Facebook uses machine learning (ML) for face recognition, Apple uses it to make Siri sound more human, and Google minimizes energy use at data centers with the help of ML. Machine learning is a trendy element of artificial intelligence that is being successfully used in many industries. Regardless of the industry your business operates in, consider using ML to improve productivity and receive higher ROI.
What is machine learning?
In traditional computer science we need to explain the task we wish to accomplish to the computer. For example, if we plan on creating a tool that calculates salaries, then we need to write a program that translates to the computer how to perform each operation in a language that it understands. This limitation hinders innovation because the machine cannot find and fix problems on its own, which can require much more energy from a development team.
Machine learning could simplify this process by a lot, as it involves “teaching” computers to learn on their own. This is done via an algorithm which educates computers to perform a task without having the developer explicitly code “instructions” in the program. In the supervised learning technique, the processor studies previous examples in order to run a machine learning algorithm. The more data is processed, the more accurate the algorithm becomes.
The Royal Bank of Scotland has launched Luvo earlier this year. It is a bot that can answer customers’ questions and perform money transfers. It uses machine learning to provide customers with continuously better responses over time. McKinsey points out that some European banks that use machine learning techniques saw a 10% increase in sales of new products, 20% increase in cash collections, and a 20% decline in churn.
Dataminr transforms tweets into actionable signals for stock traders. It classifies them based on location, relevancy, and ranks them by their levels of urgency. An alert sent to a trader even a couple of minutes earlier can result in a significant profit.
SailThru learns customers’ interests and purchase behaviors. It predicts when a customer will make a purchase. For its client, The Clymb, it increased the total email revenue by 71%. It also collected data from all digital channels. This predicted top buyers’ next actions, and identified marketing trends.
A startup from London helps its customers generate reports. The company’s technology can scan text documents and establish relationships between concepts. It has increased workers’ productivity by 25% and saved 40 hours of engineers’ work per month.
Machine learning is not limited to the aforementioned case studies. It can be used in fraud detection, cybersecurity, search, manufacturing, medicine, robotics, personalization, and other industries. To implement machine learning technologies, one needs a strategy and a deep understanding of a company’s KPI’s.
If you plan to use ML in your business, define your criteria of success. It is not enough to know what your customers are about to do; the most important thing is to understand some of the reasons behind their behavior.