How will AI transform programmatic video?

Debbie Meltzer
Aug 24, 2017 · 4 min read

Is the Artificial Intelligence narrative in programmatic video on track or are we being deliriously dosed on hype and marketing claptrap?

Artificial Intelligence (AI) is changing the way we think of advertising technology. Increasingly, ad-tech companies are investing in AI to improve personal engagement, influence consumer decisions and monetize more smartly. Programmatic video, involving complex standards and bidding processes, is under pressure to harness machine-driven transactions to vastly improve video ad delivery and yield optimization.

Meanwhile on the consumer side audiences, in particular millennials expect a more personalized communication channel. They’re tired of mass-marketed ads and one-way sound bites 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 the outcomes of AI. We’d like to believe that AI transitioned beyond sci-fi and matured into a technology that will truly uplift brands, heighten consumer engagement and offer more return to publishers, as indicated in an eMarketer survey. But we need to make sure we’re not stepping into a minefield of smoke and mirrors. There are still reservations whether AI lives up to market expectations and fulfills the promise of substantially impacting campaigns results.

The evolution of Artificial Intelligence in programmatic transactions

Artificial Intelligence 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 efficiently.

AI algorithms that learn to predict consumer engagement at the right time and then modify the bids to maximize results, could offer a leap board for advanced programmatic transactions. Through big data analysis and laser targeting, these algorithms can help marketers reach their goals and exponentially improve their campaigns. On the sell side it holds the promise of significantly increasing publisher’s yield optimization in real time to reach far better returns.

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.

Ad-technology is harnessing machine learning, a subset of AI, to learn what types of campaigns yield the best returns. 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.”

Machine learning for programmatic video
When monetizing for video, showing the right ads to the right customer at the right time is a mammoth challenge. Through machine learning, programmatic video solutions are able to manage predictions that forecast how campaigns will work out and optimize for the best match. These algorithms can be purposed to analyze programmatic video processes in a way that is not humanly possible. They can identify the best users by their habits and interests and determine whether a video’s creative should be instantly loaded and played. Machine learning algorithms are already introducing new ways to predict and automate video campaigns such as; identifying the optimum time of day to bid and the probability score for a viewer to engage with the video unit. Once information is fed through the big data pipes and processed through programmatic platforms, AI-driven monetization can figure out;

- Where to bid and what amount to bid on
- Which audience is most likely will convert
- What format to use; Outstream, Interstitial, etc., and on what device

With machine learning algorithm, programmatic video can automatically manage all the bids on the client and server sides. The ability to bid and transact on both sides significantly reduces video latency and improves cross-device delivery through a process known as hybrid video header bidding. The bids can then be adjusted by machine learning algorithms and optimized in real time for enhanced results.

The human factor is still needed
Nonetheless 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.

In sum
Programmatic video driven by AI technology is moving into a realm of more powerful transactions. Advanced programmatic video platforms are harnessing AI to automatically tie data, speed up loading times, manage bids and improve yield. Machine learning algorithms are matching demand with supply by identifying the best users and context. Predictive modeling processes are defining which impressions are worth bidding on and which to abandon.
It’s a brave new world out there, but the future is looking bright. With the right partner AI technology can make a significant difference to consumer engagement, brand campaigns and publisher revenue.

By: Debbie Meltzer, Marketing content, debbie@cedato.com. Cedato

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