Machine Learning in Retail in 2025: Best Real-World Use Cases

Machine Learning (ML) in retail today is a real advantage, allowing companies to gain a clear edge over competitors stuck with manual or outdated systems. Retailers use ML to analyze buying behavior, adjust pricing in real time, optimize inventory levels, and prevent fraud. As a result, retail machine learning helps to understand customers better, automate operations like product assortment planning, demand forecasting, replenishment scheduling, and customer segmentation, and boost revenue.

Cleveroad has been building tailored digital solutions for the Retail industry since 2011. We help retail businesses apply advanced data-driven technologies, including ML and Artificial Intelligence (AI), that match their processes and goals. Whether your priority is inventory accuracy or product recommendations, our team brings intelligent automation into action with practical results.

Based on our experience, we’ve prepared a comprehensive guide on machine learning in retail industry. Reading our article, you’ll learn:

• What retail machine learning means, how it works, and what benefits it delivers to your business
• Top 10 machine learning use cases in retail that help companies drive personalization, cut costs, and improve operational efficiency
• How to implement machine learning in your retail business, with a full breakdown of each stage, from business case to launch and scaling
• Real-life examples of AI and ML in retail, showing how industry leaders apply them in practice
• Challenges you may face during ML adoption and how to overcome them with the right approach

Machine learning is a type of artificial intelligence that enables systems to learn from data and improve automatically. Instead of following static rules, ML algorithms find patterns in historical and real-time data, then use those patterns to make predictions or decisions without manual intervention.

Machine learning in retail processes large volumes of customer, sales, and inventory data to help businesses act smarter. ML helps analyze massive amounts of customer behavior, forecast demand trends, and optimize operations to improve efficiency and satisfaction.

Retailers apply ML technology across critical areas of their operations. It enables them to deliver personalized promotions based on individual shopping habits, anticipate stock shortages before they happen, adjust pricing in real time based on current demand, and detect suspicious patterns that may signal fraudulent transactions. Now, let’s break down the key retail machine learning business benefits.

ML helps identify what your customers truly want. By analyzing past purchases, browsing history, and even time of day, you can predict which offers or products are most relevant to each individual. This allows you to serve personalized product recommendations, increasing satisfaction and loyalty without annoying users with irrelevant messages. Moreover, studies show that 80% of consumers are more likely to purchase from brands offering personalized experiences (Source: McKinsey).

Retailers face constant pressure to do more with less. ML algorithms automate stock management, detect wasteful processes, and help reduce overstock and out-of-stock risks. For instance, predictive inventory systems can calculate how much stock is needed at each store, helping reduce warehousing costs and improve shelf availability. According to McKinsey, businesses that use predictive analytics typically boost their revenue by 5–10% while cutting operational costs by 20–30%.

Instead of relying on intuition or manual reports, machine learning gives your team real-time insights based on data. You can forecast sales volumes, spot shifts in customer demand, and react quickly to market changes. This speeds up planning, reduces guesswork, and empowers smarter business decisions. With time, these faster, more accurate decisions compound into significant performance gains across the entire retail chain.

ML analyzes which product combinations work best, what layout improves conversions, and how promotions affect customer behavior. With this insight, you can optimize pricing, upsell more effectively, and reduce churn. MarketUS found out that 62% of top retailers cited enhanced customer behavior insights thanks to ML. Every decision becomes grounded in real data, which means better outcomes at scale.

Machine learning in retail is used across a wide range of business tasks, from predicting demand and personalizing product recommendations to optimizing supply chains and detecting fraud. It also helps automate stock management, analyze customer sentiment, and fine-tune marketing strategies based on real behavior. These capabilities allow retailers to work smarter, reduce waste, and serve customers more effectively.

Let’s explore 10 practical use cases where AI and ML in retail help companies increase profits, reduce costs, and deliver superior customer experiences.

