The Artificial Intelligence Marketing Arms Race

marksmithData-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Mark Smith, president at Kitewheel.

Marketers, being marketers, are always looking to invent new names for their products to make those products seem better than previous iterations.

This is most common when the need for new product cycles outpaces companies’ ability to produce significant new features or advances. For an excellent example, take a look at what’s been happening in the analytics space for the past 25 years.

Twenty-five years ago, reporting and analytics were the two primary categories for how companies leveraged data. Reporting was about putting critical data into useful formats, while analytics was the process of forecasting and predicting the future based on the analysis performed on the data contained in reports. This basic division has not really changed in the two and a half decades since, but the terminology certainly has.

Analytics became “advanced analytics,” followed by “data mining,” “modeling” and “predictive modeling.” These were all iterations on the same concept: using data to obtain a prediction of the future to a relatively high degree of certainty.

After “predictive modeling,” one of the more recent terms that evolved was “machine learning.” This, in a way, was somewhat differentiated from its predecessors because it added an element of automation around the modeling process in analytics. Today, “machine learning” has mostly become “artificial intelligence” (AI). The term even became a core theme for advertising at this year’s annual conference for the Direct Marketing Association.

What characterizes AI, in the context of the marketing and advertising technology industry? Is it an automated, “real-time” process of analytics or is it data modeling to generate predictions? To summarize, it’s the most recent iteration of machine learning, which is just an automated version of what was once known as analytics.

Today, so-called AI is used to predict the likelihood that a given marketing tactic will produce a certain outcome, and that the amount spent on that tactic will produce more conversions. Essentially, AI exists to tilt the odds in the marketers’ favor when planning campaigns.

But is this really artificial intelligence? Do the algorithms that produce marketing predictions possess an intelligence that allows them to operate in a way that mimics human thinking? Not exactly.

No matter how many layers of automation it’s wrapped in or how accurate the algorithms become, statistical analysis will never be artificial intelligence. True intelligence is a layer of comprehension and understanding that goes on top of the various statistical algorithms used by marketers and data analysts in many other industries on a daily basis.

What analytics platform could have predicted the breakout success of Pokémon Go? What human could have, for that matter? Human nature is still ultimately unpredictable in a way that data analysis cannot accurately account for. While so-called intelligent algorithms can make feasible the large-scale arbitrage models that are so common in digital marketing, it is possible to predict approximately how many sales will take place out of a given number of impressions, for example. The intuition that guides marketing implementation is unique to humans.

This is not to understate the power of current data analytics models. While it is true that the general concept has not changed over the past 15 years – we still often use aggregated data to make predictions – the capacity and degree of automation has grown exponentially. It’s a testament to the power of these systems that they can even be called “artificial intelligence” with any degree of credibility. It is amazing that a platform can ingest literally billions of data points and accurately predict outcomes that drive true business results, and perhaps we have not yet come up with the right term that truly captures that phenomenon. Hence, we rely on “artificial intelligence.”

As data analytics platforms become even more automated and accurate, it is a near certainty that a clever marketer will come up with a bigger and better term than “artificial intelligence” to describe the next generation. This will not change the underlying reality that the same statistical algorithms are still at the heart of any predictive technology. I guess the true “arms race” is in marketing terminology and not in mathematics.

True “artificial intelligence” will, unfortunately for those employed in the marketing industry, only emerge when technology is capable of replacing the humans who ingest data from a wide variety of sources and orchestrate marketing programs using a combination of intellect and intuition. In that respect, we should perhaps hope that the advent of truly intelligent software takes place later rather than sooner.

Follow KiteWheel (@Kitewheel) and AdExchanger (@adexchanger) on Twitter.

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  1. While I tend to agree that nowadays terms like artificial intelligence and machine learning are inflated and often misused, they are very established computer science techniques that have been used for many years and are used by many people today in their daily life (think virtual assistants, online product recommendations, etc.). While popularity is raising now, AI has been around for many years (since 1950s). Today, the advancement of computing power makes AI application a lot more powerful and useful in various fields. With the explosion of data in the online world, machine learning and artificial intelligence are taking a dominant role in predicting campaign outcomes that match buyers KPIs, which would be an extremely complex problem to solve for traditional software, given the huge amount of variables involved. AI is very different from big data, statistical analysis, business intelligence, the main difference being that AI algorithms can be trained and can learn from sample data. Once they learn they are able to make predictions on future outcomes when presented with data outside of the sample used for learning (similar to the way humans learn). It is complex technology and there are a handful of teams with world-class expertise that can claim to develop and apply true AI and machine learning; in this regard I tend to agree that often time such terms are just a good fancy cover for more “traditional” predictive algorithms.

    • Hi Fabrizio – thanks for your comment, I think you got my high-level point here that people are jumping on the AI bandwagon with things that are not AI. However I do have to point out that your definition of training on sample data and predicting on new data is exactly what any good statistical algorithm in SAS, SPSS or R would do, and is what more esoteric techniques like neural networks and genetic algorithms have been doing for over 25 years – I was using them in the late 1980s. But these approaches are the very pinnacle of what marketing technology will use today, I just don’t think it should be called AI, that term should be saved for something much more sophisticated. Very best regards, Mark