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Let’s See ... Can Computer Vision Affect Brand Performance?
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Let’s
See ... Can Computer Vision Affect Brand
Performance?
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Mohammad Shihadah
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January 9, 2017
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Computer vision lets
marketers add meaning
and categorisation to
large amounts of video
footage.
In the heyday of TV commercial advertising of the 1960s,
marketing decisions involved sending out surveys and shelling out
for primetime ad placements. The digital age has given us big data
Users don’t just
interact with videos,
increasingly they are
capturing and
uploading their own
user-generated content
via social media.
http://www.cmo.com/features/articles/2017/1/6/four-ways-computer-vision-and-ai-will-hack-brand-performance.html#gs.pjIFtBI[3/7/2017 12:08:56 PM]
Let’s See ... Can Computer Vision Affect Brand Performance?
and made it far easier for brands to produce engaging, high-quality
videos delivered cross-device. However, when it comes to
understanding visual content and its impact on audiences, brand
insight has failed to keep up.
The marketing ecosystem is multi-channel, multi-platform, crossdevice, and technology-driven. It’s chaotic, and the industry is still
trying to make sense of all this big data and tie it back to the big
picture. As video takes over the online world, marketers are faced
with ever-increasing repositories of visual data, and they’re using
text-based tools to interpret this content. It’s like trying to
understand the complexities of deep space with a magnifying glass.
There is a huge buzz in the industry about how artificial intelligence
(AI)—or more specifically computer vision—can change this.
From accelerating programmatic video capabilities, to uncovering
semantics in Facebook uploads and analysing human emotions,
machines can detect visual triggers invisible to the human eye. And,
the reality is, today’s tools barely scratch the surface. So how is
computer vision offering new insights and capabilities to hack real
brand performance?
1. Hyper-Personalised Ad Selection Through Video
Fingerprinting
Computer vision lets marketers add meaning and categorisation to
large amounts of video footage. Through facial, object, audio, and
speech recognition, machine tools automatically tag every frame
with a huge amount of metadata. This could be anything, from a
model’s hair color, to their gender, facial expressions, the time of
day, or any object in a single shot.
Advanced forms of
emotive computing,
known as affective
computing, are also
able to detect hidden
human responses.
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This process of transcribing video, adding semantics and defined
categories, can be used to catalogue brand video, enabling hyperpersonalised ad delivery in real time. A combination of this AI and
programmatic creative changes allowed Unilever’s Axe to deliver
100,000 different versions of its “Romeo Reboot” campaign,
personalised to user tastes. Axe partnered with research firm
Box1824 to target different versions of the video to audience
profiles, using known insights such as known musical tastes or
purchase history to select the most appropriate content.
In this way, brands use computer vision to create customised ad
campaigns that will resonate with individuals. Machine learning can
also help to identify larger trends and optimise campaign delivery
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Let’s See ... Can Computer Vision Affect Brand Performance?
over time. Marketers can apply this technology to ad placements,
evaluating the context, from static pages, text, and images to full TV
shows to select appropriate ads in real time. So when Cristiano
Ronaldo scores a goal in the game, at half-time you can make sure
to place the Nike ad with Ronaldo in it.
2. Extract Audience Insights From UGC
Cataloguing visual brand content to power personalised ad delivery
is only the beginning. Users don’t just interact with videos,
increasingly they are capturing and uploading their own usergenerated content (UGC) via social media.
On platforms such as Facebook, WhatsApp, and Snapchat, users
upload and share over 1.8 billion photos a day. And as the digital
world turns to video, on YouTube alone users are uploading an
average of 300 hours of video every minute. Adding semantics to a
growing mass of UGC means brands can gather further audience
insights, helping them to derive personal characteristics, tastes, and
experiences.
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Visual intelligence platform GumGum uses computer vision to scan
visual social media content, providing marketing teams with
actionable audience information. Using this information they helped
L’Oreal’s Feria to target customised ads to women with different
hair colours. With techniques like this, brands are able to search
social media for visual content that relates to their product or
offering. They can respond with campaigns catered to intelligent
audience insights, or even augment UGC with branded additions.