Fraud causes trillions in losses annually, and traditional rule-based systems like static fraud filters or basic transaction threshold checks often miss the warning signs. These legacy tools rely on predefined rules, which fail to adapt to evolving fraud tactics or recognize subtle behavioral changes. With machine learning retail solutions, you can detect complex anomalies in real time, such as unusual purchase locations, sudden changes in buying patterns, mismatched customer identity details, or multiple failed login attempts. ML identifies various fraud types:

• Payment fraud: Classifies risk based on IP, address, or purchase volume, blocking suspicious transactions instantly.
• Chargebacks: Flags suspicious refund patterns using behavioral scoring and clustering.
• Return fraud: Spots anomalies like frequent or high-value returns using time-series analysis.
• Promo code abuse: Uses Graph ML to detect bots, fake accounts, and abuse networks.

Pernambucanas, a major Brazilian retailer, tackled document-based fraud by developing an in-house solution called Documentoscopia. Powered by ML, the system automates the review of ID documents and extracts data at scale, analyzing three documents every two seconds. As a result, the company reduced manual fraud checks by 80%, cut onboarding time from hours to seconds, and improved overall customer experience without compromising security.

Customers don’t always search with the “right” words. Deep learning retail systems enable semantic search, which interprets user intent, a foundational technique behind many use cases and a very promising technology today. That means someone searching “jacket for cold rain” will see waterproof winter options, not just everything labeled “jacket.” It makes your product search feel intuitive and increases conversion.

In a real-world example, Grupo Casas Bahia, one of Brazil’s largest retailers, upgraded its marketplace search with Google Cloud’s Retail Search and Recommendations AI. As product volume scaled into the millions, the new system reduced catalog load time from 24 to 4 hours and supported over 7 million daily Stock Keeping Unit (SKU) updates. As a result, the company saw a 58% increase in search-driven conversions and a 28% rise in income per app user.

AI and machine learning in retail power personalized suggestions based on what a shopper viewed, added to their cart, or bought before. You can use this across email, on-site banners, or even in-store systems. Tailored offers improve basket size, repeat purchases, and overall customer satisfaction.

Big Sur AI, an AI-driven e-commerce platform, helps retailers deliver this kind of real-time personalization through its AI Sales Agent. The tool mimics in-store assistant behavior, offering product suggestions, answering detailed questions, and guiding shoppers with context-aware recommendations. As a result, merchants using Big Sur AI have improved online engagement, increased sales and conversion rates, and strengthened customer loyalty.

Machine learning for retail helps you predict revenue using historical sales data, seasonal demand, and external market signals. These insights guide budgeting, promotions, and inventory levels. Instead of relying on static spreadsheets or manual estimates, ML tools deliver continuous forecasts that adjust to real-time conditions, helping retailers avoid stock issues, plan resources better, and set realistic performance targets.

Unilever uses ML tools to analyze customer behavior, campaign performance, and market trends to fine-tune its sales forecasts. In one example, the company ran a multi-country campaign for Close-up toothpaste and used data-driven insights to adapt messaging in real time, reaching 500 million people and driving measurable engagement improvements.

Personalized marketing starts with understanding your audience. Segmentation tools based on machine learning group your shoppers based on real behavior, not just age or location. You can create targeted campaigns for VIP buyers, first-time visitors, or price-sensitive shoppers, each receiving relevant offers that convert better.

For example, Gainz, a leading Japanese home improvement retailer, used machine learning to segment demand patterns and enhance sales-planning accuracy. Their ML model analyzed massive sales datasets across 209 stores, identifying behavioral patterns down to individual product categories and store locations. This allowed Cainz to tailor inventory planning and promotional strategies to real-world customer segments, reducing stock issues and improving development speed. With parallel data processing via Cloud Run jobs, their team cut preprocessing time from 3 hours to just 50 minutes, enabling fast, adaptive planning at scale.

ML models define when, how, and what you should communicate to each customer. They analyze behavioral data to train generative AI components that craft personalized messages, such as emails, push notifications, or product recommendations. Then, AI agents automatically deliver these messages through the appropriate channel, ensuring timely and relevant engagement with every customer. This makes the message more relevant, so customers are more likely to respond and businesses waste less on ineffective marketing.

