10 Key Use Cases of Machine Learning in eCommerce

Online shopping was born to make the shopping process as easy and fast as possible. Now all it takes is a few clicks to find the right product and place an order.

But behind the seeming ease lies a complex work of algorithms. Many successful online stores are already actively using machine learning (ML). It helps with personalized marketing, improved customer service, inventory management, delivery optimization, and more.

eCommerce is entering a new era where machine learning plays a key role. At SCAND, we specialize in implementing cutting-edge machine learning technologies that help eСommerce businesses stay ahead of the curve. Let’s understand how it can help and why it matters.

Case 1: AI-Powered Product Recommendations

Have you ever had it happen: you go to an online store and get lost among thousands of products, not knowing what to choose? Now imagine: you open the site, and the system already shows you exactly what you need.

This is not telepathy – this is how machine learning works. Algorithms analyze your behavior: what products you browse the longest, what you add to your cart and then delete, and what purchases you have made before.

Amazon’s system, for example, analyzes your every move: how much time you spend on a product page, what you add to your cart, and what you put off. They even take into account which products you have viewed but never bought.

Machine learning algorithms can analyze and compare your actions with the behavior of other users. If people with similar interests have bought a certain item, the system will suggest it to you as well. Moreover, Amazon takes into account the context: time of year, holidays, and current events.

Before the start of the school year, you may be offered backpacks and stationery, and before New Year’s Eve, gifts and decorations. The result? More than 35% of all purchases on Amazon are made through recommendations.

Netflix, on the other hand, is masterful at holding your attention. Their algorithms know what movies and shows you watch, how long you watch them, and at what point you stop.

They create thousands of unique categories, such as “Dramas with a strong female character” or “Comedies about friendship with elements of romance.” This allows them to offer content that perfectly matches your mood. Netflix even tests different covers for the same movie to see which one will grab more attention. Thanks to these technologies, over 80% of Netflix views come from recommendations.

But for such systems to work perfectly, it is important that the recommendations are relevant, clear, and constantly improved. As a result – you get exactly what you were looking for, and the store increases sales. It’s not just convenient, it’s the future of online shopping.

Case 2: Dynamic Pricing and Competitive Intelligence

Imagine that prices in a store change instantly, adjusting to demand, competitors’ actions, seasonality, and even your personal preferences. This is what is known as dynamic pricing, where machine learning technologies become a key tool to help companies not only track market changes but react to them instantly.

Again, let’s turn to Amazon as it is one of the most prominent examples of using dynamic pricing. Their algorithms automate millions of price adjustments daily. Amazon tracks the prices of comparable products from other vendors; should a competitor present a better price, the algorithm can immediately reduce the price. Furthermore, prices could rise during times of great demand—that of before holidays—and vice versa.

Amazon also uses data about specific users to boost loyalty: if you frequently browse a certain item but don’t buy it, you may be offered a personalized discount—a strategy that increases repeat purchases by 30%. The business is also continuously testing pricing, enabling businesses to discover optimal price points for different user segments while maximizing long-term revenue.

Walmart, another retail giant, is also actively using machine learning for dynamic pricing. Walmart uses algorithms to examine competitors’ prices in real time, and if, for example, Target lowers the price of a certain product, Walmart can instantly react. The company also takes into account regional peculiarities: prices for the same product may differ from city to city or even neighborhood to neighborhood, depending on the income level of the population and competition.

Walmart’s algorithms employ reinforcement learning, continuously improving price adjustments through trial and error. Algorithms predict how a price change will affect demand, which allows the company not only to optimize prices but also to manage inventory, avoiding surpluses or shortages.

Case 3: AI Chatbots and Virtual Assistants

Imagine: you go to the website of an online store, and you are instantly offered help. This is not a live operator, but an AI chatbot that works 24 hours a day, 7 days a week, without breaks or weekends. It answers questions about shipping, helps you choose products, tracks orders, and even gives personalized recommendations.

Shopify’s platform, for instance, employs similar chatbots to assist customers and sellers. They not only enhance the customer experience but also lighten the support team’s job burden so that staff members may concentrate on more difficult projects.

Cosmetics brand Sephora has gone even further. Their virtual assistant provides customized recommendations, and makeup advice, and helps consumers select items in addition to answering inquiries. This not only makes shopping more convenient but also increases eCommerce sales, as customers get exactly what they need.

For chatbots to work really effectively, it is important to pay attention to training them. The first thing to consider is the quality of the data. The knowledge base also needs to be updated regularly so that the chatbot can answer the most relevant questions and stay in touch with reality.

In addition, it is important to constantly test and improve interaction scenarios. This helps to make communication with the chatbot more natural and useful for customers. However, you should not forget that even the most clever chatbot will not always be able to solve complicated or non-standard tasks. Therefore, there should always be a smooth transition to a live operator.

