AI’s Real Moment On The Advertising Stage Is Tomorrow

One of the hottest storylines in ad tech this year has been artificial intelligence (AI). Proffered as potential panacea for effectiveness, brand safety and transparency, AI has grown from niche discussion to industry obsession, a promised key to smarter digital ad targeting and trading. I’m sure you’ve seen the pronouncements: it can determine the bids most likely to succeed, it can use historical performance data to tailor campaigns and it can even swap out creative based on audience data in real-time.

There’s one issue: a lot of what’s being hyped isn’t actually AI. It’s just tools and technologies being marketed as AI in order to differentiate within a complex and competitive arena. With true AI, a machine imitates intelligent, and maybe even sentient, human behavior. And while much of what we see today looks like the computer is thinking for itself, it’s really just following very specific, pre-programmed paths using simple rule-based actions, and/or predictive analytics or machine learning. While all subsets of AI, even together they don’t add up to real AI. They’re more like “artificial AI.”

It may seem like semantics, but there are important differences. With predictive analytics, patterns in existing data are used to predict probable results and trends in the future, typically using statistical models and methods. Then, there’s machine learning, a branch of artificial intelligence where machines learn and adapt through experience, without the need for predetermined rules and human intervention. With machine learning, models and techniques will change themselves over time as more classifiers enter the system and improve the description of the data to be learned. Examples of machine learning classifiers are K Means Clustering, Linear Regression, Logistic Regression and Decision Trees. These techniques are being used in technologies today for things like facial, voice, music and handwriting recognition.

While not using true AI, the market-available technologies for programmatic advertising that we have today are still sophisticated. They effectively use machine learning and data science-based systems to predict the likelihood of desirable outcomes. They’re certainly valuable for advertisers looking to optimize their media budgets. Further, the automation they allow creates huge efficiencies. They do not, however, fulfill the promise of a set-it-and-forget-it system that gets better or more accurate without any human intervention.

When real AI is finally applied to advertising, it will be transformational. It will intelligently enable desired outcomes to be produced by calling on not one, but a collection of interrelated sciences, techniques and data processing. One day, a truly intelligent AI-based advertising system will enable buyers to seamlessly construct their entire campaign, complete with optimized buys and evolving tactics, just by specifying their goal(s) and budget. Once the algorithms take over, the system will leverage historical data about similar campaigns to make predictions and changes on the fly.

This is all achievable. But supporting it requires highly complex systems to come together, and we still have a long way to go based on today’s fragmented, disconnected assortment of pseudo-systems that look fantastic in isolation, but, in aggregate, don’t add up to the holistic system that buyers need.

Eventually, AI will evolve to where it can improve programmatic media and create a better user experience. And we will eventually get to the point where technology can drive ad campaigns that, without human interaction, achieve campaign KPIs through a virtuous circle of measuring, analyzing and acting on campaign spend, allocation and outcome variables.

In the meantime, platform vendors intent on presenting today’s “artificial AI” will accomplish more by being open about the realistic expectations of their products and the fact that their capabilities are that of early-stage, partial expressions of AI. At the same time, we as an industry should raise our level of thinking and education, so we gain an accurate understanding of AI — not just what it is, but what it can do for us. It is with that knowledge that we can begin to see the true potential of real AI.

Originally in MediaPost – MarketingDaily.

Moneyball 2.0

Welcome to a world of multiple exchanges, complex algorithms, confusing jargon, aggressive middlemen, and tuned-out clients. Sound familiar? Well, it’s not ad tech. It’s Wall Street.

The eerie parallels between today’s Wall Street and the increasingly automated world of advertising are unmistakable in Michael Lewis’ new book, “Flash Boys.”  (Lewis has shed indirect light on our industry before with his 2003 book “Moneyball,” which became the the hero narrative for quants.)

In “Flash Boys”  a group of entrepreneurs take on the big banks and high-frequency trading firms, because those players are using opaque private stock exchanges to rip off unsuspecting clients.