One company leveraging such an approach is Custard Stand, a fast-growing food manufacturer that needed to streamline customer communications and internal workflows after a major sales surge. By integrating traceability software and automating core data flows, their team reduced manual processes and improved responsiveness. These upgrades allowed the company to deliver timely, accurate updates to retailers and consumers alike, supporting both operational efficiency and targeted messaging across sales channels.

Too much stock ties up capital, but too little means missed sales. Machine learning algorithms solve this by analyzing patterns in product turnover, seasonal trends, historical demand, and even local events. Based on this analysis, ML models forecast restock dates, calculate optimal inventory levels, and trigger automatic reordering. This data-driven approach reduces overstock, avoids out-of-stock situations, and helps retailers adapt inventory decisions to real-time demand signals.

Fortenova Group, a major food and retail conglomerate in Southeastern Europe, used machine learning models to forecast demand for perishable goods like fruits and vegetables. The company reduced food waste by 7% and increased profits by 8% across 500+ stores. Store managers now use these AI-driven forecasts to place smarter orders, ensuring better product availability and fewer unsold perishables.

Retail pricing is complex. Competitors change prices, promotions affect demand, and customer segments respond differently to offers. Machine learning retail enables dynamic pricing by analyzing large volumes of real-time and historical data, including competitor prices, sales velocity, inventory levels, seasonality, location-based trends, and customer profiles.

ML models, such as regression and reinforcement learning, generate price recommendations for each SKU based on predicted demand elasticity. Retailers can adjust prices in real time or set automated rules to apply changes at scale. The result is optimized margins, higher conversions, and faster reaction to market changes.

Revionics, a retail pricing platform, used AI and ML to optimize prices across hundreds of thousands of SKUs and thousands of stores. Their platform analyzes variables like seasonal trends, competitive pricing, and local buying behavior to generate dynamic pricing strategies. Revionics enables retailers to adjust prices in real time, improve forecast accuracy, and increase profitability, while maintaining full transparency through explainable AI and embedded analytics.

AI chatbots instantly handle common questions like delivery times, order tracking, or refund policies, freeing your support team for more complex issues. They are trained using ML algorithms that analyze past interactions, identify intent, and improve response accuracy over time. As they process more conversations, the models continue to learn, enabling the chatbot to handle a broader range of queries with greater precision, without increasing employee workload or operational costs.

For instance, Magazine Luiza, one of Brazil’s largest retailers, used AI and ML to accelerate the development of digital services, including automated customer support features. Their scalable API infrastructure enabled faster rollout of chatbot-driven experiences and in-store apps, reducing pressure on human agents and improving customer satisfaction.

As physical retail evolves, collecting data inside the store has become just as important as tracking online behavior. Machine learning and computer vision now allow retailers to understand how customers move through the space, where they stop, and what products catch their attention.

Zebra Technologies, once known for barcode scanners, now uses ML and AI technologies to deliver real-time insights about in-store activity. Their systems “sense” shopper behavior, like product interaction and movement patterns, analyzing this data to inform layout decisions and workforce planning. This transformation enables physical retailers to make data-backed adjustments as easily as digital teams optimize the company's online retail.

To successfully implement machine learning in retail, businesses must begin with a clear plan, not with tools or algorithms, but with real business challenges. Start by identifying what you want to improve, such as demand forecasting, personalization, fraud prevention, or inventory management. From there, start working closely with your technology partner, who will guide you through the steps required to implement machine learning effectively and ensure that every part of the system aligns with your core business goals.

Below, you can check out our result-driven approach to implementing machine learning for retail.

Begin by identifying where retail machine learning can bring the most value. Look at your current bottlenecks, such as poor inventory accuracy, missed sales forecasts, or low personalization in marketing campaigns. By setting specific business goals from the start, you ensure every decision you make stays aligned with real outcomes, like lower stockouts or higher customer retention. This step helps define the right direction for automation, simplifying and accelerating all subsequent stages of the machine learning implementation process.

Once your goals are clear, your tech partner will help you select the most relevant machine learning use cases in retail, such as fraud detection, demand forecasting or product recommendations. At the same time, they’ll audit your existing data sources, such as Point of Sale (POS) logs or customer profiles, to assess data quality and availability. This step sets a clear direction for further ML integration, ensuring that the selected use cases are both data-ready and aligned with your business goals.

