The Impact of Machine Learning Across Industries: Spotlight on Retail

Automating processes while improving the customer experience

Introduction

Machine Learning continues to draw more attention and interest from organizations that need to make quick, accurate, and automated decisions based on the many data points available. Accuracy requires creating models that must be trained, validated, and tested using vast amounts of data before deploying them to a production environment.

For example, financial institutions need automated decisions to speed the processing of loan applications while eliminating human error and bias. These solutions should use every available piece of data about the applicant to make fast, accurate decisions that minimize risks, maximum repayments, and scale to process thousands of applications each day. While this article is focused on retail, here are some examples of ML success stories.

  • Retail - 35% of all Amazon sales come from ML-driven product recommendations.
  • Entertainment - 80% of all programs streamed on Netflix are due to the entertainment giant’s personalized, ML-driven content recommendations.
  • Healthcare - ML helps researchers diagnose more rapidly and accurately using medical diagnostic and digital medical imaging data (X-rays, MRIs, and CAT scans).
  • Financial/Retail (and Others) - ML can fight fraud in many ways, not only by detecting suspicious transactions but using sophisticated video, facial, and handwriting recognition algorithms.
  • Education - ML can detect subtle changes in a student’s performance and recommend tutoring, remediation, or counseling to increase their likelihood of staying on track.
  • Consumer-Oriented Apps - ML even powers apps like those that analyze data from video cameras and doorbells for security or the contents of your refrigerator to find the perfect recipe for you!

The retail industry—especially e-commerce retailers—has some compelling opportunities for benefiting from machine learning (ML). As in other sectors, however, retailers either aren’t aware of these opportunities or—more commonly—they simply don’t understand or have the expertise to take advantage of them. (Not every retailer is an “Amazon” with seemingly unlimited resources.)

At JBS Custom Software Solutions, we’re firm believers in helping retailers tap into the opportunities ML offers. Yet we’ve seen reluctance and even trepidation when it comes time to dip their toes into the water. Part of the problem is a lack of proper understanding of what ML is. Another is that there is simply no one-size-fits-all approach to implementing it in the enterprise, making it difficult to get started.

By 2020, 30% of retailers had deployed some form of machine learning and AI to improve their online product recommendations and another 29% were planning to in the near future. 48% planned to use ML to boost customer engagement online and/or in-store.
— Source: Statistica

When people think of ML—or artificial intelligence (AI) for that matter— they think of a silver bullet you just “turn on,” one that will suddenly solve all manner of problems and make them lots of money. Not instantly or all by itself, of course. But they believe machine learning will tell them precisely what they need to do or give them some critical information they’d not ordinarily have. They expect some magical insights to act on that will explode their business in a good way.

Indeed, once you reach a certain level of maturity with machine learning, it can provide insights that you can then use to automate processes, improve the customer experience, and make more accurate and timely decisions. But as is the case with most silver bullets, getting started with ML is not quite as simple as flipping a switch, to say nothing of deploying, refining, expanding, and maturing it.

ML vs AI

Wikipedia describes machine learning (ML) as “the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data,” in order to make predictions or decisions without being explicitly programmed to do so.”

This may serve as a foundational working definition of ML, but it needs a bit of tweaking. Those new to the technologies often use the terms ML and AI synonymously, but they are not, in fact, the same. AI enables a machine to simulate human thinking and behavior. ML is a subset of AI which allows a computer model to learn from past data on its own—continuously, automatically, and without requiring regular programming to do so.

Getting Started

You can download our free White Paper Getting Started with Machine Learning in Retail at any time. By following the steps we’ve outlined in this paper, avoiding the pitfalls we described, and selecting a savvy partner, any retailer can start enjoying the benefits of ML sooner than later.

In part two of this series, we’ll discuss four ways machine learning can help optimize retail sales, marketing, and operations.

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