Mining Unmatched Viewing
(And using it to shine a light on changing viewing habits)
A case study by digital.i analytics
A resource for understanding
changing viewing habits?
It's a truth self evident that the way in which we engage with our
favourite TV shows has changed over the last few years.
Undoubtedly this has been driven by the arrival, and success of new
breeds of SVOD services into the market. Thanks to their deep pockets
and savvy content strategies these platforms have gained an incredible
amount of market share in a relatively short period time. It’s certainly
hard to ignore the fact that 57% of UK 16-24's and 39% of 25-34’s now
come into an SVOD service at least once a week 1 .
Another chapter in this story of changing audience behaviour is to do
with how and where we watch our favourite programmes. Audiences
are now increasingly comfortable viewing content via tablets and other
internet enabled devices. As of 2016, 34% of adults BVOD viewing and
35% of their SVOD viewing was spent viewing content on PC’s, tablets
and smartphones. For 16-24’s this is even higher at 51% and 52% 2
(with evidence that this has and will continue to grow in 2017).
Furthermore, unemployment levels among young people have been
reducing significantly since 2012. The amount of young people in full
time employment or education in 2016 resembles that of prerecession
levels in 2007 3 – surely this must create a less available, yet
perhaps more commercially attractive audience?
With these developments and many more putting pressure on the
industry, understanding how as audiences we interact with our screens
has never been more important.
In usual fashion BARB is responding well to the changing market,
working hard to maintain the high level of trust agencies, broadcasters
and more place in the JIC. Most recently this has manifested in the
announcement of new screen time files which will provide accurate
information into the aggregated levels of (you guessed it!) screen time.
Longer term, we’re looking forward to Project Dovetail which will
provide standardised, census level measurement of BVOD and online
viewing on devices such as tablets and PC's.
However a time in which we are able to say with relative confidence
how many people watched the new season or House of Cards or how
viewers distribute their time across different services and channels is
quite likely some way off. Technically speaking, the infrastructure is in
place for BARB to analyse viewing from OTT services but it requires the
cooperation of these providers to supply audio references for its
content libraries (and no word on this yet).
In the meantime, analysis of ‘unmatched viewing’ helps us feel around
the edge of what is going on in our digital day. Of course, bodies such
as Ofcom and Thinkbox approach this subject with regularity,
implementing various primary studies that provide phenomenal
understanding and insights into these issues.
However, analysing these new developments within BARB’s framework
is crucial. It allows us to further our understanding of screen behaviour
within the context of linear viewing, while using a standardised
Unmatched Activity is an umbrella, “catch all” comprised of essentially
anything that BARB cannot identify. It includes (but is not limited to);
paid for and free VOD services, older content on TV catch up services,
use of games consoles, DVD’s and more (see next page 3 for a detailed
breakdown of what unmatched viewing consists of).
While we’re unable to currently dissect unmatched viewing into these
components (although we are working on a interim solution to this,
please go to page 8 for more information) we are able to analyse it
with the same level of rigour and detail that we do normal TV viewing.
Within this document we've done just that; aiming to provide some
useful stats and insights for your viewing pleasure as well as clearing up
some of the confusion that exists in understanding exactly what
unmatched viewing is.
Not only that but using a novel proprietary segmentation methodology
and by mining respondent level viewing data, we’ve broken unmatched
viewers by their weight of viewing. This has revealed some interesting
findings that are well worth a look (see page 4-6).
Lastly, with some rather grand developments going on at Digital.i, we
wanted to take a moment to reach out to our clients, friends and
contacts to showcase some of things that we are working on, and what
you can expect from us in the future.
We hope you enjoy reading!
1 Ofcom Digital day 2016, Ofcom CMR 2017
2 Source: Touchpoints 2016, IPA.
3 This is in large part as a result of legislation in 2015 requiring students to commit to sixth
form, college or an apprenticeship until the age of 18
- 2 -
Head of Analytics
To understand what unmatched viewing consists of, it is necessary to
understand that BARB uses audio matching to identify what is being
watched on a screen at any given time. This is a complex process but it
fundamentally all comes down to sound.
The panel meters present in 5,100 homes take samples of the sound
being broadcast and convert these to “fingerprints” that can then be
matched against a reference library of content supplied by content
Using this process, BARB is able to identify who is watching what -
generating the gold standard figures broadcasters, agencies and
bureau's analyse every day. This includes 1 :
• Viewing via TV sets to content broadcast in the past 28 days by
BARB-reported channels (regardless of whether it is viewed live,
via a recording, or from a catch-up service);
• The same types of viewing to non-BARB reported channels where
BARB hold an audio reference for the channel, or where the
channel is viewed via the Sky platform. In practice the only non-
BARB reported channels BARB hold references for are some of
the larger DTT channels;
• Viewing to programmes premiered or available on BBC iPlayer,
where the BBC has provided an audio reference;
Any other use of the TV set, which fundamentally BARB does not hold
an audio reference for would be classed as unmatched viewing.
