In today’s age of flying cars, robots, and Elon Musk, if you haven’t heard of artificial intelligence (AI) or machine learning (ML) then you must be avoiding all types of media. To most, these concepts seem futuristic and not applicable to everyday life, but when it comes to marketing technology, AI and ML actually touch everyone that consumes digital content.

But how exactly are these being deployed for marketing technology and digital media? We hear about AI being applied in medical and military fields, but usually not in something as commonplace as media. Utilizing these advanced technologies actually enables martech and adtech companies to create highly personalized and custom digital content experiences across the web.

The ultimate goal of all marketers is to drive sales through positive brand-consumer engagements. But a major problem is that marketers have so much content (oftentimes more than they even realize) and millions of potential places to show it, but don’t know how to determine the optimal place for each piece of content to reach specific audiences.

With all of these possible placements, it would be incredibly inefficient, if not impossible, for a human being to amass, organize, and analyze this data comprehensively and then make the smartest buying decision in real time based on the facts. Trying to test an infinite number of combinations of creative ideas and placements is like solving a puzzle that keeps adding more and more pieces while you are trying to assemble them.

So how can marketers put this data to work to efficiently distribute their content across the digital universe using the right messaging to drive the best results?

Human beings can make bad decisions based on incomplete data analysis. For example, someone might block a placement from a campaign based one or two prior experiences with incomplete or statistically insignificant data, but it actually may perform very well. But an optimization engine can leverage machine learning to understand the variance in placement performance by campaign and advertiser vertical holistically. This is why computers are simply better than humans at certain tasks.

This does not discount the value of humans, for superior customer service and relationships will always be critical. But the combination of human power plus machine learning will yield a much better result, not only in marketing technology but across all industries that are leveraging this advanced technology.

Machine learning and AI address the real inefficiencies present in digital media and have made tremendous progress pushing the industry toward personalization. Delivering personalized content experiences to today’s consumer is incredibly important, especially given the always-on, constantly connected, multi-device life that we all lead.

The power of machine learning and artificial intelligence lies in their ability to achieve massive scale that is not otherwise possible, while also maintaining relevancy. This demand for personalization escalates the number of combinations that would need to be tested to an almost unimaginable degree. For example, if a marketer wants to build a campaign with a personalized experience based on past browsing behavior, it becomes difficult to glean insight from the millions of combinations of the context in which their advertisement will appear and the variety of different browsing behaviors people exhibit. Even with fast, granular reporting, it is impossible to make all the necessary adjustments in a timely manner due to the sheer volume of the dataset.

Furthermore, it is often impossible to draw a conclusion from the data that can be gathered by running a single campaign. A holistic approach that models the interaction between users and a variety of different advertising verticals is necessary to have a meaningful predictor of campaign performance. This is where the real impact of a bidder powered by machine learning lies, because individual marketers are not able to observe these trends if that they only have experience running campaigns in a specific vertical.

An intelligent bidder determines how each placement has performed in previous campaigns. If one specific placement performed poorly for multiple advertisers with similar KPIs, similar advertisers in the future will not waste money testing that placement. The learning happens very quickly and precisely.  Instead of humans taking these learnings and adjusting the algorithms, the technology is making the changes as they are detected.

By leveraging the billions of historical data points from digital campaigns, predictions are made for future campaigns and then real-time performance data is applied to revisions. This is not a one-off process. The technology is constantly taking insights from user behavior and feeding them back into the algorithms, enabling personalized content experiences at scale.

The advertising industry has faced major challenges in relevancy for consumers and brand safety for marketers. Lack of relevancy in advertising has led to the advent of ad blockers and poor engagement, causing brands to become even more unsure of where their budgets are going and how users are responding to content. The controversy around brand safety further calls into question not only how budgets are being spent, but potential negative consequences for a brand’s image.

Machine learning holds the promise of overcoming these challenges by delivering better, smarter ads to engaged consumers and restoring brands’ trust in advertising spend and the technology that executes content and media.

Kris Kalish is the Director of Optimization at Bidtellect, a native advertising platform.