Case 4: Fraud Detection and Secure Transactions

Can you imagine artificial intelligence being able to protect your finances better than the most vigilant security guard? Modern best machine learning algorithms continuously analyze millions of transactions, detecting the slightest suspicious activity in real time. They study your financial habits – how often you make payments, typical transaction amounts, habitual shopping locations – and instantly react to any deviations from the norm.

When the system notices something unusual, like a large payment from a country you’ve never been to or an attempted purchase at an uncharacteristic time, it can instantly block the transaction or request additional confirmation. It’s like having a personal financial detective working 24/7 to make sure your money stays safe at all times.

PayPal is a great example of how this works. Their system checks millions of transactions every day using ML. If something seems suspicious, such as an unusual payment, the system may ask for additional confirmation. This helps PayPal not only catch fraudsters but also minimize false blocking of legitimate payments, preserving customer trust.

For such systems to work effectively, it’s crucial to follow a few rules:

• ML must be easily integrated into current processes
• Algorithms should be constantly trained on newly acquired data.
• Security must be layered: AI + 2FA and encryption
• It’s important to be transparent – customers are supposed to understand why their transactions are being audited.

Case 5: Inventory Forecasting and Demand Prediction

Want your customers to always be able to find the right product on the shelf and leave satisfied with their purchase? Machine learning can help here too, it allows companies to predict what products will be in demand, minimize surpluses, and avoid shortages, making life especially easy for retailers.

To do this, ML algorithms study sales history to identify seasonal trends, demand peaks, and other patterns. They even take into account the weather, holidays, the economic situation in the country, and even world events – seemingly insignificant, but all of this can affect demand. Most importantly, the algorithms help you determine when and how much product to order to avoid overages or shortages.

For example, one of the leaders in the fashion industry, Zara, uses AI to optimize its inventory. Their system analyzes real-time sales data to respond quickly to changes in demand.

If a certain item starts selling faster than expected, the system automatically increases orders. This allows Zara to avoid shortages and maintain high levels of customer satisfaction. AI helps Zara minimize surplus so they don’t have to spend on storage or disposal.

So what does it take to optimize supply chain performance with ML?

To optimize supply chains as well as possible, combine data from all sources – sales, deliveries, inventory – to get a complete picture. Update machine-supervised learning models regularly. Use machine learning to automate ordering and inventory management to reduce human error.

It’s critical that the supply chain is adaptive to respond quickly to changes in demand or supply disruptions. Also, collaborate with suppliers to improve forecast accuracy and speed up order fulfillment.

Case 6: Visual Search and Image Recognition

Visual search is a technology that allows shoppers to search for products using images rather than text. Thanks to machine learning and computer vision, users can take a photo of a product, upload it to a search engine, and find visually similar products available for purchase. Such systems not only simplify the search but can also recommend related products, such as clothing that goes well with the selected item to create a harmonious look.

So how does this search through ML for eCommerce work?

Machine learning for eCommerce algorithms examines key visual characteristics of a product, such as color, shape, texture, and patterns, further comparing it with a huge database to find similar products, but the best part is that the system can suggest additional products that go well with the selected one, such as accessories or closet items.

World giants have already shown their examples of successful implementation of visual search. For example, Pinterest Lens. Users can take a photo of any item and Pinterest will suggest visually similar products or ideas for inspiration. This is especially useful for creative people looking for unique items or decorating ideas.

Google Lens, in turn, allows users to search for information about products by simply pointing the camera at them. For example, a user takes a photo of a dress in a store and the system suggests similar or the same options in other stores.

ASOS Style Match uses this type of search to help buyers find clothes and accessories that match their style. Users can upload a photo and the system will suggest similar items from the ASOS range.

For the integration of visual search to be successful, it is important to monitor the quality of images, the interface for uploading images should be intuitive and user-friendly. It should be taken into account that most users use smartphones for this type of search, so it is important to optimize the platform for mobile devices. Regular testing and gathering user feedback will improve the accuracy and functionality of the search.

Case 7: Customer Churn Prediction and Retention Strategies

What if we told you that AI can tell you immediately which customer will leave you? Yes, they can do that. These technologies analyze behavioral patterns and help you develop personalized retention strategies – the ultimate marketing psychologist.

Algorithms study the frequency of service usage, payment history, and interaction with customer support. Then, they identify alarming signals, such as – decreased activity, missed payments, or frequent complaints. And based on this behavior, each customer is assigned a “churn score”.

For example, Netflix uses sophisticated ML models that track how often a user watches content, and if viewing time is decreasing, offer personalized recommendations when they notice a decrease in activity, and even automatically send special discounts or bonuses tailored to the interests of a user at high risk of churn. They can even offer alternative subscription or payment options.

