As the digital advertising industry gathers on the slopes of Vail, it is tim
As the digital advertising industry gathers on the slopes of Vail, it is time to take a closer look at a topic that I mentioned in my previous Vail post: the role of artificial intelligence in digital advertising.
It’s easy to confuse the AI chatter for a fleeting trend – yet for our industry, it should be regarded as the next step in our evolution. Digital and programmatic advertising has been moving towards an automated state since the mid nineties: Yahoo launched digital ads in 1994. In 2011, real-time-bidding (RTB) finally became mainstream.
Initially, automation was the key to buying and selling ad inventory at scale. Today, it is beginning to touch everything from targeting to creative messaging, becoming more refined by the day.
The next step in this evolution is predictive, AI-fueled monetization forces publishers to reimagine the content experience from start to finish. The days of hard-coded ad placements and static page layouts are numbered. Innovative publishers will invest in solutions that continuously optimize visual layouts and ad serving based on historical data. Read on to learn more about these specific applications.
User behavior is a curious phenomenon: it is both unique to a single website visitor and collective in the sense that once you have enough data, you’ll notice common behavioral patterns. How users with a certain profile are consuming your content, where they are clicking, how long they spend on your site, and so on.
With AI, publishers can build predictive models that turn page layouts into a puzzle play. The look-feel of any given page is molded to fit the user’s predicted behavior. Once publishers can deliver optimal experiences, session length will dramatically increase, leading to greater monetization opportunities.
Continuing on the notion of monetization, not all users are created equal: some are more receptive to advertising content, while others are turned off if they are aggressively targeted with ads. In the worst case scenario, poorly designed ad experiences push users to resort to ad blockers. With predictive AI, publishers can clearly see the ad saturation points for different audiences and tailor the experience. Some users might be happy to see ads upon landing, while others may desire a few pages of ad-free content before they can be advertised to.
This is where monetization gets fascinating. With fluid layouts, ad placements compose a part of the moving puzzle. Over time, you’ll be able to model ad creation based on historical data: where the users have clicked, what kind of a placement gets noticed, where and when it should be animated into view. Machine learning will help even determine the ad type: it will know if a particular user is likely to convert from a video versus a display ad, for instance.
Let’s continue the AI conversation! Contact me here to learn more.
Antti Pasila, CEO and Co-Founder, Kiosked
To declare ads.txt partners, publishers need to create an ads.txt file and list the companies that are authorized to sell their digital inventory. The file should be placed on the root domain of the publisher's website and can be accessed by anyone who wants to verify the legitimacy of the inventory being sold.