This contemporary high-tech Wall Street is, on the surface, very similar to programmatic ad tech. Clients entrust professionals with their money; the professionals make trades in a complicated system of bidding alorithms and exchanges; and a host of middlemen and technology people manage the handoffs between buyer and seller. Sounds a lot like marketers putting ads on digital publishers through a programmatic system.

Given the parallels, players and would-be players in programmatic should sit up and pay attention to “Flash Boys” — if for no other reason than to avoid Wall Street’s mistakes.

Here are the similarities between “flash boys” and programmatic advertising:

Math Men. Like advertising, Lewis’ Wall Street, on the surface, is dominated by client guys with expense accounts. Underneath is a class of mathematicians, software programmers and IT guys, who are building the plumbing and the logic that makes the whole financial system work.

Murk. However, a market built by Math Men has major clarity issues. Just as in programmatic technology, the plumbing of the new Wall Street is complex and hard to understand. And according to Lewis’ descriptions of clueless salesmen and clients, Wall Streeters would rather live in a world they don’t fully understand than look stupid by asking questions.

Agency problems. Murk breeds a disease that’s best described by microeconomics: agency problems. The textbook definition of an agency problem is when a “principal” hires an “agent” to act in the principal’s best interests; but the agent’s interest may conflict with the principal’s. Agency problems thrive in murk. When the principal can’t understand what the hell the agent is talking about, the principal is going to have trouble applying his or her judgment.

The tragedy of murk and agency problems is that they slow down the adoption of a system that can actually benefit everybody. Don’t trust your agent? Now a principal will become mistrustful, and simply avoid the new system.

What Programmatic Advertising Can Learn from “Flash Boys”

Many of Michael Lewis’ books view exotic worlds through the eyes of a naive hero figure. In “Moneyball” it’s Oakland Athletics baseball coach Billy Beane (played by Brad Pitt in the movie). In “Flash Boys” it’s Brad Fukuyama, a trader for Royal Bank of Canada. In their small ways, both heroes took on an established system and won.

I would argue that any of us entering the deep dark woods of programmatic should emulate this Michael Lewis hero archetype in order to be the best champions of our businesses in a complex world of advertising technology.

Heroes are ordinary. Fukuyama and Beane didn’t know a damn thing about the exotic worlds of high-frequency trading or statistics (respectively) when their narratives begin. They are not powerful people in their industry. They do not even have especially distinguished careers. Yet they change the system. Translation to advertising: Advanced degrees are not required to take command of your programmatic strategy.

Heroes ask stupid questions that are actually smart. “Flash Boys”’ most compelling passages show how Fukuyama and his companions chip away at the secrets of high-frequency traders. They do it by asking questions, and by demanding answers they can understand. One is shocked by how many people they have to ask, how many times they have to ask, and how much balderdash they have to tolerate, to get real answers. Translation to advertising: Gird your loins for condescending answers to your common-sense questions, but stay strong.

Heroes are not afraid to demand change. Once the heroes of “Flash Boys” truly understand the system, they are able to create a solution that works for them. (Fukuyama has started an alternative exchange called IEX in response to the experiences covered by “Flash Boys.”) Translation to advertising: The programmatic ecosystem is in its early days; the way it works can be shaped by clients with clear ideas and demands.

I am in no way suggesting that the world of programmatic is a treacherous place in need of reform. Quite the contrary; I believe that programmatic advertising offers astonishing efficiencies for marketers, and those efficiencies can be used to benefit those marketers’ products and customers, employees and shareholders.

What I am suggesting is that the ecosystem of programmatic advertising will benefit from a plain-English approach. Businesspeople — marketers and media folk alike — can, and should, still speak Business even after they cross the border into Technology. Math-man dazzle and murky terms should not be tolerated. Agency costs can be avoided; efficiencies can be gained; and, as we build the ecosystem we deserve, we can all be heroes.