Data Analytics

3 Ways Machine Learning Is Changing Retail for the Better

Want more personalized customer journeys and better inventory management? Consider machine learning.

Autumn is an associate editorial director and a contributor to BizTech Magazine. She covers trends and tech in retail, energy & utilities, financial services and nonprofit sectors.

Customers want convenience; retailers want growth. Machine learning can help with both.

Artificial intelligence has revealed its potential in retail, as anyone who has ever interacted with a digital sales assistant knows. In fact, the value of AI in retail is projected to reach $46 billion by 2032, according to a recent report.

But often, when we say, “artificial intelligence,” what we really mean is machine learning. Machine learning involves algorithms that computer systems rely on to execute infinitely complex tasks and continually evolve their outputs by data, training, and experience over time.

Machine learning is revolutionizing retail for all stakeholders: customers, retailers, and employees. It supercharges customer experience while simultaneously tackling logistical issues such as inventory and shrinkage, halting frustrations at every point along the retail journey. Here’s how.

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Giving Consumers a Personalized Shopping Experience

Nearly three-quarters of consumers expect personalized experiences, according to research from McKinsey. And when they don’t get it, 76 percent say they’re frustrated.

Enter the power of machine learning. These sets of rules and algorithms analyze tremendous amounts of customer data, both on a macro level (how people who enter a retail website through Facebook interact with the site versus those who enter organically, for example) and on a micro level (how a particular Facebook user interacts with the site). With that range of data, machine learning can provide customized recommendations that give customers the sense that their preferences are understood and accounted for. This, in turn, helps build loyalty. It’s a win-win proposition: Customers leave satisfied, with the items they planned to purchase along with a few “surprise and delight” items that have been curated for them. The result? Retailers increase their sales.

$46 billion

The amount that artificial intelligence in retail is projected to reach by 2032

Source: precedenceresearch.com, “Artificial Intelligence in Retail Market,” Feb. 12, 2024

A Solution to Stockouts

Retailers struggle with inventory management, chronically trying to balance the risk of overstocking or understocking. Machine learning supports retailers through a combination of stock monitoring across locations, order processing across purchasing channels, automated reordering and customer notifications. This helps manage stockouts and the inevitable frustration that customers experience when faced with them.

But machine learning’s capabilities enable it to go beyond real-time inventory management and enter the realm of forecasting. With AI-supported forecasting, systems can analyze everything from historic sales data to weather to current events to predict optimal stock levels. When machine learning is applied to supply chain management forecasting, errors are slashed by up to 50 percent, which in turn triggers a decrease in lost sales and product unavailability of up to 65 percent, according to McKinsey.

The result: recouped costs from reduced errors, lower incidences of overstock and stockouts, a smaller storage real estate footprint and associated costs, and greater customer satisfaction.

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Reduce Theft, Fraud and Other Forms of Shrinkage

Predictive analytics is the machine-enabled key to reducing theft, fraud and other forms of shrinkage. By scanning customer behavior and identifying what standard, good-faith transactions look like — and what deceptive transactions and other behaviors look like — the pattern recognition capabilities of machine learning can help retailers spot theft and fraud in real time.

Machine learning can also play a role in improved Internet of Things devices. Advanced video surveillance, face recognition technology and even radio frequency identification technology all see their capabilities grow when paired with the algorithms that drive machine learning.

In an era when criminals themselves are leaning into machine learning, it’s important for retailers to keep pace. “The real short-term challenge is the degree to which AI can be an accelerant of successful attacks,” Buck Bell, head of CDW’s global security strategy office, told BizTech in 2023. Algorithms get more effective as they go, but this holds true for criminals as well as retailers.

The real-time insights enabled by predictive analytics have the potential to empower retailers in a relatively flat sales environment. Loss mitigation, inventory support and tailored customer experiences not only safeguard profit margins but also foster the kind of dynamic, customer-forward retail ecosystem that will see growth in the months and years to come.

miakievy / getty images