How AI can deliver targeted ads while ensuring brand safety

Soon after, other brands like Unilever, Verizon, Adidas, and Microsoft followed suit along with over 1,000 other advertisers, with news sites calling it the largest advertiser boycott in the website’s history.

The renewed attention on fake news and hate speech on the platform has put into sharp relief how brand risk is a tangible threat for marketers.

And this has spurred some fundamental change in Facebook’s model. The company has since committed to hiring a civil rights executive, and to labelling ‘newsworthy’ posts from politicians that break its rules against inciting violence or voter suppression. The recent happenings also highlight the pain points of digital marketers today.

Facebook is undoubtedly a giant, though countries such as China and Japan are not adopting the platform as widely. It is still clearly valued for its micro targeting capabilities that allow marketers to reach audiences based on interests and demographics more precisely and efficiently.

Technically speaking, precision targeting is not a concept unique to Facebook. Just as Facebook’s algorithms can help businesses maximise their ad spend by targeting the most relevant consumers, advertisers can achieve similar metrics programmatically.

There is a misconception that only Facebook has the wealth of data required for optimal targeting. There is abundant data in the digital space, it is the ability to make sense of them and use them in a constructive manner that is challenging. One way to overcome this is the adoption of AI to rapidly process and detect patterns, correlations and anomalies to help advertisers better understand their audiences and reach them not only at the right place with the right message but also at the right time.

Still, there is a persistent pitfall that hounds digital marketers when it comes to programmatic advertising: brand safety and ad fraud.

Programmatic buys have undoubtedly changed the game with automated media buying, providing immediate access to a wide range of inventory in a fraction of the time and cost. But while removing the human element has made the ad-buying process more efficient, it has understandably led to gaps in inventory quality: ads are often reaching bots instead of people, and as the YouTube brand safety scandal has demonstrated, there is a chance that an ad could even be appearing beside offensive content.

Yet as blackisting, white listing tools and other security solutions evolve and become smarter, so do bots. Ad fraudsters have become more creative in developing bots that can mimic human activity, breezing through bot checks. It’s a cycle that goes on and on.

It is simply not humanly possible to be on top of these issues all the time; delivering targeted ads while ensuring ads are placed in brand safe environments and keeping up with fraudsters. Lucky for advertisers today though, there are technologies available to help them achieve efficiencies and minimise risks in the digital ad space.

How artificial intelligence can help deliver targeted ads while ensuring brand safety

As the world reels from the effects of the coronavirus crisis and other world events, more brands are turning to digital platforms and as such, seeking greater accountability for their media spend.

AI systems enable marketers to look beyond using just the usual social media platforms. They not only offer precision targeting of ads, but can also perform real time volume data processing to self adjust and optimise campaigns. This ability to process huge amounts of data including real time reading and assessment of sites for content and context, can also provide a more enhanced safety net to the entire targeting and ad-buying process.

Some other layers of protection include:

Third-party brand-safety blacklists

Dynamically updating blacklists from third party specialty vendors who constantly identify sites that contain inappropriate content.

Category context

Utilising web-crawlers to ensure ads remain within relevant categories. This is done for targeting purposes, but also yields additional brand-safety benefits in addition to the use of blacklists.

Scanning individual landing pages

This involves programmatically analysing any individual landing page, where an ad shows up. This dramatically raises targeting accuracy and reduces the risks of ads appearing alongside related but undesirable content. For example, a lot of media has focused on the dangers of travel in and out of high-risk Covid-19 territories. Ironically, those same pages are full of airline and travel content and thus are advertising travel deals on those very pages. This is a prime example of a situation where advertisers may have brand-safety but no control on the context in which their ads are displayed.

To be effective, AI technology needs to be deeply embedded into digital advertising apertures or portals. For example, we currently have access to 95% of the world’s digital inventory, including Google Display and Video 360, Facebook, LinkedIn, and Twitter. Since cookies store a lot of user information including any personal information a person enters into their browser, there can be concerns around privacy and security. So instead of using third-party cookies, we are connected directly into these different media portals, ensuring maximum safety and data privacy, without sacrificing the ability to target effectively. Using proprietary algorithms, we compile these into an anonymised behavioural profile and feed these to programmatic engines to target real people.

In summary, a fully automated AI system will go a long way in empowering advertisers to examine different aspects of brand safety, and protect their brands from ad fraud and brand risk while ensuring that their ads are positioned to the right audience, at the right place and time.

Ian Chapman-Banks is the chief executive officer and co-founder at Sqreem Technologies.