GUEST:
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? Outside of this sector, we hear about AI being applied in medical and military fields, but usually not something as commonplace as media. Utilizing these advanced technologies actually enables mar tech and ad tech companies to create highly personalized and custom digital content experiences across the web.
The ultimate goal for all marketers is to drive sales through positive brand consumer engagements. A major problem for marketers is that they have so much content (often times more content than they even realize) and millions of potential places to show it, but they don’t know the most 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. Testing an infinite amount of combinations of creative ideas and placements is like a puzzle that keeps adding more and more pieces while you are trying to place them all together.
So how can marketers put this data to work to efficiently and 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. 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 that were 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 with the always-on, constantly connected, multi-device life that we all lead.
The power of machine learning and artificial intelligence is the ability to achieve massive scale that is not otherwise possible while also maintaining relevancy. This demand for personalization makes the amount of combinations necessary to test explode. 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 power of a bidder powered by machine learning lies because individual marketers are not able to observe these trends due to the fact that they may 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 on 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 revise. 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 be 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 trust for brands in advertising spend and the technology that executes content and media.
Kris Kalish is the Director of Optimization at Bidtellect, a native advertising platform.
