To the average person, artificial intelligence might conjure scary sci-fi visions of robots and humanoids, but for publishers who are beginning to embrace AI-powered technologies, it means nothing but dollar signs when looking toward the future.
In advertising, AI has already come a long way when it comes to improving basic processes. For example, machine learning has radically improved personalized engagement, making it possible for advertisers to build and serve unique and contextualized ads on an individual basis, appealing directly to what each consumer wants based on vast data points.
Thus far, consumers have positively responded to this use case for machine learning in advertising. A survey from Boxever found that nearly 80% of marketers believe that consumers are ready for AI. Meanwhile, a separate study from Adlucent found that 71% of consumers preferred the highly personalized ads made possible by advancements in big data.
As a result, the entire ad tech industry is scrambling to build solutions capable of predicting consumer engagement at the right time on the right screen, to share personalized ads while delivering top monetization for publishers.
As we’ve already seen the possibilities on the consumer side, the bulk of publishers could still use some convincing that the adoption of AI could be the best technology decision, in particular for advertising transactions. In order to leverage AI technology, publishers need to significantly upgrade their businesses processes and workflow. Many publishers lack the bandwidth and resources needed build or change their infrastructure to transition to AI-based processes.
At its most basic level, AI makes it possible for computers to simulate human thinking processes, based largely on its ability to capture and process data at a greater speed than humans. In advertising, this translates into personalized creative, targeting the right users and perhaps most importantly, automating the campaign process to buy the right media at the right time.
From a publisher’s perspective, this holds the promise of significantly increasing yield optimization in real time to improve ROI. By harnessing AI, we’re now able to see which types of campaigns yield the best returns, rather than blindly relying on hit-or-miss tactics that drain budgets. To date, many acquisition experts resort to calculated guesses when it comes to increasing their bids or amending the way an ad looks. AI-driven campaigns can use machine learning to calculate a campaign’s probability of success based on millions of data points. It can evaluate what happened, why it happened and what might happen next in campaigns.
Over the past year or so we’ve seen publishers flocking to programmatic video to bolster their monetization. This is an area that artificial intelligence has the biggest potential to transform. Through AI and machine learning algorithms, programmatic video is increasingly able to make predictions to optimize campaigns in a way that’s not humanly possible. For example, it can identify the best users by their habits and interests to determine whether the video creative should be instantly loaded and played. In the programmatic process, AI can determine where and when to bid, the amount to bid on, which audience is the most likely to convert and which video format is optimal.
For publishers, these abilities translate into big bucks. According to Juniper Research, machine-learning algorithms that drive efficiency across real-time bidding networks will generate $42 billion in annual ad spend by 2021, up from $3.5 billion in 2016.
So what should publishers be looking for when it comes to AI? Publishers need a solution that can leverage machine-learning algorithms to analyze ad inventory performance data for optimization. The right ad tech solutions will merge together and improve programmatic video transactions, using machine learning algorithms to match demand with supply while identifying the right users, context and timing. In video, where there are more layers of data, AI technology needs to be able to collect multiple data from video inventory and create an invaluable data set on audience interest. This will make it possible to predict how user experience and user engagement will ultimately affect video revenue.
Technology with predictive modeling can determine which impressions are worth bidding on to optimize in real time to meet your monetization goals. So in this brave new world of disruptive publisher technologies, we can see a significantly stronger role for artificial intelligence.