Machine learning in retail: use cases,
examples & adoption guidelines

ML-powered recommendation engines serve as a digital counterpart to human sales assistants, providing personalized recommendations to help customers navigate the vast product catalogs of modern ecommerce platforms. First, a recommender engine analyzes personal data and consumer behavior (past purchases, browsing history, product reviews, etc.), along with product information and contextual parameters, such as broader sales trends. The system can then recommend items already purchased by other customers with similar attributes and buying patterns (collaborative filtering), offer items similar to those already bought by the customer (content-based filtering), or combine both mechanisms (hybrid approach).

Amazon’s engine is one of the most advanced recommender systems. The solution has recently been enhanced with generative AI and large language models to further personalize the descriptions of each recommended product based on customer preferences, highlighting the features that can be more relevant to a certain user.

Contextual, semantic & visual search engines

Unlike traditional search engines that match keywords in search queries with those in product descriptions, contextual search solutions powered by ML and Generative AI, including natural language processing or NLP, can understand user intent behind a query based on contextual factors and consequently provide more relevant results. Such factors can include user location, time of the day, or search and purchase history. ML-enabled semantic search engines consider contextual clues, as well as the relationships between words, phrases, and concepts to better interpret synonyms, ambiguous terms, or other implications. For instance, they can list running shoes for queries containing “athletic footwear”, or prioritize affordable laptop models for students. A good example of this is the US department store chain Macy’s, which adopted Google Cloud for Retail to provide online store visitors with contextually relevant search results.

Computer vision-powered visual search solutions enable customers to use a product image, either uploaded or captured live with a camera, as a search query to find visually similar items available on a retail website. Major retailers have already integrated this technology into their online platforms, including Amazon, H&M, and IKEA.

AI chatbots & virtual shopping assistants

Many retail businesses rely on conversational AI to automate and scale their sales and service operations, while providing customers with fully personalized shopping experiences and round-the-clock assistance. Originally designed as distinct types of solutions, the most advanced AI chatbots and virtual shopping assistants powered by GenAI now boast a broad and nearly overlapping range of capabilities, making them valuable across multiple functions. For instance, they can provide ecommerce website visitors with personalized recommendations based on recommender engines’ output, send reminders to facilitate shopping cart recovery, and gather customer feedback on new products. Furthermore, these tools can automate a variety of customer support activities, from providing order status updates to assisting buyers with product returns.

ThredUp, a major online resale platform specializing in secondhand clothing, has recently incorporated an AI-powered bot named Style Chat into its website. The chatbot provides personalized outfit suggestions based on visitors' prompts, such as suitable outfit options for a summer wedding.

Dynamic pricing

To predict the impact of demand fluctuations and suggest price changes which maximize profit while minimizing the risk of churn, machine learning systems can take into account multiple parameters that would be difficult to track manually. For example, they can search the web for prices of similar products offered by competitors, promotional initiatives in place, and the pricing history of particular products, or consider broader market trends and their underlying drivers such as product demand compared to supply. These AI-driven tools can also identify the lowest-priced items in their respective categories to help retailers set up a markdown pricing strategy and get rid of obsolete merchandise at the end of a season.

While Amazon and other ecommerce platforms have pioneered this approach, dynamic pricing is now gaining traction among traditional brick-and-mortar businesses, with chains like Best Buy and Kroger adopting electronic tags to display and adjust prices in real time.

Contextual commerce

Some social media platforms incorporate contextual commerce features powered by machine learning algorithms to recognize products in online content and highlight them with image overlays, enabling users to purchase them with a simple click.

The shoppable media technology company AiBUY, for instance, built a video ecommerce platform with product recognition capabilities powered by neural networks. When the system detects an item within an uploaded video, it compares it to merchandise available in partners’ online catalogs to identify the closest match, allowing viewers to quickly reach the corresponding product page via an embedded link.

Demand forecasting for inventory management

Stockouts and overstocks cost retail companies billions of dollars a year worldwide. To optimize their inventory levels and better balance product supply and demand, many retail businesses are turning to predictive analytics systems powered by ML algorithms. Such tools can forecast demand trends based on factors like seasonality, holidays, pricing, and promotions. Combined with computer vision-powered cameras, predictive analytics software can complement information from digital sources (social media, ecommerce platforms, etc.) with data points collected in physical stores, such as foot traffic patterns. This helps retailers better assess which products are capturing customers' attention and fine-tune their inventory restocking plans and product placement strategies accordingly.

For instance, Walmart adopted a ML-powered inventory management system that can analyze historical sales data from both physical and digital channels to forecast future demand, helping adjust stock levels across distribution centers and stores. The solution also considers macroeconomic trends, local demographics, macroweather patterns, and in-store customer interactions to provide more accurate predictions.

Delivery optimization

Delivery is another key aspect of supply chain management that can be enhanced with machine learning technology. For example, ML-based software can factor in customer preferences, including same-day delivery, or cross-check those preferences with driver and vehicle availability to recommend the best shipping options and optimize delivery schedules accordingly. These systems can also be used to dynamically adjust routes according to traffic conditions or to predict sales trends by geographic area and thus store popular items in the right warehouse, minimizing transportation times.

When it comes to optimizing deliveries with AI, Amazon is probably the first example that comes to mind. A decade ago, the company patented an anticipatory shipping system that uses ML algorithms to predict customers' future orders based on past purchases, wish lists, and other factors. This enables Amazon to send products to a closer hub before customers actually purchase them, making deliveries faster and cheaper. More recently, Amazon implemented Wellspring, a generative AI-based mapping system that combines data from satellite imagery, previous deliveries, or other sources to create more accurate maps for its drivers than those available on navigation apps. For instance, the solution can specify the location of an apartment in a multi-building complex and recommend the best access point.

Checkout automation

Cashierless stores represent one of the most promising, yet controversial, fields of applications of ML and artificial intelligence in retail. Transforming stores into smart retail locations, where customers can shop without having to interact with a cashier seems to be beneficial for both retailers and consumers. Instead of traditional checkout lines, these stores utilize AI technologies such as ML-powered computer vision solutions to automatically track purchases and process payments. Specifically, ML systems can monitor customers picking items from shelves, identify products and add them to the respective digital carts, and charge shoppers as they leave the store.

When adopted in small retail environments, cashierless technology has proven rather effective. For instance, Poland’s largest convenience store chain Żabka has expanded its service offering with 24/7 cashier-free minimarkets called Żabka Nano, enabling customers to complete their grocery shopping in an average of 60 seconds. So far, however, not all implementations have gone as planned. Amazon has recently scaled back its cashierless grocery store chain Amazon Go and discontinued the “Just Walk Out” technology at its largest Amazon Fresh supermarkets, amid accusations of hiding the true extent of human intervention in its ML-based operations

Fraud detection & video surveillance

ML-powered fraud detection systems enable retailers to identify anomalous customer behavior, which can indicate criminal activity, and thus prevent unauthorized transactions. For example, a machine learning solution can spot a sudden increase in the frequency of credit card payments, particularly for luxury goods, which is a common sign of credit card fraud. It can also spot suspicious login events, such as from an unusual IP address, to detect account takeover. Major online retail platforms like eBay and Alibaba have been using this technology for years, with a growing focus on deep learning and neural networks.

ML-enhanced video surveillance takes a similar approach, using anomaly detection to identify unconventional movement patterns. Japanese tech company Vaak, for instance, built an AI solution designed to catch shoplifters in action and alert store managers. To maximize detection accuracy, Vaak's team fed machine learning algorithms with 100,000 hours of surveillance data portraying over 100 behavioral aspects, including hand movements, facial expressions, gait, and clothing choices.