Machine Learning Reshapes the Marketing Landscape

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Marketing has been evolving from a discipline based on gut instincts to one relying on empirical data. In fact, marketing today is almost completely driven by data, and companies are collecting oceans of it, literally. By 2020, the world will have accumulated 44 zettabytes of information, up from 10 zettabytes in 2015, according to market research firm International Data Corp. (IDC). To put those numbers into context, one zettabyte of apples would fill the Pacific Ocean.

But collecting data is only the first step in the process. By itself, data does nothing. Its value only comes once corporations use it to garner insights that can then be used to improve the business. Given the sheer volumes of data available, that is no easy task. Using information to gain business insights is labor-intensive, requiring marketers to sift through reams of reports, correlate items in an ad hoc manner, make business decisions, and monitor their results.

Luckily, machine learning has finally reached a level of sophistication where it can help.

“Machine learning has become mainstream in marketing,” says Colin Priest, director of product marketing at DataRobot. “This is mainly due to increased competition, which forces innovative marketing teams to adopt new technologies that give them insights from data coming in from their multichannel touch points.”

Machine learning, the next rung on the artificial intelligence ladder (see sidebar), turns information into action. It relies on algorithms to illustrate data point connections and generate reports. Since machine learning is a horizontal technology, marketers can apply it to any part of a customer engagement.

Companies can, for example, use the technology to measure marketing campaigns more granularly than in the past. With traditional newspapers, radio, and television, businesses blasted out advertisements and had no idea how they impacted customers. With search and social media enhanced by machine learning, they can now monitor where users click and gain more insight into the sales process.

Advertising is certainly one area where machine learning is widely used. “The most mature of the machine learning tools in marketing is programmatic advertising,” says Andrew Frank, a vice president and distinguished analyst at Gartner. “Firms have been using sophisticated software for years now and know how to bid on real-time opportunities based on whether or not users are clicking on their ads.”

Social media also generates oodles of information. Each day, more than 3.5 billion Snapchat videos are generated, and machine learning can also help marketers filter and use that content more effectively. “Machine learning tools enable companies to capture social sentiment and identify which customers are brand champions,” says Gerry Murray, research director at IDC.

Customer service is another area of interest for machine learning, with 57 percent of executives believing its most significant benefit will be improving the customer experience, according to Forrester Research.

Rather than simply responding to random inquiries, companies can build models that anticipate what the consumer wants and respond proactively. For instance, if a customer picks up the phone after clicking through a website and ending up with an empty shopping cart, the representative who answers the call could deduce the customer was unable to find the specific product she wanted.

In addition, companies are relying on machine learning for segmentation analysis. They are trying to identify the high-value customers and determine how to serve them better, while at the same time trying to identify the low-value customers and limit the resources they consume.

Personalization is one more area of emphasis. Marketers’ ultimate goal is real-time personalized advertising that spans across all platforms and delivers optimized messaging to each customer.

One way to achieve that is with recommendation engines, another place in the marketing stack where machine learning has really taken root. Once customers purchase a product, companies can present them with similar items that might interest them as well.

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