Companies implementing such technologies are able to reduce customer churn by 15-25%. But it is crucial that every decision is accompanied by warmth and attention: the customer needs to feel that their tastes and needs are truly valued.

Case 8: AI-Generated Content and Automated Marketing

A recent study by Amazon Web Services found that 57% of content on the Internet is either generated by machine intelligence or translated into other languages with AI expected to reach 90% by 2026.

So how does AI create content for businesses? AI analyzes product specifications and turns them into sales descriptions with SEO optimization in mind. The system can create hundreds of unique texts in a minute, maintaining a unified brand style.

But it can write not only product descriptions but also articles for your blog. Based on keywords and topics, the AI generates structured articles, selecting examples and statistics. And all of this will match the tone and style of your brand.

When it comes to emails, ML creates customized offers for different audience segments, increasing the conversion rate of emails and advertising campaigns.

The best tools for content automation:

• Jasper AI – Specializes in creating sales texts and marketing campaigns
• Copy.ai – Generates creative texts for social networks and advertising campaigns
• ChatGPT – A well-known universal tool for different types of content
• Writesonic – Creates SEO-optimized articles and lendings

Case 9: Smart Search and Voice Commerce

“Okay, Google, I broke a 60-by-80 bathroom mirror with a matte finish – find exactly the same one” – and in a moment you see not just similar models, but a specific replacement tailored to the fixtures and style of your bathroom fixtures.

That’s the real power of machine learning in eСommerce: when computer vision matches chipped photos to catalogs, neural networks reconstruct missing parameters, and the voice assistant specifies: “ In your house are Grohe faucets – show compatible accessories?”.

It’s no longer a search – it’s a digital explorer that recreates the right product from scraps of memory, even when you don’t remember the exact model. Magic? No – just algorithms that have learned to see the world through the eyes of the customer.

To incorporate smart search into your program, you need to understand the principles without which a lot can go wrong as you intended. First of all, the system should work with conversational phrases, not just exact product names, because real customers rarely formulate queries as a catalog. Show products with filters like “similar”, “alternatives”, and “often bought together”.

The more users interact with search, the more accurate it should become. Algorithms need to memorize:

• Which options are chosen more often
• Which queries remain without suitable results
• How users refine the search themselves

Voice and text searches should work on a single base. If a customer first searched for “red sneakers” by voice and then switched to the text query “Nike sneakers,” the system should take both options into account.

Response speed is also an important criterion. A delay of more than 1 second reduces conversion by 10%. Search should be instant, even when analyzing millions of products.

Major market players have already proven the effectiveness of these technologies in practice. Amazon Alexa demonstrates impressive results – 35% of users regularly make repeat purchases through voice commands.

Google Shopping AI (thanks to deep analysis of customers’ search intentions) was able to increase conversion by as much as 30%. The Walmart Voice Order case is particularly illustrative – their voice ordering system radically reduced checkout time from 5 minutes to 30 seconds.

Case 10: Sentiment Analysis and Customer Insights

Feedback and social networks are a gold mine for business if you know how to analyze them properly. Modern machine learning systems don’t just collect reviews, they understand the hidden emotions and real pains of customers.

AI scans thousands of reviews, comments, and posts, identifying: tone (delight, annoyance, disappointment), key topics (which product features are mentioned more often), and hidden trends, such as customers starting to complain en masse about the packaging after a design change.

Solution examples:

• Lexalytics – Analyzes even sarcasm in texts (“Oh yeah, ‘great’ service – waited 3 days for the courier!”)
• MonkeyLearn – Automatically sorts reviews by category (quality, delivery, service)

When working with feedback, it’s critical not just to collect it, but to act on it – if 70% of negative feedback mentions a “flimsy lid,” it means it’s time to change the packaging design. Track dynamics – machine learning should show how customer sentiment changes after innovation.

Also, look for non-obvious connections. For example, negative reviews of delivery are more likely to appear when it’s raining. Integrate data across all departments – from product development to customer service. Respond in a personalized way – automation + human engagement (“We see you’re upset about the delay – here’s a promo code to compensate”).

Best Practices for Implementing ML in eCommerce

Modern eCommerce platforms are increasingly using unsupervised learning techniques to uncover hidden patterns in customer data without predefined labels. By implementing advanced ML algorithms, businesses can leverage machine learning to enhance marketing strategies and optimize operations.

For instance, natural language processing enables smarter analysis of customer reviews and queries, driving more personalized customer experiences. Below we explore key approaches to maximize these technologies’ potential.