With a complex tech project like machine learning in retail, we recommend starting with a Proof of Concept (PoC). A PoC is the lightweight version of the ML system focused on one high-impact use case. Its main goal is to show how machine learning performs in your business context using your historical or real-time data to perform one core task.

For example, the PoC might focus on one frequent fraud scenario, such as repeated failed payment attempts from the same IP range, to test whether a model can reliably flag it in real time. Or, in the case of a recommendation engine, the system might suggest a single product type (like shoes) based on one variable, such as recent browsing activity. These small-scale experiments test isolated capabilities that validate whether machine learning logic works in your environment. The PoC helps validate the core idea, collect early feedback, and uncover what must be refined before scaling to full deployment.

After the successful launch of the PoC and receipt of initial positive results, you can begin expanding the capabilities of AI and ML in retail. Your IT partner will take over the full model development process. They’ll select the right algorithms, such as classification, clustering, or regression, based on the chosen task, and train models using your data. The team will also handle evaluation and tuning, making sure the model meets your accuracy and performance benchmarks. You’ll get a solution tailored to real-world retail challenges, not just lab tests.

Cleveroad applied such an approach while developing a subscription-based mobile marketplace called El Tab. Built using Flutter for iOS and Android, the app lets users buy drink subscriptions at local bars in London. The platform leverages machine learning-based recommendation algorithms to personalize notifications and suggest new offers based on users’ past preferences.

Our team handled all stages from business analysis and the platform’s MVP development to enhancing functionality and training, and validating the recommendation logic, ensuring the system accurately matched users with bars based on preferences. As a result, our client received the platform, allowing it to offer real-time personalized experiences. Such an attention to users’ preferences drove engagement and retention while supporting growth for local bar partners.

Here is what Oliver Carew, Founder of El Tab, says about collaboration with Cleveroad:

After validation, your vendor will embed the machine learning for retail into your current systems, whether that’s your ecommerce platform, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or POS. They’ll set up dashboards, real-time alerts, and interfaces that help your team act on insights immediately. They will also handle ongoing performance tracking, retraining, and optimization by the vendor, ensuring the system evolves as your data and market conditions change.

Leading global retailers now embed AI and machine learning in retail across their operations, transforming everything from personalization to inventory and trend planning. Here are the top brands that have already implemented ML in their retail business and started benefiting from it.

Amazon leads in retail machine learning through its industry-leading personalization engine. By analyzing browsing consumer behavior, purchase history, and search intent, the system creates tailored recommendations across the website, mobile app, and even Alexa, their voice assistant used in smart home devices. ML models also adapt to customer life cycles and patterns, such as seasonal preferences or gift-buying behavior, to refine results further.

These capabilities drive about 35% of Amazon’s total sales, making it a perfect example of how to leverage machine learning in eCommerce. Beyond revenue, ML enables Amazon to reduce cart abandonment, increase average order value, and enhance cross-selling across product categories. This real-time personalization engine also fuels its ad business, helping brands target users with far greater precision than traditional retail advertising (Source: Arxiv).

Walmart relies on machine learning retail solutions to optimize demand forecasting, shelf replenishment, and logistics. The company uses a custom ML platform to process massive amounts of real-time and historical data, including sales records, weather, promotions, and local demographics, to generate store-level inventory forecasts.

This AI-driven approach has reduced stockouts by up to 30%, increased forecasting accuracy, and improved product availability for customers. As a result, Walmart not only minimizes revenue loss due to empty shelves but also reduces excess inventory and spoilage, particularly in fresh categories. It enables the company to operate more efficiently while providing a better shopping experience across its 10,000+ global locations (Source: Walmart).

Sephora implements AI and ML in retail to deliver hyper-personalized beauty experiences. Through tools like the Color IQ system and Virtual Artist app, it analyzes facial features, skin tone, and product preferences using machine learning to suggest tailored skincare and makeup products. These AI tools are integrated both online and in-store for a seamless experience.

