For years, the retail industry has been talking about the positive effects of tools like personalization, customer segmentation, automated marketing, and in-store analytics. And, with every year, new technologies make it a bit easier to use these tools to target the right buyers, create relevant experiences, and make the buying process a dream.
This is evidenced by steady retail sales growth at an average rate of 5.1% annually over the past decade, reaching $21.8 trillion in 2024. (The pandemic, while briefly disrupting patterns in 2020, reinforced the value of flexible, tech-driven solutions.)
Recently, however, retail companies implementing machine learning and AI-powered tools have seen far greater gains. Between 2023 and 2024, these businesses reported a 14.2% sales and profit increase, compared to just 6.9% for those sticking to traditional methods.
The message is clear: implementing machine learning technology into retail operations offers a chance to move beyond incremental gains and achieve substantial growth. This article will cover the benefits of adopting machine learning tools and provide use cases to help grow your retail business.
Table of contents
Benefits of machine learning in retail
Integrating machine learning into retail offers several benefits, including the following:
Better customer experiences
Machine learning tools improve customer experiences by analyzing behavior, preferences, and shopping patterns to create personalized journeys. From delivering product recommendations that align with individual tastes to optimizing search results based on intent, these tools make every interaction more relevant.
For example, British grocery chain Tesco uses machine learning to suggest healthier food options to its customers. By examining purchase history, Tesco can identify patterns, like frequent purchases of sugary snacks, and offer alternatives like low-sugar or high-protein items.
The same principle applies to digital retail. By understanding individual preferences, machine learning tools can automatically (and instantaneously) adjust search results, promotions on digital signage and in-apps, and notifications to match what a particular customer is more likely to need or want.
💡Pro tip: Shopify brings together every interaction you have with a customer in a unified customer profile. From the loyalty points they’ve earned online to the conversations they’ve had with sales associates in-store, you get a 360-view of your shopper to tailor their experience—no matter where they shop.
Increased sales with demand forecasting
Machine learning is highly effective at predicting consumer behavior, especially when it comes to demand forecasting—a critical driver of sales.
Take a fashion retailer preparing for a seasonal launch. Machine learning technology can analyze historical sales data, social media trends, and search engine activity to spot emerging patterns. For example, if searches for pastel dresses surge in the spring, the system flags this trend and recommends stocking more of these items ahead of time.
As sales progress, the algorithm monitors performance in real time, identifying new opportunities to boost revenue. If pastel dresses sell out faster than anticipated, the system can adjust restocking schedules and suggest adding complementary items—like pastel scarves or shoes—to further increase average order value. These real-time, adaptive insights allow you to stay ahead of demand.
Lower costs
For businesses selling seasonal products (like outdoor gear, for example), balancing inventory and merchandise can be a costly challenge. Overstocking drives up storage expenses, while understocking results in missed sales opportunities. Machine learning addresses these inefficiencies by delivering precise, data-driven inventory recommendations.
For example, Shopify’s machine learning platform, Merlin, analyzes things like historical sales, weather data, and holiday patterns to identify buying trends. If it identifies that waterproof tents are popular during spring but taper off by late summer, the algorithm adjusts procurement schedules accordingly.
This minimizes overstock and markdown expenses so that inventory levels align with the real demand. In turn, you significantly reduce operational costs by reducing excess storage and wasted resources.
Better decision-making
Machine learning also empowers retailers to make smarter, faster decisions by analyzing vast amounts of data and uncovering patterns that might otherwise go unnoticed. This leads to smarter, data-driven decisions that directly impact business performance.
For example, machine learning can:
- Identify ideal pricing strategies through dynamic pricing models
- Improve customer targeting by personalizing marketing campaigns
- Enhance product recommendations to boost cross-selling opportunities
Machine learning allows businesses to respond quickly to market shifts and customer preferences by automating analysis and providing real-time recommendations.
But for it to work effectively, you’ll need a commerce operating stack that unifies your front- and backend data and operations. This approach reduces data silos without the need for a complex technical infrastructure that unifies your data in one place.
Shopify does it all for you as standard—and it’s proven to help retailers benefit from an 8.9% boost in sales and a 22% reduction in total cost of ownership, on average.
10 AI and machine learning use cases in retail
We’ve talked about the benefits of integrating machine learning in retail, but where and how is machine learning most useful? Here are 10 of the most popular machine learning use cases in retail.
1. Fraud detection
Fraud detection is the process of identifying and preventing deceptive activities like fake transactions, account takeovers, or payment fraud. These cybercrime issues cost merchants an estimated $9.5 trillion every year.
Machine learning helps in fraud detection by analyzing transactional data, spotting unusual patterns, and flagging them as potential fraud. Algorithms continuously learn from new data, improving their accuracy in identifying behaviors that don’t align with legitimate activity.
