Is Computer Vision the Sure-Shot Solution to
Brand Safety Woes?
Today, brands are not only concerned about the return
on investment (ROI) when they run an advertising campaign, but also about where
their ads are appearing. They don’t want their ads to be placed against any
sort of harmful, unsafe or inappropriate content, as any single placement of an
ad against such content can critically damage brand image. The scale and speed at
which the programmatic advertising works has made it quite difficult for brands
to ensure brand safety.
Brand safety has become a major concern since few
years back, when one after another disastrous ad placement issues came into
light. It was found that some famous brands were unknowingly supporting
terrorism by ad placement against hate videos on YouTube. Another finding that
shook the video advertising world was that ads of some of the biggest
brands were seen running against the videos of child exploitation.
Brand safety poses a serious challenge to brands. The
placement of ads against unsafe video content not only puts a brand’s
reputation at stake, but it also leads to loss of consumers’ trust in the
brand. This, in turn, leads to brand avoidance and decrease in sales.
There are some brand safety measures that
advertisers have been using, but these methods are quite far from being fully
reliable and effective. A keyword blacklist details words and phrases that describe
content against which a brand does not want to have its ads placed. But this keyword-based
brand safety method does not take into account nuances and context, thereby letting
in some unsafe placements or blocking some safe placements.
Using whitelisted channels limits the reach that a
brand can achieve through social media platforms. Another reason that makes whitelisted
channels a less sought-after option is that this method is quite expensive.
Using manual methods for filtering out unsafe content
is not feasible keeping in view the enormous volume of video content that is
uploaded on an hourly basis.
By bringing in context to advertising, artificial
intelligence offers a remedy to brand safety woes. Although artificialintelligence advertising solutions that use machine learning (ML), natural
language processing (NLP) and semantic analysis, work by understanding the
context of a webpage and automatically regarding content as unsafe or appropriate,
they fail to effectively ensure brand safety, especially, in video advertising.
The true remedy to brand safety woes is provided by AI
advertising technology that makes use of computer vision. Computer vision enables
detection of contexts in video with high accuracy, thus allowing advertisers to
display context-relevant in-video ads in a brand safe manner.
By using computer vision, computers are able to
see, identify and process images and videos just like human beings do or even
better than that. Computer vision based in-video context detection technology
can easily and accurately identify faces, objects, emotions, logos, activities
and scenes in videos. This enables advertisers to display in-video ads that are
fully in line with the video content that a user is watching, while strictly
avoiding ad placement against any content that has been regarded inappropriate
or unsafe by a brand.
The computer vision based in-video context
detection technology provides double benefits – firstly, it displays ads
against the relevant video content, thus boosting the chances of a user’s
engagement with the ad, and secondly, it effectively avoids ad placement
against brand unsafe content. With computer vision, brands can really play safe
when it comes to displaying in-video ads on online video platforms.
