Mining Unmatched Viewing_A Digital-i Case Study

feccleshare

We've put together this brief, top-line summary of 'unmatched viewing' and its potential as a resource to furthering an understanding of how our TV habits are changing.

Enjoy!

- Felix Eccleshare
Head of Analytics, Digital-i

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.

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 (Unmatched Viewing 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 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.

Cohort

Size of

Sample

%

Sample

%

Viewing

Total TV Unmatched

Ave Hours Ave Hrs

%

Unmatched

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/Digital-i

More magazines by this user