3. Synchronised Advertising And Improved TV Attribution
Scheduling digital ad delivery in sync with TV commercials can
now be done through computer automation. Computer vision keeps
an eye out for the commercial and triggers a complementary ad on
mobile or desktop. According to Accenture, 87% of consumers use
a second screen. AI provides a way for marketers to take advantage
of these habits, linking linear viewership to the digital space,
offering tailored calls to action in sync with TV content or paid
commercials.
This also means marketers can schedule ads and launch
conversations during their competitors’ commercials, or even just in
tune with relevant TV content. This means an individual watching a
travel documentary about Southeast Asia on TV, for example, could
be served a tailored video campaign on their mobile device offering
http://www.cmo.com/features/articles/2017/1/6/four-ways-computer-vision-and-ai-will-hack-brand-performance.html#gs.pjIFtBI[3/7/2017 12:08:56 PM]
Let’s See ... Can Computer Vision Affect Brand Performance?
a discount flight to the beach paradise of Koh Phi Phi. In the future,
employing computer vision to gather user insights means it could
even take insights from Instagram posts of users’ last vacation.
And real-time data that ties this all together gives marketers
analytics on attribution, helping them to measure downloads and
page visits in correlation with TV ad spots. As attribution windows
continue to shrink, computer vision will become essential to
accurate analytics across multiple platforms. Furthermore, brand
identification in scenes means businesses can measure earned media
and also ensure copyright protection.
4. Sentiment Analysis Via Emotive Computing
Today, marketers are also able to take visual data from the other
side of the screen, with the use of cameras to measure emotional
responses or identify a consumer’s surroundings, to deliver more
relevant ads. Agencies such as Omnicom and WPP’s MediaCom are
making strides with “feature extraction” and sentiment analysis,
measuring audience reactions when exposed to different ads.
M&C Saatchi in London used this form of visual analytics to create
the world’s first outdoor AI campaign. Using a camera to capture a
viewer’s facial expressions, M&C ran a series of AI posters at bus
stops, switching the layout, copy, font, and images through a
“genetic algorithm” using a person’s visible emotions, for instance
if they looked happy or sad, to build a tailored creative.
Advanced forms of emotive computing, known as affective
computing, are also able to detect hidden human responses. For
example, software startup NuraLogix uses a process called
Transdermal Optical Imaging, which uses cameras to measure blood
flow information and determine emotional responses.
Of course, using personal data in this way is controversial, and there
are various barriers to brand adoption. Collecting biometric data
such as “faceprints” violates privacy acts, both Google and
Facebook currently face lawsuits regarding biometric data. While
facial recognition is possible on Android, Google does not currently
allow this on the MyGlass app store.
According to U.S. media company PSFK, Apple also bars facial
recognition via the camera on its phones, relegating users who want
to experience this technology to the desktop. However, through its
purchase of AI startup Emotient, the tech giant has furthered its own
advances in emotional detection. What Apple plans to do with these
http://www.cmo.com/features/articles/2017/1/6/four-ways-computer-vision-and-ai-will-hack-brand-performance.html#gs.pjIFtBI[3/7/2017 12:08:56 PM]
Let’s See ... Can Computer Vision Affect Brand Performance?
new-found capabilities still remains to be seen.
Nevertheless, computer vision continues to advance rapidly.
Artificially intelligent sight has overtaken human vision and
perception. The effect of this is that, as video becomes the digital
norm, vast quantities of branded video, UGC, and even consumer
emotional responses can be translated, communicating meaning in
real time, and making this all searchable for the future.
When paired with big data and response measurements, brands infer
valuable audience insights, helping them to co-ordinate marketing
activities to create engaging campaigns that work in synergy. As the
visual online world gains momentum, AI technology will be our
eyes. Computer vision will guide marketers through the noise of all
this video content, helping to uncover new intelligence that will
transform brand performance insights, light years from the
guesswork of old TV advertising.
Mohammad Shihadah is the CEO of real-time
video detection company IDenTV, which he
founded five years ago. He was previously CEO
of AppTek. Find out more about him here.
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http://www.cmo.com/features/articles/2017/1/6/four-ways-computer-vision-and-ai-will-hack-brand-performance.html#gs.pjIFtBI[3/7/2017 12:08:56 PM]
Let’s See ... Can Computer Vision Affect Brand Performance?
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