This would include:
• Viewing to any channel outside the Sky platform for
which BARB does not hold an audio reference
• On-demand content for which BARB does not hold
an audio reference
• DVD playback
• In some cases EPG (depending on the behaviour of
the audio – often the channel audio will remain,
with or without a reduced size picture)
• User generated content (wedding videos, etc)
• Other devices that may be used on the TV such as
security cameras, Raspberry Pi, etc.
1 ”Viewing” to radio stations is also measured as well a few miscellaneous uses for the Sky box such as
(interactive menus). For a full breakdown what itemised please contact us or BARB directly.
How big is unmatched viewing?
In 2016 time spent with unmatched
forms of activity represents 14% of all
screen time in the UK - up from 11%
Among 16-24’s, unmatched is 31%
of screen time – up from 21% in 2014.
Inclusion of unmatched
viewing, neutralises the decline
observed in linear viewing.
While consumption of linear forms of TV may be
declining, total use of the television set is NOT decreasing.
What’s apparent, from even a cursory look at the data, is
that the TV still remains a central hub within the average
home; even if audiences are becoming increasingly
confident accessing and consuming content across
multiple platforms. However, live TV still dominates.
Ave Daily Hrs x Viewing Activity, Inds
Live Vosdal TS 1-7 TS 8-28 Unmatched
2010 2011 2012 2013 2014 2015 2016 2017
Unmatched activity is growing
fastest among UK’s youngest
The proportion of screen time that BARB are reporting as
unmatched is increasing. However, linear TV remains the
go-to destination for the majority of audiences.
Younger viewers spend the most time with unmatched
forms of content and they have also experienced the most
significant increases since 2013.
Unmatched as a proportion of Total Screen Time 1 (%)
2013 2014 2015 2016 2017
Average TVR, Terrestrials & Unmatched x Year, 2014-16, 16-24’s
For 16-24’s, unmatched viewing
is now larger than all of the
Unsurprisingly, these shifts are most profound among the
UK’s youngest audiences. For 16-24’s unmatched viewing
represents 31% of their total screen time – but put
differently, for this demographic unmatched activity
achieves a higher share of viewing than the entire
2014 2015 2016
1 Total Screen time = All Live + VOSDAL Viewing, catch up 1-7, catch up 8-28 day viewing and unmatched viewing
Ave Daily Mins of Unmatched (All Viewers) x Device 2 , 2016
Games Console TV set TV service Blu Ray/DVD/VHS Other Unmatched viewing occurs
across a variety of platforms.
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 (Unmatched Viewing 3 ) x panellist, 2016
SH H M L SL
Ave Daily Hours x Unmatched Viewing 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.
Total TV Unmatched
Ave Hours Ave Hrs
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%
Devices that constitute the summary groups can be found in the appendix (Page 10).
Excludes those who watched less than a daily average of 3 mins
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
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?
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
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
What can we expect from the future?
Start a conversation!
We would love to hear your personal thoughts on this – please get in touch!
Some exciting things to come!
We’ve just undergone a corporate re-brand and those of you that are eagle eyed will have already spotted our new
logo throughout this report.
We are arranging a launch event in late October to officially unveil the new Digital.i - invites will be issued soon!
We have developed our own bespoke piece of software that allows us to analyse respondent-level viewing data. Using
this tool we have modelled a brand new, innovative segmentation methodology that allows us to create more reliable
We have uncovered crucial insights for major broadcasters by segmenting audiences using this method. More recently
we have been further developing this methodology to better understand respondent-level volatility and what impact
this has on channel viewing. We have some initial findings which suggest the importance of more effectively targeting
Unmatched Attribution Methodology
We are currently developing a model that uses a variety of sources to breakdown what unmatched viewing consists of.
The main purpose of this is to provide estimates on the level of viewing to on demand services such as Netflix and
Amazon Prime and we hope, this will ultimately shed more light on the way in which the TV set as a whole is being
used. These estimates are of crucial importance considering that data on these SVOD services is relatively scarce, and
delivery of project dovetail has been pushed back to March 2018.
- 8 -
Felix Eccleshare, Head of Analytics
2016 Unmatched viewing x Device & custom category (used on page 6)
Device Category Ave Audience (000s) % Viewing
TV TV set 383 27%
Xbox Games Console 194 14%
Playstation Games Console 170 12%
Virgin Tivo TV service 108 8%
Sky PVR TV service 80 6%
Internet STB Other 74 5%
Blu Ray/DVD/VHS Blu Ray/DVD/VHS 74 5%
Unknown Other 55 4%
Freeview PVR TV service 52 4%
PCTV Other 43 3%
Youview (Ceased) TV service 39 3%
Wii Games Console 25 2%
Other STB Other 16 1%
BT Vision TV service 14 1%
Youview BT TV service 13 1%
Freesat PVR TV service 12 1%
Virign + TV service 11 1%
Virgin TV service 10 1%
Youview Talk Talk TV service 9 1%
Other Satellite PVR Other 8 1%
Freeview STB TV service 8 1%
Other Other 7 1%
Freesat STB TV service 6 0%
Game Other Other 4 0%
Youview Unbranded TV service 3 0%
Now TV Other 2 0%
Sky STB TV service 2 0%
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Author: Felix Eccleshare