Beyond Black and White: The True Color of Brand Safety
Over the past few years, a lot of brand safety issues
have surfaced that have led marketers to review their brand safety measures.
The current coronavirus crisis has intensified the brand safety woes of
marketers, as most of the brands don't want ad adjacency to the content dealing
with morbidity and mortality.
Common brand safety methods used by marketers include
blacklisting and whitelisting. Blacklisting involves avoiding placement of ads
against content containing one or more blocked keywords. In case of video
content, a blocked keyword is searched in topic, title, description and
metadata.
Keyword-based blacklisting method is in reality not
that effective as it seems to be. It is marred by under- and over-blocking of
content. Research shows that because of the use of keyword blacklists, more
than half of the safe stories published on the major news platforms are being
incorrectly tagged as brand unsafe.
Keyword-based blacklisting method can lead to blocking
of completely innocuous content. This is because it fails to comprehend the nuances
in context, i.e. it is unable to understand the true context in which a keyword
is used. For example, if "alcohol" is the blocked keyword, then the
blacklisting method will not only tag a video featuring drunk and driving as
unsafe, but will also tag a video featuring a recipe in which alcohol has been
used as one of the ingredients, as unsafe.
Another problem with blacklisting is that universal
blacklists cannot be created. They have to be regularly updated and modified
according to the brands' requirements, current happenings and events, latest
news, countries, languages and culture. There is also a requirement to tweak
blacklists regularly on the basis of current safe content consumption patterns
of consumers, so that increased reach for the advertising campaigns can be
achieved. Overall, keyword-based blacklisting method is quite cumbersome to
implement as it needs a lot of fine-tuning. With this method, content under-
and over-blocking is a common problem, and this hinders marketers in getting
optimal results from their advertising campaigns.
A whitelist enlists content that has been labeled as
safe for ads to be placed against it. A whitelist provides a safe and trusted
environment to brands to advertise within. Curating a whitelist for advertising
on a video platform, for example for YouTube advertising, involves tagging
unsafe content at the keyword, topic, video and channel levels. Video-level
tagging helps brands to filter out unsafe videos from an otherwise safe
channel; brands do not have to blacklist the entire channel just because of one
or few unsafe videos.
Again, like keyword blacklists, whitelists need to be
regularly updated, otherwise the campaigns will not witness an increase in
reach, and brands will miss newer safe and engaging content for their ads; ads
will keep displaying against the same video content enlisted in the static
whitelist.
Creation of whitelists is not an easy process; it
requires a lot of curation by marketers, and is time-consuming and expensive.
As the whitelisting method limits the number of videos against which ads can be
placed, marketers are unable to take the full advantage of the true potential
of huge video hosting platforms like YouTube. The campaign's reach gets reduced
and the right audience does not get fully targeted.
The above-mentioned brand safety methods provide only
suboptimal brand safety and have significant limitations. A highly effective
way of ensuring brand suitability and safety is provided by contextual brand
safety method that makes use of AI and computer vision. AI-powered brand safety
platforms that deploy computer vision technology, provide high degree of
context relevance unmatched by keyword-based methods.
Computer vision can accurately detect contexts in
videos such as faces, objects, logos, on-screen text, emotions, scenes and
activities. Thus, it can effectively detect unsafe or harmful contexts in
videos without the risk of under- and over-blocking of content.
Amid the coronavirus pandemic, computer vision-powered
brand safety platforms enable brands to selectively block ads against
mortality-related coronavirus content, while allowing ad placement against
positive coronavirus content. Thus, brands can safely capitalize on the news
content; this is not possible with keyword-blacklists that fail to understand
the true context in which the keyword "coronavirus" is being used.
By using AI-based contextual brand safety method,
marketers can not only effectively block ad placement against recognized unsafe
categories, but can also custom define unsuitable contexts that are unique to a
brand. This helps them provide a fully suitable environment to brands for
advertising.
Computer vision enables marketers to go beyond
blacklists and whitelists in order to achieve brand safety in its true color.