More specifically, a retailer could use machine learning to monitor credit card transactions. For example, if a customer typically shops in New York but suddenly makes high-value purchases in a different country, the system might flag the transaction for review. This protects the retailer’s customers' accounts while minimizing disruptions to valid purchases.
2. Semantic search enhancements
Semantic search uses machine learning to focus on the intent and context behind a user's query, instead of simply matching keywords. This makes it possible to deliver highly relevant product results, aligning search outcomes more closely with what customers want.
In a real-world example, Shopify integrated AI-powered search capabilities in their storefronts to better interpret customer intent. How? Using embeddings—numerical vectors representing textual and visual content—to process millions of data points daily. These embeddings enable the system to understand the context of a search query and return the most relevant product results.
Real-time embedding pipelines update product data immediately after changes are made, allowing you to provide up-to-date and context-aware search experiences.
3. Personalized product recommendations
Advanced machine learning algorithms can analyze customer behavior, purchase history, and browsing patterns to suggest products that match individual preferences. By delivering these valuable insights directly to sales staff or online platforms, retailers can create a shopping experience that is unique to each customer.
Fashion retailer Diane von Furstenberg (DVF) used this kind of personalization and transformed its clienteling process with Shopify POS. Shopify brought DVF data into one platform and helped them unify customer purchase history, preferences, and stylist notes in one place.
With this data, personal stylists can deliver customized product recommendations and proactive communications (such as notifying customers about back-in-stock items or exclusive events). These changes have improved customer relationships and increased sales.
“If we weren’t using Shopify POS, I think our team would have a harder time building rapport with our customers and staying in touch with them,” says assistant store manager Joanna Puccio. “With Shopify, my staff can log customer information in one place and refer to it whenever they need. They can reach out to clients for things they’ll actually be interested in, which makes our customer experience feel so much more curated and personal.”
4. Revenue prediction
Nobody knows what the future holds. However, machine learning models give you a more accurate estimation. It can analyze historical data, seasonal trends, and market indicators to forecast future revenue with remarkable precision.
By identifying patterns and relationships within the data, these machine learning systems provide valuable insights that help retailers plan anything from inventory and staffing to marketing and sales strategies more effectively.
For example, a retailer could use machine learning to anticipate revenue spikes during the holiday season. By processing past sales data and considering factors like customer behavior trends and marketing campaign performance, the model could predict the expected revenue for Black Friday or Cyber Monday. This helps the retailer to adjust stock levels, prepare promotions, and allocate resources to handle increased demand.
5. Customer segmentation
Personalization is more than a nice-to-have for modern consumers—some 70% say it’s important to interact with employees who know who they are.
AI-powered customer segmentation tools help cater to these preferences by analyzing behavior, preferences, and purchase patterns to create detailed shopper profiles. This helps businesses design more targeted marketing campaigns and deliver personalized experiences that resonate with specific customer groups.
Airsign, a sustainable vacuum brand, is an example of a brand that used Shopify's segmentation tools to optimize its operations and marketing. It identified three core customer segments:
- Design-conscious urban shoppers
- Millennials transitioning to suburban living
- Older customers valuing high-quality appliances
Using this data, Airsign ran a marketing campaign to target early buyers who couldn’t subscribe to their filter replacement program at launch. By offering this group a custom discount, they saw a 30% conversion rate.
Segmentation also revealed inefficiencies in Airsign’s shipping model. The retailer identified rising costs from increased demand in distant regions and shifted to a bi-coastal fulfillment strategy. This change reduced shipping expenses by 25%, allowing the brand to maintain competitive pricing while improving service efficiency.
6. Automated marketing
Machine learning has transformed marketing by making it smarter, more targeted, and more efficient. Automated systems use data like customer behavior, purchase patterns, and engagement history to personalize content, timing, and channel selection for each individual. The result is marketing campaigns that connect on a deeper level and drive better outcomes.
A great example is iTokri, an online store for Indian handicrafts. By using Shopify’s Launchpad, iTokri automated key marketing and sales tasks, such as:
- Scheduling personalized sales events and creating targeted campaigns for returning customers
- Streamlining post-checkout options, like gift wrapping and shipping to multiple addresses, making the shopping experience more convenient
These automations also extended to email and SMS campaigns, helping iTokri personalize messages for specific customer segments. By implementing these features, iTokri increased its returning customer rate by 42% and achieved a 91% year-over-year growth in international revenue—all while reducing administrative workload by 50%.
7. Inventory management
Inventory management has long been a point of contention for retailers. However, predictive machine learning algorithms can analyze sales trends, seasonal patterns, and inventory turnover to ensure stock levels align with demand. This reduces the risks of stockouts or overstocking—both costly challenges for retailers.
