Using Computer Vision for Effective Visual Content Strategy
Visual formats such as images and videos are embraced
by people over just a plain piece of text. Images and video enable brands to bring
life to their messages, making consumers better understand their products and
services. For brands, an effective and strong visual content strategy drives
engagement and sales.
Research shows that brands are using visual formats much
more on their own platforms and their social media pages for conveying messages
to consumers, but less frequently in display ads.
But what is causing marketers to give less preference
to display ads when it comes to using highly effective content formats - images
and videos - for communication with the consumers? Research shows that using their
own platforms allow them to exercise more control over their visual content in
comparison to putting it out on the uncontrolled internet in the form of ads.
There is enormous competition and it is hard for marketers to ensure that they
are reaching their targets and drawing user engagement.
Another reason that marketers cite is of brand safety.
Enormous amount of content is uploaded on the internet on daily basis and marketers
have no idea against what content their ads would get displayed. On their own
platforms, whole content is under their control.
Research shows that when it comes to using visual
content for increasing user engagement, raising brand awareness and generating
revenue, marketers face the following issues – insufficient viewability,
contextual irrelevance, and ineffective demographic targeting. Data privacy
regulations such as the General Data Protection Regulation (GDPR) and the
California Consumer Privacy Act (CCPA), along with the gradual phasing-out of
third-party cookies in Chrome by Google, have made practices like demographic
targeting all the more difficult.
The problems that hinder the use of visual formats by
marketers in display advertising, namely – insufficient control over ad
placement, insufficient user engagement, brand unsafe environment and data
privacy laws – can be resolved through contextual targeting.
Contextual targeting involves placement of an ad against
the content that is relevant to the ad, i.e. the ad is in line with the content
that the user is currently interested in. Contextually targeted ads readily capture
the attention of users and increase their chances of viewing or clicking them,
as it is likely that users are already interested in the products or services
being advertised.
Keywords-based contextual advertising often delivers
sub-optimal results as keywords fail to fully reflect the user’s current state
of mind, while AI-powered solutions that utilize technologies such as NLP and
semantic analysis fail to understand nuanced contexts and complex relationships
that exist between words.
The true contextual targeting can only be achieved
through computer vision. By leveraging computer vision, marketers can take
control of their visual content strategy and use visual formats to run highly
effective video advertising campaigns, without worrying about data privacy and
brand safety issues.
Computer vision is an advanced technology that enables
computers to understand images and videos. Computer vision uses deep learning
to make computers learn how to detect patterns in images and streaming videos.
Computer vision powered contextual advertising
technology works by accurately detecting contexts in streaming videos in order
to display in-video ads that are in line with what the user is actively
engaging with. Any content that is unsafe or unsuitable is contextually
filtered out to provide true brand suitability.
Computer vision enables marketers to embrace
contextual targeting and fully utilize their visual content for achieving their
marketing goals.
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