Their personalization strategy drives strong customer engagement metrics and contributes to higher conversion rates and increased customer loyalty. In-store consultations became faster and more precise, while online shoppers enjoy curated recommendations that match their needs. Sephora’s AI-powered customer journey has become a key differentiator, especially among younger consumers who expect tech-savvy service (Source: TechRepublic).

Zara harnesses machine learning in retail industry to maintain its competitive edge in fast fashion. It uses AI to scan social media, customer feedback, sales trends, and even in-store fitting room data to detect upcoming fashion trends. This allows Zara to make design, production, and stocking decisions based on real-world signals, not gut instinct.

As a result, Zara dramatically reduced overproduction and waste while speeding up time-to-market. It refreshes their collections more frequently than competitors and achieves higher sell-through rates with lower markdowns. So, machine learning helps the brand maintain agility in a trend-driven industry, ensuring it delivers the right styles at the right time, every time (Source: 1xmarketing).

While machine learning in retail industry brings massive potential, many companies face serious roadblocks in the integration. Challenges like poor data quality, lack of skilled talent, high costs, and ethical concerns make it difficult for retailers to adopt ML effectively.

Let’s break down the main categories of retail ML integration challenges and what they involve.

Even the smartest model fails without the right data. Retailers often struggle with both data collection and readiness, which delays or limits ML performance. Here are the main data-related challenges:

• Data quality and quantity. Many retailers have incomplete, outdated, or inconsistent datasets. Without clean and sufficient data, ML models can't learn patterns accurately, which results in unreliable outputs.
• Data silos and accessibility. Sales data, inventory logs, and customer behavior are often stored across disconnected systems. These silos block unified analytics and prevent end-to-end visibility, making ML adoption patchy at best.
• Data privacy and security. Strict regulations like GDPR or CCPA require careful handling of customer data. Retailers must ensure encryption, anonymization, and access controls, or risk legal issues and reputational damage.

At Cleveroad, we strictly adhere to data quality standards and protection measures. Every machine learning retail project starts with structured, validated data pipelines designed to minimize inconsistencies and gaps. We ensure your data meets readiness criteria for machine learning applications, from POS logs to third-party datasets.

In terms of security, Cleveroad operates under an ISO 27001-certified security management, backed by documented policies and controls proven to safeguard sensitive information from our side. We also implement security testing, malware and fraud prevention, vulnerability assessments, and RegTech tools. These measures help retail businesses meet compliance requirements, reduce risk exposure, and maintain customer trust throughout the ML implementation lifecycle.

Mostly, retail infrastructure wasn’t built with AI in mind. That’s why many companies hit roadblocks when integrating machine learning into daily operations. There are the main retail machine learning technical challenges:

• Integration with existing systems. Legacy POS or ERP systems may lack the flexibility to connect with modern ML pipelines. Without integration, insights can’t be delivered where they matter most, like pricing engines, recommendation systems, or dashboards.
• Scaling ML solutions. What works for one store might break at the chain level. Scalability issues often emerge when retailers try to roll out models across different stores, channels, or regions with varying data and processes.
• Skill gaps. Most retail teams aren’t staffed with ML engineers or data scientists. Without internal expertise, companies struggle to build, train, or maintain machine learning retail solutions.
• Ensuring accuracy and avoiding bias. ML models can underperform or become biased if trained on skewed data. That can lead to wrong predictions or unfair targeting, hurting both revenue and reputation.

We at Cleveroad ensure end-to-end quality control across every stage of the ML retail app development process. Our ML solution team validates all key aspects of the solution, including core functionality, third-party integrations, system performance under load, user experience, and compliance with strict security standards.

For retail businesses with outdated or fragmented systems, we also offer legacy software modernization services, helping reengineer old platforms to support modern ML pipelines and integrate seamlessly with new technologies. Such a rigorous approach to testing helps us deliver reliable, high-performing machine learning retail solutions that fit seamlessly into your operations and meet user expectations from day one.

RetailOps, a SaaS retail operations platform, is a strong example of how Cleveroad helps modernize outdated systems and introduce AI-driven functionality. We reengineered the platform’s mobile application using Swift, revamped its entire UI/UX for clarity and usability, and integrated barcode scanners and desktop printers to improve operational efficiency.

