Leveraging Emotions in Media Planning

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SUPPORTED BY:
Emotions in Media Planning &
Creative Testing
Mihkel Jäätma
CEO Realeyes

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We measure
emotions to guide
better marketing
decisions
Source: Prof Gerald Zaltman, Harvard Business School,
'How Customers Think’ (p40.), 2011
Over 90% of human behaviour
is driven by emotions

Emotionally intelligent Creative Testing and Media Planning
have huge ROI potential
1.[TRUE or FALSE] Marketing Mix Models
can help create your marketing strategy
and write your marketing plan.
True
False
Creative Testing Media Planning Result
A brand can expect that even
in their best categories 33%
of current video ads have
low/no impact
Investing in those mixed ads
yields 9% lift in sales today
Remove low potential videos
from the portfolio and inform
edits where potential exists
Skew spend against higher
performing videos and bias
toward most engaged audience
segments
Combining both effects would
yield a 4% lift in sales
This is $80m in extra
revenue for a client with $10b
in total revenue, of which 20%
are video-driven, for no
increase in media spend
Improved sales impact from 9% to 10.9% from 10.9% to 13.1%
Sales increase +1.9% +2.1% +4.0%

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© The Advertising Research Foundation
Optimising video performance at every stage of the campaign
Media Agencies
Media Platforms
Brands
Research Agencies
Programmatic
Platforms
Media Platforms
Programmatic
Platforms

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Real audience responses have been difficult to gather
Slow & expensive
Not scalable
Weak links to creative
and media decisions

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Webcam-based emotion measurement makes it easy
Fast & affordable
Scalable
Linked to real business
outcomes

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Completing our understanding of consumer response
Genuine emotions
3D modelling enables natural, free
head movement. Technology tackles
‘in the wild’ occlusions such as
glasses, beards etc.
Cultural differences addressed
with country norms
Beyond basic six
Standard emotions plus engagement,
net positive/negative, headpose

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EmotionAll® score
The EmotionAll® score is a 1 to
10 performance score, relative
to the entire emotion database
of over 7,000 videos.
Get an instant snapshot of your
video's performance to predict social
media activity, guide media decisions
and inform your campaign strategy.

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…based on four building blocks
Attract
Can you grab people’s
attention? Measured
by peak surprise
during the early stage
of the video.
Retain
Can you keep it?
Measured by peak
happiness after the
early stage of the
video.
Impact
Can you leave
something behind?
Measured by Daniel
Kahneman’s peak-end
rule (peak + end) / 2
Engage
Can you build
momentum? Measured
by peak engagement
anywhere in the
video.

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EmotionAll® is linked to social media performance
Assess earned
media potential
Inform edits to
improve potential
Inform proceed/
prioritise decisions

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Use it to guide your media plan
Select the best
performer and refine
targeting against
most engaged
audience segment.
Target young women
Use Video A

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Let’s see how it actually works…

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Case Study: Media Planning
Coach
Potato
City
Rivalry
Mission
Statement
Town
Soccer
Star 4
4
5
6
8
5 different video concepts across 3
markets of UK, Spain, and Germany
resulted in a range of EmotionAll® scores.
By digging deeper we could identify
which demographic would be most
effective despite having a low score.

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Different videos appeal to different markets
4 4 4 6
4 4 5 5
5 7 4 5
6 6 7 6Coach
Potato
City
Rivalry
Mission
Statement
Town
Soccer
Star
8 8 8 8
% Engagement / EmotionAll® Scores

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…and different genders
Town 4 4 5 5
Town: Global comparison Engagement > Gender
Target women only
A low EmotionAll® score due to a
lack of engagement from men. It’s
clear that women stay significantly
more engaged compared to men,
who’s engagement drops
significantly from 40 seconds - this
point almost divides the sexes.

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…or ages
Town: Engagement > Germany > Women > Age
Target older German women
With only two minor points
where engagement reached
similar was the same, the older
age group (33-49yrs) were far
more engaged than the younger
group (16-32yrs) throughout.
Town 4 4 5 5

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Women younger
Women older
Town: Engagement > Spain > Women > Age
Target younger Spanish women
The younger female age group
responds better due to two scenes
which significantly polarises
opinions between the age groups.
The second most significant point of
difference happens to be when the
brand is revealed.
Town 4 4 5 5

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Give each video every chance to succeed
Place higher performers in sharing
environments with the lower scores
in paid environments for
guaranteed views.
Based on Realeyes analysis of 2083 YouTube videos
and 371,245 video views in March 2015.

