June 14, 2016
What retailers need to know about machine learning
By Meghan Mabey
Machine learning isn’t an entirely new science, but it’s gained fresh momentum recently. Machine learning is constantly changing and improving, allowing businesses to better predict what products customers will want. Better predictions mean higher revenue and, overall, a better customer experience that will keep customers coming back.
New advancements in the field of machine learning mean that retailers, brand owners, and managers of brick-and-mortar stores can use the same technology that Amazon and other e-commerce giants use. These online giants use the technology to offer personalized recommendations to consumers on their websites; now brick-and-mortar retailers can do the same with physical products, for a completely unique and customized in-store shopping experience.
What is Machine Learning, Exactly?
Machine learning describes the science of enabling computers to act without explicitly telling them to do so. It works by developing and training an algorithm to make predictions on data and independently adapt as it is exposed to new data. Way back in 1959, Arthur Samuel, an American pioneer in the field of machine learning, artificial intelligence and computer gaming, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.”
Fast forward to 1997, and Tom Mitchell, computer scientist and Chair of the Machine Learning Department at Carnegie Mellon University, offers his own definition of machine learning: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
So, as a simple example, if you want your program to predict future traffic patterns at a specific intersection (T), you can apply a machine learning algorithm that uses data about past traffic patterns (E), and, if it is successful, the program will be able to better predict traffic patterns in the future (P).
How You’ve Used Machine Learning
You’ve probably already heard plenty about machine learning, and have likely even used it, although you may not have realized it at the time.
The product recommendations you receive from Amazon when you’re shopping online? Machine learning.
The highly-publicized, self-driving Google car? Also an example of machine learning.
Other common types of machine learning include practical speech recognition, effective web searches, and Google's ability to categorize emails as “Spam” or “Important” in your inbox. To help us better understand the process of teaching machines to provide reliable and valuable predictive data, AnalyticsVidhya.com created this helpful infographic, which breaks down the process of machine learning into three parts:
Machine Learning in the Retail Industry
How does this all relate to you and your business?
More and more, customers want personalization and interaction in their shopping experience, and machine learning can help make this happen. Let’s take a practical example of machine learning in the retail industry. As a retailer, you want to better connect with your customers and create a more personalized in-store shopping experience, with the goal of making your customers happier and ultimately improving your business.
Here are some ways that machine learning can help achieve this goal:
Machine learning can create data insights to accurately predict which products customers will want in the future, based on their past behavior and on the choices of other customers.. This predicted data can be used to offer more of the highly-desired products to the right person, at the right time.
Machine learning can also help determine which product categories are more frequently purchased together in a store, data that can be used to develop consumer incentive programs and targeted marketing campaigns.
Machine learning can help predict inventory needs accurately, and can be used to identify which buying circumstances cause products to sell more quickly and which behaviors or offers promote additional purchases.
Machine Learning Works for Retail
A huge amount of digital data is generated on a daily basis. Machine learning allows computers to analyze high quantities of data without human intervention, making business operations exponentially more efficient.
With machine learning, brick-and-mortar retailers can offer customers the same kind of interactive shopping experience in their stores, as big e-commerce brands like Amazon are able to offer online.
Machine learning is the technology that powers intelligent, automated, and responsive in-store software. By predicting what customers want ahead of time and offering them an interactive shopping experience with personalized recommendations, retailers can increase revenue, improve the efficiency of their operations, and improve customer satisfaction – the three keys to a successful business.