In this multi-part series, we’ll look at upcoming trends in marketing in 2016 you should be prepared to address. Today, we’re looking at Machine Learning.

Machine learning has been a goal of computer scientists and engineers since the 1950s, but only in the last 5 years has it become accessible to more people. Prior to the democratization of machine learning, advanced technologies were limited to the biggest computer science companies and laboratories, only for corporations with the biggest budgets.

What is machine learning? In short, it’s a discipline of computer science in which machines teach themselves, learn from data, and change their programming as data changes. Machine learning means machines figure out how to recognize patterns and adapt to them.

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Today, machine learning technologies are available for pennies on the dollar, from IBM’s Watson Analytics to Google’s Predictive API to open-source projects that require investments in time only. Machine learning organizations have created entire virtual servers you can deploy with a single click to have the necessary infrastructure for a machine learning environment.

What does this mean for you as a marketer?

With the democratization of machine learning tools and technologies, you can download and install artificial intelligence tools on your personal computer that required a mainframe, a room full of servers a generation ago. Leading edge marketing organizations are already experimenting with machine learning tools; Google Analytics has machine learning for attribution analysis built into its Premium product.

If you have development chops or a development team, begin experimenting with the various open source packages available, tying them together with your marketing analytics data. Teach the machines to identify patterns in your marketing data and then extrapolate likely scenarios, likely predictions for future data. A simple example would be to look for patterns in web analytics data. You as a human can spot the pattern pretty easily in this data set:

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Above, you can see the pattern of weekdays and weekends. What you can’t see, but a machine could, would be more subtle patterns in the data, such as the influence of holidays and events. Machine learning tools can extract that information and more important, begin to predict it for days and times in the future.

If you don’t have access to a development team but you’re looking to get your toes wet in machine learning, look to products from companies like IBM’s Watson Analytics as the first step in your machine learning journey. These tools are very affordable, around $30 a month, and can help you mine insights with the data you already have.

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