There’s been a lot of buzz in the marketing industry — not to mention in the mainstream media — about the growing power of “machine intelligence.” But the science of — and fascination with — artificial intelligence has been around for decades.
Computer scientists and psychologists have long expounded on theoretical concepts and explored advanced modeling techniques in the tireless pursuit of creating machines that “learn.” What has caused the field to explode in recent years is the exponential growth in computing power available to inventors, researchers and innovators in the subject area.
A legendary theory in computer science circles, known as “Moore’s Law” after renowned researcher and Intel co-founder Gordon Moore made the initial observation in 1965, predicted that computing power would double every two years. And it largely has, despite computer scientists boldly (and wrongly) predicting the “death” of Moore’s Law every now and then. This power has opened up tremendous opportunities to leverage machine learning algorithms in an incredibly broad range of use cases, not just a sensational event like a computer beating a human expert in the world’s most complex game, but also in things we experience every day.
Think about it: Machine learning is all around us. Apple’s Siri, Microsoft’s Cortana and Amazon’s Echo (“Alexa”) — voice recognition powered by machine learning. Self-driving cars? Coming soon. Facial recognition models used by the TSA? Already here. Even some of the news we read is generated based on algorithms, with little to no human editing required — sports results and financial activities, in particular, are fields where we have seen automated reporting emerge.
Marketing, of course, is no stranger to the benefits of machine learning. When Amazon recommends to you that “customers who browsed for this product also browsed for X, Y and Z,” it is the outcome of sophisticated machine learning algorithms sifting over enormous amounts of data to recognize patterns of product browsing across their immense customer base. And marketing organizations themselves in recent years have started to see a rapidly growing staffing demand for that most elusive of creatures: data scientists. These are the experts who wrangle big data, develop machine learning algorithms, and use the model results to drive action, supporting the most challenging problems facing marketing, customer service, customer experience and more.
As one industry pundit said recently: In marketing, “data science is having a moment.” Similarly, “predictive intelligence,” referring to a broad category of data science and machine learning techniques used to forecast outcomes, has been the rage in the last couple of years. Note that tools and technologies exist today such that the execution of predictive models can be partially or fully automated. For example, Adobe recently announced that they are rolling out an in-house artificial intelligence platform, Sensei. As TechCrunch reported, “Sensei uses various forms of artificial intelligence … to build models and distribute the data generated by these models across the platform where it’s needed.” This, combined with ongoing technology innovations and Moore’s Law, is starting to make the actionability of customer data at scale a reality.
Jim Sawyer is Chief Scientist at Elicit. The company's resident savant, Jim is responsible for the artistic application of Elicit’s customer science. From evaluating the state of customer data and analytics systems to developing customized segmentation, Jim leads a team of data scientists to bring customers to life through data. He has over 20 years of experience in analytics, a Stanford B.A.S. in Mathematical and Computational Sciences and a Georgia Tech M.S. in Industrial and Systems Engineering. Elicit's Fortune 500 clients include Southwest Airlines, Fossil, GameStop, Sephora, BevMo!, HomeAway, Best Buy and Pier 1 Imports.