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Case Study: Media Planning
Video A (0:30)
Video C (0:15)
Video B (0:30)
Video A (0:30)
Video D (1:56)
The Test
2 Markets (UK & AUS)
300 Viewers per video
Men & Women
Age 35 - 65 (UK);
35 - 50 (AUS)
Monday
Assets supplied
Wednesday
Results available
Thursday
Results presented
Monday
Optimised campaign
launched
Recommended Media Actions
In UK:
• prioritise videos A & B
• bias all videos towards 50+
• bias A & B toward women
In AUS:
• prioritise videos A & C, with focus on Mums
• bias video A towards men & women 43-50
• bias videos C & D towards women 35-42
• video B to Mums only

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Performance Summary: AUS
6
6
good moderate weakstrong
Percentile 55th
3
3
22nd
5
5
42nd
5
4
40th
Men
6 5 6 6Women
5 3 7 735-42
6 5 4 543-50
8 6 8 6Mums
EA® Score
• EmotionAll® scores show Video A
above average, with good sharing
potential.
• Videos C and D are moderate and
Video B is low.
• Video B does especially badly
amongst men and 35-42 year olds
• Mums are driving performance for
both videos A and C

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Channel Split – Video A vs Video C
Video AVideo C
Video A has good earned media
potential. Optimal split shows
capability to bias toward social
media channels promoting
more elective viewing, like
sponsored stories (Facebook),
promoted Tweets (Twitter) etc.
Video C has moderate earned
media potential. Optimal split
biased toward paid channels
with less ‘elective viewing’
options, like pre-roll and TV.

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Case Study: informing Mediacom planning processes
The Test
1 Market (AUS)
300 viewers per video
Men & Women
Age 18 – 65
Videos B and D scored higher, so will rely
more heavily on a mass reach model on
Facebook & pre-roll. Due to the greater
EmotionAll scores, they have higher potential
to drive earned value across social media.
Videos A and C will be aimed at custom
audience groups based on those who reacted
in a more positive way, distributing to those
people only and pulling back on spend.
Actions Taken
Video C (0:55)
Video D (1:55)
Video A (0:45)
Video B (0:50)

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4
Performance Summary: AUS
4
6
good moderate weakstrong
Percentile 31st
8
6
83rd
5
6
47th
7
8
76th
Men
4 9 6 6Women
6 3 6 718-29
7 530-49
3 9 7 850+
EA® Score
• Videos B & D have excellent viral
potential.
• The success of Video B is driven by
women & older participants.
• 50+ are a strong segment in all
cases bar Video A.
7
7

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Case Study: emotion targeting via programmatic platforms
Video A (0:30)
Video B (0:30)
The Test
1 Market (CZ)
300 viewers per video
Men & Women
Age 18 – 65
Test ran in 4 days
before actual campaign
launch
Cookie-matched
emotion responses
linked to Xaxis audience
segments
Video B - Sofa
Target
• Females, <30
• Hobbyists
• Health_Conscious
• House_Proud
Avoid
• Males, Ages 30-49
• Job_Hunters
• Business_Focussed
• Science_Followers
Target
• Females, 30+
• Travellers
• Science_Followers
• IndexSeg (website visitors)
Avoid
• Males, <30
• Financially_Savvy
• Job_Hunters,
• Foodies
Video A - Table
Recommended Actions
Video B - Sofa
(0:15)
Video A - Table
(0:15)

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Audience Targeting - Demographics
High levels of both any emotional
expression (Engagement) and net
positivity (Valence) are priority
segments.
Video A - Table:
o Target: Females, Ages 30 or
over
o Avoid: Males, Ages 29 or less
Video B - Sofa:
o Target: Females, Ages 29 or
less
o Avoid: Males, Ages 30-49

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Audience Targeting – Behavioural Segments
By using tracking pixels, Realeyes links
emotion responses to any Xaxis audience
segment.
High levels of both any emotional
expression (Engagement) and net positivity
(Valence) are priority segments.
Video A - Table:
o Target: Travellers, Science_Followers,
Techies, and IndexSeg (website visitors)
o Avoid: Financially_Savvy, Job_Hunters,
Foodies, and
Style_and_Fashion_Enthusiasts
Video B - Sofa:
o Target: Hobbyists, Health_Conscious,
and House_Proud
o Avoid: Job_Hunters, Business_Focussed,
and Science_Followers

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Audience Targeting – Results
The most important client target
group, IndexSeg (website
visitors), responded very well to
Video A with high levels of
Engagement and Valence.
That favorable emotional
response was mirrored in actual
results of the campaign
performance with above average
click-through rate (CTR) for the
IndexSeg audience segment that
was profiled
0.60%
0.53%
Video A Video B
CTR by Creative-
IndexSeg
Average CTR
for the
campaign =
0.20%

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Predict Sales Lift with 75% Accuracy
• Validation project with
Mars – co-created the
largest emotions database
linked to sales
• Could guide smarter media
spend decisions at scale,
by identifying which ads
will sell and which won’t.

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The Future: Programmatic Ad Recommendation Engine

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