Why you need to update your brand safety protocols

AI-enabled brand safety strategies are the way forward, writes Silverbullet and 4D's Marco Godina

For marketers, brand safety and suitability has become a major focus. Brands need to be wary of what they do and say, but also where their ads end up. To keep track of where their content is shown, brands are pretty well clued up on implementing the tools to place their ads in "brand safe" contexts, but it's becoming ever more prevalent that a ‘one size fits all’ solution isn’t enough for today’s climate. 

When it comes to brand safety and flagging harmful environments, marketers should look to AI-based systems. AI helps brands, publishers, and platforms focus on nuanced brand safe and suitable environments, giving them a deeper understanding of the quality, safety and credibility of any piece of content on the web.

The recent 4D (a division of Silverbullet) and Factmata whitepaper, 'Is brand safety enough?', explores how marketing now requires nuanced brand suitability beyond the traditional scope of existing brand safety providers.

Factmata and Silverbullet found unsuitable content flagged by Factmata, but missed by existing brand safety vendors, equates to 0.71% of total spend. With global programmatic spend in 2020 reaching US$126.5 billion, advertisers lost around $898 million on content considered unsuitable or unsafe for brands just in the last year.

All context is not created equal

The vast majority of traditional contextual solutions were built at the dawn of programmatic advertising, and many have not kept up to speed with the nuances in how harmful online content is framed, worded, and produced. 

Dated algorithms rely on keyword targeting and blocking, which alone do not protect a brand entirely. And, with restrictions around the walled gardens making it even more difficult to manage brand safety in today's polarised environment, businesses are rightfully searching for new solutions.
 

Brand safety in today's climate has never been more important

In 2020, the unforeseen global pandemic brought brand safety and suitability challenges into sharp focus for many marketers.

As publishers across the globe unveiled the impact of COVID-19 to their readers, they simultaneously began to lose revenue from a broad spectrum of advertisers who were blocking pages inadvertently.

This is because the fundamental methodology of many brand safety tools relies simply on the blocking of keywords. For example, if an advertiser decided to add COVID-19 to their blocklist in order to avoid appearing next to negative news stories, they would effectively be blocking almost all of the leading global news sites of the time.

With a recent Ofcom report revealing that nearly nine out of 10 adult internet users turn to traditional media as a source of COVID-19 information, advertisers could be missing out on an abundance of audience engagement opportunities.

This is backed by a study commissioned by CHEQ and Digiday, which reports nearly two-thirds of advertisers stating brand safety tools are not fit for purpose. Around 92% of marketers also stated they would forgo the use of brand safety tools if they were not achieving adequate reach, while 99% are seeking more customised tools to ensure safety, without sacrificing reach. Brands are at significant risk of both long and short term consequences as a result of forgoing brand safety for reach.

The open web is a difficult place to navigate, and brand safety has fast become the number one focus for marketers moving forward in this politically and economically volatile environment. The good news is, brand safety and reach do not need to be mutually exclusive.

Marketers need to expand their toolkit and implement new tools and techniques into their brand safety etiquette to make sure they don't fall behind. Modern marketers who want to thrive in the new marketing age need to be aware of nuance and true context to identify the right moment and the right environment in order to align with the right message, by using the latest AI-enabled contextual outcome engine.