What Machine Learning Can (and Can't) Do

• Updated on: 2018-09-06
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On first hearing, “machine learning” and “Artificial Intelligence” sound like technologies that will replace people. Computers will find people and sell them stuff they want, so who needs humans in marketing?

Well, it turns out that you do. Computers can do the scut work of counting, but only humans can truly say what counts. Marketers are not going to be replaced by automation, but they can make best use of it so long as they know what it can do and can't do, just like any other tool.

Take note of what machine learning can and cannot do. You can program it to seek attributes, to count how many clicks a web site gets, “to learn from the data for a long time.” says Amil Kamath, Adobe Fellow and VP of technology at Adobe.

Machine learning can recognize campaign subject lines, tag images for visual search, analyze sentences, undertake real-time decision-making, power recommendation engines, and engage in real-time bidding, he pointed out.

Machine learning or AI is best applied either when there is a low yield in a business process or a large consumer surplus is generated from applying AI. While there are many functions where machine learning can be applied effectively, marketing, drug discovery or patient monitoring are sweet spots for machine learning.” says Aman Naimat, SVP of technology at Demandbase.

“[W]e should not apply machine learning to tasks where humans are very effective, like air traffic control at an airport.  If a task is already optimized, incorporating machine learning would not serve to maximize any return on investment.” he says.

Machine learning is “a buzz word mixed with AI and chatbots,” says Meghan Keaney Anderson, VP of marketing at Hubspot. “It's really about a type of programming that looks for patterns in the data and tries to learn from past patterns.”

“By learning, [AI] becomes smarter in the process,” she adds.

“We've seen artificial intelligence and machine learning across the entirety of sales and marketing.” says Nipul Chokshi, VP of marketing at Lattice Engines, a specialist B2B solutions provider. Machine learning is great at spotting patterns, can highlight segments for targeted marketing, and spur demand acceleration further down the funnel. It should enable the marketer to deliver a smarter message, he said.

Before machine learning becomes effective, the machine — obviously — has to learn. Programmers will shovel terabytes of data into the hopper, all gladly digested by the learning algorithm. Ironically, the system is no less human than the people who built it. That means errors will be lurking. They will have to be screened out.

Take Lattice Engines. It offers a solution that looks for patterns in the data to identify who is likely to convert to a sale. “We use 80% [of the data] for our training model. “ Chokshi says. The remainder is set aside. “We use it to test the model to see if it could predict accurately,” he said, because the answer is already known.

“[I]f machine learning is king, data is his queen. If you don't have enough, or rich, training data, no machine learning algorithm is going to work.” adds Naimat. “The best way to ensure quality of data is actually using more data. We often triangulate confidence in our data by getting different perspective on the same data from different sources. If lots of sources agree, then it's more likely to be true.”

“For us, our data, especially analysis and other solutions—is used by the marketer right now.” says Adobe's Kamath Data is used in the training and it's possible to measure the validity, improve the model, and understand the differences that should be addressed. “Put in an extra layer to remove the outliers.” he said. If something is not right with the data, “we will see things suffer.” he says.

Quality is not entirely guaranteed, but quantity can make up for it, Anderson notes. “With small data sets, the impact of bad data is exponentially worse.” she said. “Whenever we see people fail, it is because (they) applied too small a data set.”