Machine Learning Is Helping Martech Lead the AI Revolution

Machine Learning Is Helping Martech Lead the AI Revolution

By Devin Guan. Published on June 19, 2017.

Artificial intelligence gets a lot of press (thanks, Elon), but the fact is, AI couldn't be the rockstar it is without the behind-the-scenes help of machine learning (ML). While the two are closely related, there's a critical difference between AI and ML: AI makes decisions, while ML makes predictions. Think of it this way: it's AI that steers the Mars rover around the rock in its path, but it's ML that recognizes the rock to begin with.
In marketing and advertising, the best example of AI is the programmatic ecosystem. This includes the decisions made around whether or not to bid on a given impression, how much to bid, what creative to serve, and various campaign optimization techniques.

But in order for the AI to make good decisions, it needs valid ML predictions as input. The fastest, most efficient programmatic bidder on the planet would be useless if poor decisions were being made. Therefore, it behooves the teams in the marketing and advertising space that are building AI to focus first on delivering the strongest ML. This mindset is exactly what the industry has begun to adopt.

In the martech space we hear a lot about graphs, including social and identity graphs, as well as Google's PageRank, which ranks the relevance of websites to populate search results. These graphs typically refer to an interconnected web of consumers, devices, cookies, IDs, locations and websites. The most prominent application of these graphs is personalization -- from targeted ads, product recommendations, news articles and other digital customer experiences. That personalized content is a decision (AI), as a result of the predicted graph associations (ML).

Determining identity

Brands and enterprises want to know if two given devices are owned by one person; if two individuals are part of the same household; if the logged-in user of an app is the same person who visited the website without logging in; and if the consumer who was recently at this location uses this other device. By determining identity, information from one environment can be leveraged in another.

Unfortunately, because we're dealing in a world of predictions, these questions don't yield binary answers. The world is more complicated than just black or white, yes or no. We don't see a movie because it's objectively good or bad. We see it because we read a good review, we like the actor, it won an award, or maybe the showtime is just convenient. There are a ton of influences and attributes behind every decision.

Understandably, brands and enterprises don't want a statistic for an answer, and so our job as technologists is to get as close as possible to a "Yes" or a "No" with certainty. One way to do that is by applying ML to these graphs and looking at how devices interact with each other.

Most of the firms using graphs – brands, agencies, and enterprises – are thinking about device pairs and how they interact with each other. They constantly ask: "Do Smartphone 1 and Tablet 2 belong to the same consumer?" In order to answer that, however, you have to take into account Desktop 3 and Smart TV 4. In other words, instead of determining just the probability of A or the probability of B, it's determining the probability of A given the probability of B.

Increasing complexity

But it goes beyond A and B. It extends to, "What is the probability of A, given the probability of B and the probability of C through Z." When any of those individual inputs change, the rest of them change as well. And in our interconnected world, it's not even A-to-Z scenarios. It's billions of scenarios. You can see how arriving at a "yes or no" answer is not a simple task.

In fact, it gets even more complex. Because each device has attributes that interact with each other -- locations, IDs, cookies, etc. -- the whole data set acts as a fractal, where every attribute has attributes that have attributes. At some point, all this gets too complex to process for even the most advanced supercomputers. So while we're still left with an approximation at the end of all of this, by applying machine learning to the graph itself, we can get much closer to the truth, in a purely technical sense, or a "yes or no," in a practical sense.

Advanced ML is just now beginning to be applied outside of academia, and our world of digital advertising and marketing is among the first professions to implement it. From more relevant ads, to personalized content, to e-commerce recommendations, to predicting customer churn, to new applications in things like fraud detection and risk protection, marketers are finally seeing the concrete benefits of harnessing ML.

That said, new methods inevitably bring new challenges. Keeping pace with the rate of change, and bridging data science to business strategy, will be tantamount to marketers in the years ahead, as machine learning becomes a catalyst for innovation in our industry and beyond.


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