Please see the keynote video here: https://vimeo.com/68847912
Video advertising avoidance and visual disengagement with TV ads has become one of the most serious problems that the modern advertising industry faces.
This, coupled with the exponential growth in video consumption online, is the main reason that advertisers are increasingly migrating TV money online. However, as performance of traditional online advertising has declined, and in a world where users are increasingly consuming content based on social recommendations and peer-to-peer networks; leading brands have increasingly adopted pull driven mechanics for delivering effective brand and product messaging
Brands are now developing “snackable” video content (usually called viral ads, branded content or branded entertainment) for online consumption. The basic idea driving this development is that while users may actively seek to avoid classic ad formats, they are showing an increasing and simultaneous willingness to connect with some brands voluntarily.
As for the recipe for success in video advertising - the multi-billion dollar question quickly arises – what are the rules of creation in this new pull driven content landscape? What works – and why?
Ekman and Friesen (1978) proposed the Facial Actions Coding System (FACS) to identify basic emotions from facial expressions. FACS has proven very useful in marketing contexts to expose users direct emotional response - but for many years it has relied on manual interpretation, which has made it error prone, slow and expensive. However with the growth of technology, we can now apply FACS testing to thousands of people across the world in 48 hours, using opt-in user panels and normal webcams.
Using this new technology platform Be On and RealEyes have tested over 300 different ads, from standard 30 second TV ads to the most successful viral ads, across a total audience of over 100,000 people from 25 countries. So come and join Be On for a world first view into what emotions have taught them about how users respond to various forms of video ads and content, at scale, and across the world.
2. 20,000
unique videos
since 2005
2005 2013
Viral Video
2005
The “Social
Media”
Playbook
2008
Branded
Entertainment
2010
Generation
Social
2012
Be Emotive
2013
4. • Content
2005 2013
5
4
3
2
2009
Global digital content created and shared
Source: KPCB, YouTube
YouTube hours of video uploaded per minute
100
75
50
25
ZB Hrs
100 hrs
of video uploaded
to YouTube each
minute
8 ZB
of data per year
will be created
by 2015
5. • Content
< 100 > 1m
YouTube videos by number of views
Source: Business Insider, Businessweek
%
30
20
10
29.6%
11.4%
0.3%
1k – 2.5k 10k – 100k
2.7%
0.0001%
probability user
will view your
content
6. • Choice
2000 2012
1,000
800
600
400
200
2008
Global internet connected device shipments
Source: BI intelligence, Microsoft
Units
(millions)
2004
Smart
phones
Tablets
Personal
computers
Wearables
47%
multi-task on
multiple
screens
7. • Choice
Share of device page traffic on a typical work day
Mobiles
brighten the
commute
PCs dominate
working hours
Tablets popular
at night
Source: comScore, Ericsson
60%
of consumers
watch video
on-demand
weekly
9. • Consumption
#
##
Source: Ericsson, KPCB, Microsoft
2/3
Use multiple
screens
150
times per day
a user checks
their smart
phone
1.6bn
Mobile
broadband
connections
(43% y/y)
10. • Connection
Source: KPCB, YouTube, Facebook
0 100
Facebook
YouTube
Twitter
Google+
LinkedIn
Pinterest
MySpace
Instagram
Tumblr
Foursquare
80
Which social media do you use?
%604020
2011 v 2012
1bn
unique users
visit YouTube
monthly
1.1bn
global active
Facebook users
530m
photos
uploaded and
shared daily
15. 4.5x
higher purchase
uplift
Richer media vs simple
Brand
Favourability
Purchase
Intent
2.5
2.0
1.5
1.0
0.5
Aided Brand
Awareness
Delta
(exposed
minus
control)
Richer media delivers on branding goals
Source: Google Doubleclick
15x
higher brand
favourability
16. Choice over interruption
Video format
preference?
Premium quality feel?
More intrusive
format?
Positive brand
sentiment?
Added most value?
Pre-roll
Positive emotion
towards format?
Format related to
search/activity?
Native
Source: Be On internal research
17. 75%
higher engagement
amongst users who
choose to watch
video content
vs
Choice
Interruption
Source: YouTube, Neilsen, Reelseo
Native
Pre-roll
82%
Higher brand lift
generated by
native
ads, compared
to pre-roll
20%
higher conversion
with user initiated
videos
18. Source: Gunn Report 1992-1995, IPA
Les Binet & Peter
Field, Marketing in the
Era of
Accountability, 2007
Emotional advertising
campaigns are more
effective and more
profitable than rational
campaigns - even in
'rational' categories…
“
”
11x
more efficient
in market share
growth
The Gunn Report
32. Consumers love emotional content...
3xhigher average
view to end %
8xhigher click
through rate
20xbetter in
converting views
into social
actions
100xQuicker in
attracting
viewers
Source: Be On research
Reduced price sensitivity and creates strong sense of brand differentiation.
DEMO
Data from LG report
Point:1. Standard doesn’t work2. Even modest emotional differences make exponential difference in breaking through
Emotionally stronger ads get3x higher average view to end %8x higher click through rate20x better in converting views into social actions100x quicker in attracting viewers.Source: Be On and Realeyes research based on 400 videos study, 2013