Additionally, we laid the groundwork for implementing machine learning modules by restructuring data flows and system logic to support inventory intelligence and real-time visibility. These upgrades enabled RetailOps to overcome integration challenges, improve scalability, and gain complete transparency over inventory movement and daily retail operations.

Machine learning retail adoption is not just about tech. It’s about people. Internal resistance and ethical questions can slow down even the most promising projects. Below are major ethical and organizational challenges:

• Ethical considerations. Bias in training data, untransparent algorithms, and automated decisions raise ethical concerns. Retailers must be transparent about how and why their systems make recommendations or flag transactions.
• Organizational resistance to change. Employees often fear that automation will replace jobs. Without clear communication and involvement, teams may push back against new tools, even if they improve business outcomes.
• Lack of understanding and education. Decision-makers may approve ML projects without fully grasping how they work, which leads to unrealistic expectations or poor strategic alignment.
• Balancing automation with customer experience. Over-automation risks making interactions feel robotic. Retailers must ensure machine learning enhances, but does not replace, the human touch where it matters.

Cleveroad has strong expertise in developing technology for organizations, where ethics is essential. For example, we developed a cross-platform social networking platform for faith connections for our client from the USA. The platform includes a mobile app designed as a social network for parish members, enabling communication within specific ministries, access to shared educational video content, event scheduling, and group interaction.

A separate web-based admin panel supports role-based access control (RBAC) and allows regional administrators to manage parishes based on their area of responsibility, from publishing educational materials and meeting details to overseeing user activity, all while staying compliant with GDPR.

As a result, our client received a platform that aligned with the ethical expectations of religious communities, helping simplify communication with parishioners and streamline the distribution of educational materials. By improving usability and accessibility, the app encouraged existing parish members to actively use the system and made it easier for them to invite like-minded individuals, ultimately expanding the platform’s reach within the community.

Cleveroad is a skilled retail software development company with a strong experience in providing AI and ML integrations. We offer various retail software services, including retail tech consulting, custom software development, reengineering, and third-party integrations. For over 13 years, we’ve been helping retailers of all sizes, including startups, SMBs, and enterprises, achieve their goals and bring innovative machine learning ideas to life in real business environments.

Choosing Cleveroad as your retail ML integrator, you’ll obtain:

• AI Strategy Workshop stage to help you clarify your ML solution idea, understand its business impact, and define everything from suitable use cases and scope to execution steps
• Experts skilled in integrating custom retail solutions with third-party platforms like Shopify, Salesforce, QuickBooks, Stripe, and HubSpot to ensure seamless data flow and system interoperability across your tech ecosystem
• Collaboration with an AWS Select Tier Partner leveraging powerful AWS-native generative AI tools, like Amazon SageMaker, Bedrock, AWS Glue, and others, to speed up AI and ML adoption
• AI PoC services to test and optimize how an ML technology solution fits and performs within your retail business’s existing IT ecosystem
• Partnership with an ISO-certified company implementing ISO 9001 quality management systems and ISO 27001 security standards

We at Cleveroad have solid experience in custom retail ML implementation. To demonstrate our expertise, let us share one of our recent cases for a US-based B2C marketplace operating across the United States and Europe – Web-Based Marketplace Platform.

Our client aimed to launch a competitive online marketplace that would stand out in a crowded niche. Instead of investing resources blindly, they sought a reliable partner who could define the right MVP scope, maximize ROI, and provide ongoing technical support. Cleveroad became that partner, handling everything from market analysis and UX prototyping to full development and SEO-optimized delivery.

We built a custom web and mobile marketplace platform featuring an advanced product recommendation engine powered by machine learning algorithms. The Cleveroad team trained the ML model on user interaction data and evaluated it on recommendation accuracy and user engagement metrics before production. We optimized the search system to deliver fast and relevant results, and the recommendation logic used buyer behavior data to personalize the shopping experience.

Thanks to our collaboration, our client got a robust marketplace with strong discoverability and user engagement. Due to its smart recommendations and UX-focused design, the platform began attracting organic traffic within three months of launch.