily mins (see right, which is based on total unmatched viewing). We are able to do this through the use of our specially designed, bespoke piece of software which analyses respondent level data from Database 1. We can do this for any channel, demographic or period (going back as far as 2014). Ave Daily Hours (<strong>Unmatched</strong> <strong>Viewing</strong> 3 ) x panellist, 2016 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 SH H M L SL Ave Daily Hours x <strong>Unmatched</strong> <strong>Viewing</strong> Cohort 3 , 2016 For all but the very heaviest viewers, Linear TV still remains the go-to destination for viewing. Using the above method of audience segmentation we have analysed how audiences consume unmatched viewing. What it has revealed is that the very heaviest viewers tend to supplement their levels of live TV viewing as opposed to replacing it with unmatched forms of content. Cohort Size of Sample % Sample % <strong>Viewing</strong> Total TV <strong>Unmatched</strong> Ave Hours Ave Hrs % <strong>Unmatched</strong> Super Heavy 342 3% 20% 02:22 05:04 68% Heavy 725 6% 20% 03:00 02:24 44% Medium 1274 10% 20% 03:05 01:22 31% Light 2373 19% 20% 03:17 00:44 18% Super Light 7957 63% 20% 03:30 00:13 6% 2 Devices that constitute the summary groups can be found in the appendix (Page 10). 3 Excludes those who watched less than a daily average of 3 mins Source: BARB/<strong>Digital</strong>-i
Final Thoughts… The importance that unmatched viewing represents as a resource for furthering our understanding of the TV market and its audience shouldn’t be ignored. While the industry (and indeed many of our clients) do analyse unmatched viewing to some extent, we feel that there needs to be a greater impetus to examine unmatched viewing on a more frequent basis; whether that be forensically in detailed analytics projects or adding it to regular tracking in preexisting reporting. Clearly it has it’s limitations but we should aim to plunder it as much as possible – who knows what gems of insight lurk within? Some of our thoughts on how to take this further…. i. Beyond analysis of standard demographics are there any emergent segmentations to the audience of unmatched viewing? ii. iii. iv. How does this differ by different household dimensions (such as region, platform ownership, size, life stage etc.)? At what times do viewers tend to spend time with unmatched forms of content? Conversely, at what points of linear TV schedules more resilient to this type of viewing? Are there any clear relationships between the viewing of unmatched content and different channels and/or brands? v. Strategically, what can be done to mitigate against the negative effects of unmatched viewing? vi. vii. viii. At a panel member level, is there anything that can be learned from analysing journeys of audiences between linear TV and unmatched content? Is there any evidence that big launches on OTT services affect viewing/can these even be perceived? What can we expect from the future? Start a conversation! We would love to hear your personal thoughts on this – please get in touch!