Through machine learning, computers recognize patterns in data. When exposed to new data, they can answer questions and even make predictions without being explicitly programmed.1 The applications of this are seemingly limitless-and so are the business advantages.
One key area that machine learning can greatly improve is customer experience. For example, businesses can reduce churn, or customer attrition, by using machine learning to identify patterns of behavior that typically precede churn. As customers begin to engage in those behaviors, the business can take preventive measures before customers say goodbye for good.
Similarly, machine learning can help businesses improve customer service. When your system "learns" to recognize the signs that an online shopper is struggling with a task, it can place an instant messaging window on the screen, offering the shopper immediate assistance, perhaps even suggesting a phone call.
Machine learning also enables companies to identify patterns of activity that tend to culminate in customer complaints. Intervening at a crucial point can help businesses turn a potentially bad customer experience into a positive experience, reducing heated phone calls to the customer service department.
Financial services organizations increasingly rely on machine learning, and we expect that to continue in the coming year as well. The technology is particularly useful in helping banks detect and prevent money laundering and fraud. Machine learning techniques help banks reduce false positives and focus where there are real issues. Incidentally, the increased accuracy and focus on anti-money laundering (AML) and fraudulent activities also helps these organizations achieve compliance with increasingly stringent regulatory standards.
A highly promising subset of machine learning is natural language processing (NLP), which will also continue to gain traction in 2017. Through NLP, machines can quickly read and comprehend large volumes of information that would take humans many hours to peruse, let alone act on. Through NLP, machines can recognize patterns in the material, make predictions, and answer questions. That carries implications for many businesses.
For example, a recent article in Forbes noted that "machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need."2 NLP also can be used to "translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case," the article points out.
In fact, NLP doesn't just sort through information; rather, NLP helps attorneys predict the likely outcomes of particular cases given the outcomes of past cases involving similar issues, and determines which specific areas of a case the attorneys should focus on most heavily.
In healthcare, NLP can review massive volumes of physicians' notes and make observations and predictions that can help improve patients' outcomes; for example, it may suggest that certain patients are likely to develop costly complications or wind up in the emergency room within the next 72 hours.
Although machine learning has been around for several years, it is becoming increasingly valuable-and necessary-as the volume of data available to businesses continues to escalate.
In a data-rich environment, organizations that use machine learning to predict outcomes and manage the business based on these predictions will gain important competitive advantages. Others run the risk of being left behind.
1 "Machine Learning." April 22, 2013, Stanford Online. Stanford University. Available at http://online.stanford.edu/course/machine-learning.
2 "Cheat Sheet: 5 Things Everyone Should Know About Machine Learning," by Bernard Marr, Sept. 16, 2016. Forbes. Available at http://www.forbes.com/sites/bernardmarr/2016/09/22/cheat-sheet-5-things-everyone-should-know-about-machine-learning/2/#2e85d8013051.