Self-Driving Media Buying: What Is It, and Why Do Marketers Need It?

January 28, 2021, 12:01
By Frank Guzzardo

When you think of “self-driving”, the first thing that comes to mind is most likely an image of the autonomous vehicles that carmakers have been promising us for years. Yet the same principle that underlies self-driving vehicles – namely, the ability of those machines to drive without human assistance or oversight – can also be applied to media buying through the application of deep learning. Just as deep learning enables self-driving cars to take in all kinds of data about their surroundings and use that information to make decisions about the best routes, when to start and stop, and how best to avoid accidents, it can also be used to determine the optimal media-buying strategy for reaching and converting customers, without requiring marketers’ constant attention or management.

To put it simply, self-driving media buying is simply the process of letting a deep learning-enabled algorithm make decisions about media purchases based on what it thinks is most likely to lead to a positive outcome. It is able to make those predictions by analyzing a marketer’s first-party data to see what factors in the past might have led a customer to perform a certain action, as well as which characteristics single someone out as more likely to become a customer of that brand. The algorithm itself is self-learning, which means that it is constantly taking in new information and using those inputs to adjust its predictions, ensuring greater accuracy and efficiency over time. 

Many marketers might find the concept of self-driving media buying to be intimidating, but in fact the opposite is the case. Currently, many marketers find themselves in the position of having to manually optimize their media buying, constantly pulling levers, trying to find scale while having to add more targeting to get results.  It is frustrating and inefficient. Self-driving media buying frees marketers from these inefficiencies because the algorithm itself is able to self-optimize, and because it can manipulate an unlimited number of combinations of “levers” it is able to achieve results at scale. Instead of having to take a hands-on approach to every media buying decision and wait to see if it worked, marketers can let the algorithm take the lead and spend time focusing on more pressing, strategic business decisions.

Self-driving media buying powered by deep learning that is tailored for marketing offers several additional advantages to marketers. Besides optimizing spend, it can also algorithmically deliver incrementality at scale, identify the best way of reaching and converting a potential customer, optimize creatives and creative order, and drive ever greater efficiencies over time.

While self-driving cars are not yet available to the everyday consumer, self-driving media buying is within every marketer’s reach – and it does not require a data science team to implement. With the help of AI powered marketing companies providing algorithmic advertising products, marketing teams can finally become well-oiled machines, capable of meeting every marketing challenge and finally achieving what seemed impossible over the past decade – great results at scale.

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