Tool selection

For the successful implementation of ML projects in eСommerce, it is important to select technologies for specific business tasks. Scand.com specialists, who have many years of experience in developing ML solutions, recommend:

• Google AI for deep learning of customer behavior analytics and personalization
• AWS SageMaker for accurate demand forecasting and inventory management
• TensorFlow/PyTorch if you need to develop custom models of recommendation system

Data handling

It is important to follow the key principles of data preparation:

• Collect comprehensive metrics: from classic transactions to behavioral patterns
• Implement a multi-level validation and data cleansing system
• Use modern storage approaches (Data Lakes, vector databases)

Ethical considerations

When using machine learning, it is critical to strike a balance between personalization and respect for user privacy. Personal information (names, exact addresses, payment details) should be removed or encrypted before analyzing user behavior.

• Preserve useful patterns of behavior
• Eliminate the risk of sensitive data leakage
• Comply with GDPR and other regulatory requirements

Optimization and development

Effective implementation methodology:

• Phased launch with controlled A/B tests
• Comprehensive monitoring of business metrics
• Scheduled model retraining

For companies that want to get the most out of machine learning, but do not have in-house expertise, SCAND offers comprehensive services for the development and implementation of ML solutions. Our experts will help you go all the way – from data analysis to implementation of a working system.

Future Trends of Machine Learning in eCommerce

The eCommerce industry is being transformed by machine learning solutions that inspect vast amounts of data to predict customer needs before they arise.

These advanced machine learning models are redefining how online retailers engage with shoppers across every touchpoint, creating experiences that blend cutting-edge technology with human-centric design.

Below, we explore four groundbreaking trends where artificial intelligence and machine learning are revolutionizing online commerce.

Hyper-personalization and AI-driven customer engagement

Today’s machine learning solutions are moving beyond simple product recommendation systems to creating a truly personalized shopping customer experience. It’s no longer just about analyzing purchase history, but deeply understanding the context of each customer.

Advanced algorithms have learned to recognize the emotional state of shoppers via camera and microphone – capturing changes in voice intonation, facial expressions, and even pupil dilation when viewing certain products. Some online retailers are experimenting with biometric data – for example, assay pulse rate or skin-galvanic reaction when interacting with a product.

Of particular interest is the adaptation of interfaces to the cognitive characteristics of users – the system can determine what type of information presentation (visual, textual, interactive) is best perceived by a particular person.

A vivid example – Alibaba is testing a system of “neuro-marketing”, where machine intelligence adjusts the output of goods based on electroencephalogram data read by a special headset. This makes it possible to literally read shoppers’ minds and offer them exactly what they subconsciously want.

Augmented reality (AR) + AI for virtual shopping experiences

Augmented reality technologies combined with machine learning create fundamentally new opportunities for online sales, powered by advanced deep learning models. Modern virtual fitting systems are now able to determine body parameters with high accuracy (up to 1 centimeter) based on an ordinary photo and automatically adjust clothes.

But this is just the beginning – there are solutions that model how things will look after a few washes or how furniture will change after 5-10 years of use, utilizing sophisticated models. Especially promising is the direction of ML stylists in augmented reality – such systems can combine closet items from different stores, creating holistic images.

IKEA has already introduced Visual AI – a technology that recognizes interior features from a photograph with 98% accuracy, taking into account even such nuances as natural light and shadows. This allows furniture to be virtually “placed” in a room, taking into account all the real parameters of the space.

Blockchain + AI for secure transactions and supply chain tracking

The combination of blockchain technology and artificial intelligence is creating a fundamentally new ecosystem of trust in eCommerce business. Every product can now have a digital passport with a complete history of its movements from manufacturer to buyer.

This is especially relevant for the fight against counterfeiting – the system automatically verifies the authenticity of the product at every stage of the supply chain. Smart contracts on blockchain allow for the automation of financial settlements with suppliers – payments are made instantly when predetermined conditions are met.

A separate area is environmental footprint tracking. Buyers can see what carbon footprint a particular product has left on its way to them, which becomes an important choice factor for environmentally conscious consumers. The technology also solves the problem of fake reviews and ratings – each rating is recorded in the blockchain and cannot be changed or deleted.

Autonomous shopping experiences powered by AI

The concept of stores without cash registers is rapidly evolving in the eCommerce industry, powered by advanced machine learning use cases. The next generation of Amazon Go will leverage sophisticated analysis of customer behavior – recognizing shoppers by unique biometric parameters like gait and gestures rather than smartphone apps.

Next-gen voice assistants now process vast amounts of data to handle complex dialogs, even for non-trivial purchases like insurance products. But the most revolutionary innovation is predictive shopping – where systems examine behavioral patterns and biorhythms to ship products before customers place orders. Major retailers in the FMCG sector are already piloting these systems.