In 2021, it is not uncommon to hear the saying that “retail is dead”, and in part, it rings true: the old way of shopping has been long buried. Gone are the days of flipping through magazines, taking monthly trips to the mall, and even paying in cash. As companies have adapted new technologies and stayed on top of emerging trends, shopping experiences both online and in-store have integrated machine learning and artificial intelligence in every step. 

In the online setting, machine learning and artificial intelligence are used from browsing to checkout. To increase sales and capture customers, retailers use recommender systems to push new products to customers based on previously purchased or viewed items. They can do so on their site, and in promotional emails or newsletters, helping customers feel understood, and make them more willing to buy. Algorithms also help companies strategically price their products, searching the web for competitors’ prices, gathering data on what price point consumers are buying at, and predicting how much consumers will be willing to pay.

Both online and in stores, companies use technology to help predict consumer behavior. Gathering detailed data on transactions, from product type to date and time purchased, allows retailers to refine marketing strategies. Machine learning and AI help to answer questions like: which day of the week are potential buyers most likely to visit websites? How often do customers make transactions in person vs. online? Are consumers more likely to purchase around their birthday or a holiday? In all cases, algorithms are used to track and analyze this data to help companies make predictions. 

Companies also use “chatbots”: algorithms that simulate human interaction to make users feel closer to a real store and build consumer trust. Chatbots and virtual assistants “ask” questions of users to help recommend products, answer questions, and direct them to other areas of the site, gathering data at every step.

When buyers are able to come in stores, technology plays no lesser of a role. Managers use the same consumer behavior prediction models to anticipate when to restock shelves, how to best staff their store, and know which items will be in high demand. Computer vision algorithms play a crucial role as well, and enhanced video analytics provide insights that online retail cannot. Features like facial recognition can identify the customers’ ages, and gather data on when certain age groups are most likely to shop, and what for. Algorithms can detect things like walking patterns, the direction of customers’ gaze at displays, and from there help retailers optimize store layouts and product placement.

From predicting whether buyers will purchase on their birthday, to tracking how long they will look at window displays, computer systems have become intertwined in retail. As data is gathered at every step of the purchasing process, companies can fine-tune marketing strategies and optimize shopping experiences. Smart shopping can ease customer experiences, drive up engagement, and bring in revenue for companies. In short, high-tech retail is far from being dead.