Twinkle Twinkle Little One, a Chicago-based baby and child boutique, is an excellent example of improved inventory practices with machine learning. Their previous system required manual reconciliation between online and in-store inventory, leading to frequent overselling and frustrated customers.
By unifying their sales and inventory data on Shopify, they eliminated manual adjustments and achieved real-time synchronization between all locations. This newfound visibility allowed them to identify high-margin and fast-moving items, optimizing their inventory mix and driving a nearly 80% year-over-year revenue growth.
“The time we’ve saved that would have otherwise been spent managing operational overhead is huge,” owner Corey Glasson says on the brand’s migration to Shopify. “That’s time we can invest into actually growing the business. With Shopify, we’re able to be proactive and build for the future.”
8. Price optimization
There are a multitude of factors that contribute to a product’s sale price: competitor prices, the manufacturer’s recommendation, and consumer demand. Machine learning systems analyze these factors—market trends, competitor pricing, and customer demand—to dynamically adjust prices. This helps retailers maximize profitability while staying competitive.
Machine learning algorithms work by processing vast amounts of data in real time, so that businesses in the retail industry can react quickly to changing conditions and refine pricing strategies for different products or regions.
For example, a retailer selling seasonal apparel could use machine learning to automatically adjust prices as demand fluctuates. During a sudden heatwave, the algorithm might increase prices on summer essentials like lightweight clothing and sunglasses due to higher demand, while offering discounts on off-season items like winter coats to clear inventory.
Similarly, the system could monitor competitor pricing for similar products, so that prices remain attractive—without eroding profit margins.
9. Customer service automation
Providing fast, accurate answers to common questions is a cornerstone of great customer service. This is where AI-powered automation shines.
Chatbots and virtual assistants handle repetitive inquiries—like “Where’s my order?” or “What’s your return policy?”—around the clock, so that customers get immediate support. These systems continuously learn from interactions, improving their accuracy and efficiency over time.
For example, if a shopper wants to track their package, Shopify Inbox can instantly retrieve and share the latest shipping details, eliminating the wait for a human agent. If another customer is searching for a product, the bot can guide them to the right section of the website or recommend alternatives when items are unavailable.
10. In-store analytics
While online retail has long benefited from data-driven insights, physical stores are now catching up with the help of machine learning and computer vision. These retail technologies bring the same level of precision to brick-and-mortar locations, enabling retailers to understand consumer behavior data in ways that were previously impossible.
Amazon Go grocery stores are a well-known example of how computer vision can transform in-store analytics. These physical stores use things like advanced machine learning, IoT technology, and footfall to track customer movement, interactions, and foot traffic patterns.
This data provides Amazon with actionable insights that improve the shopping experience and store operations. For example, if a product is frequently picked up but not purchased, Amazon can analyze why—perhaps adjusting its placement or offering a promotion.
The system also tracks which areas of the store see the most traffic, helping optimize layouts and prioritize high-visibility product placement.
Grow your retail business with Shopify’s AI-powered solutions
If you're looking to achieve the 14.2% growth reported by businesses using machine learning, start by integrating AI-driven tools into your operations.
Shopify offers solutions like customer segmentation, predictive inventory management, and automated marketing campaigns to help businesses make smarter decisions and act on data with precision.
Using real-time insights and advanced machine learning algorithms, Shopify helps retailers adapt to customer needs, improve processes, and drive sustainable growth. Get started today.
Machine learning in retail FAQs
What does ML mean in retail?
In the retail industry, machine learning (ML) refers to using machine learning algorithms to process customer data, identify trends, and make data-driven decisions that improve operations and customer satisfaction.
What’s the difference between machine learning and AI?
While you'll often hear "AI" and "machine learning" used interchangeably in retail, they serve different purposes. Artificial intelligence is the broader ecosystem of smart retail technology, while machine learning is a specific component that focuses on analyzing patterns in data to make predictions and improve over time.
In practice, machine learning powers specific functions like demand forecasting and price optimization, while the broader AI ecosystem includes technologies like computer vision for store monitoring, natural language processing for customer service, and robotics for warehouse automation.
How is machine learning used in retail?
Machine learning helps retailers analyze customer data, forecast demand, and optimize inventory. It’s used for pricing strategies, personalized recommendations, and improving supply chain efficiency.
How does Walmart use machine learning?
Walmart uses machine learning to improve inventory management, predict customer preferences, and improve in-store and online experiences. Its AI-driven systems analyze sales patterns and suggest operational adjustments to meet demand. Walmart also uses machine learning in its Intelligent Retail Lab (IRL), where AI-powered cameras monitor inventory levels, keeping shelves stocked so customers can find what they need.
How is AI used in stores in the retail industry?
AI supports retail stores by automating tasks like shelf monitoring, checkout systems, and customer support. It also provides insights for targeted marketing and improves product placement to boost sales.
