Is the Artificial Intelligence (AI) narrative on track or are we being deliriously dosed on hype and marketing claptrap?
Artificial Intelligence is changing the way we think of advertising technology. Increasingly, ad-tech companies are investing in AI to improve personal engagement, influence consumer’s decisions and monetize more smartly. On the consumer side audiences and in particular millennials expect a more personalized communication channel. They no longer want mass-marketed ads and one-way sound bites across their multiple screens that ignore their needs and preferences.
But are consumers ready for a deeper level of hyper-personalized exposure and brand engagement? A survey by Boxever found that marketers are more confident about consumers’ readiness for AI.
Ad-tech is certainly feeling the pressure to build AI-based solutions that can predict consumer engagement at the right time, on the right device, and share personalized experiences while delivering far better monetization. But can AI truly live up to market expectations, or are we stepping into a minefield of smoke and mirrors?
The evolution of AI in ad-tech
AI makes it possible for computers to simulate human thinking processes. It rides on the surmise that computers can capture far more data than humans. It assumes computers can process data at greater speed and perform multiple tasks more effectively and efficiently.
In video it introduces the ability to predict and automate campaign processes such as buying the right media, targeting the right users and personalizing the creative.
On the sell side it holds the promise of significantly increasing yield optimization in real time to gain far better ROI.
If so, we’d like to believe that AI transitioned beyond sci-fi and matured into a technology that can truly uplift brands, heighten consumer engagement and offer significantly more return to publishers.
Indeed video ad-tech is harnessing AI to learn what types of campaigns yield the best returns so that they can be monetized more effectively. Long gone are the early days of programmatic transactions based on hit-and-miss campaigns that notoriously drained budgets and left a trail of frustrated partners on the buy and sell side.
AI and machine learning for video header bidding
When it comes to monetizing for video, showing the right ads to the right customer at the right time is a mammoth challenge.
Through AI and machine learning algorithms, programmatic video solutions are increasingly able to manage predictions that can forecast how campaigns will work out and optimize for the best match.
AI can be used to analyze programmatic video processes in a way that is not humanly possible. It can identify the best users by their habits and interests and determine whether the video creative should be instantly loaded and played.
Once information is crunched through the Big Data pipes and fed through programmatic processes, AI-driven monetization technology can figure out where and when to bid, the amount to bid on, which audience is most likely to convert and what format to use.
When combined with machine learning, these automated processes can help to automatically manage the bids on the client and server sides, otherwise known as hybrid video header bidding. It can then adjust the bids and optimize in real time for outstanding results.
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.”
Nevertheless although machine learning algorithms do the heavy lifting and lower costs, there are still blind spots that can only be recognized and managed by people.
Unsupervised self-learning technology will only take you so far. After-all technology can only process and analyze the information it’s fed. A machine won’t try to figure out what’s wrong with the data, or work out a way around that problem.
Here at Cedato we are increasingly working with AI technology to tie data together and improve programmatic video transaction processes. Our machine learning algorithms match demand with supply by identifying the best users and context at the right timing. We use predictive modeling to help determine which impressions are worth bidding on and which should be abandoned. Our algorithms learn and automatically optimize in real-time to meet and exceed our partners’ monetization goals.