Machine learning in ecommerce:
10 use cases, examples, and best practices

Machine learning in ecommerce: 10 use cases, examples, and best practices

September 5, 2023

Machine learning in ecommerce involves the adoption of systems powered by self-learning algorithms to forecast sales trends, fine-tune marketing strategies, streamline inventory management and order deliveries, personalize the shopping experience, and mitigate online retail risks.

Explore the key use cases, benefits, and adoption best practices of this technology to find out how ML is reshaping the way we buy or sell on the web and why online businesses should consider involving machine learning consultants in their ecommerce projects.

ML in ecommerce: market stats

+ 5-15%

in revenues by implementing ML-based product recommendation systems


+ $1.7 trillion

the value AI, including ML, is expected to create in the retail industry

McKinsey Insights

+ 25%

in customer satisfaction and revenue achieved by ML in ecommerce


+ 94%

in qualified new site visitors due to leveraging social data and machine learning in retail and ecommerce


Top 10 machine learning use cases in ecommerce

From marketing and customer care to logistics and security, ML in ecommerce is paving the way for innovations in various business functions. Let's find out how machine learning algorithms are applied in this field and what advantages ecommerce companies can get from implementing ML in their operations.

Recommendation engines

ML-based recommendation systems are a staple in all major ecommerce platforms and online stores as they drive upselling. These engines can follow two distinct approaches to provide suggestions. Those relying on content-based filtering check a customer's purchase history and recommend other products with characteristics similar to those already bought. Systems using collaborative filtering, on the other hand, will suggest products that have already been ordered and positively rated by other users with similar buying patterns.

Increased average order value, greater customer lifetime value
Content-based filteringBlurgBought by BlurgRecommended to BlurgSimilar itemsnoisybluefancycan be used in spacenoisybluefancycan be used in spaceCollaborative filteringBlurgBought by both usersSimilar itemsBought by Blurg, recommended to ZorgZorg

Targeted advertising

Similarly to recommendation systems, this technique embraces customization to drive sales. Potential new customers can be segmented and targeted with tailored ads according to a variety of parameters, like their social media interactions, previous online purchases (including those made in virtual stores), Google search history, and other types of big data used in ecommerce. Then, machine learning algorithms process this information to frame customer behavior and purchase patterns, forecasting the products they may like.

Enhanced lead conversion, optimized marketing costs and ROI

Smart search engines

Traditional search engines integrated into ecommerce stores match the keywords used for the site search and those chosen to describe the products in stock. Enhanced with deep learning-based natural language processing capabilities, however, these tools can achieve way deeper context understanding. For example, an ML-driven engine can take into account a broader range of synonyms. It can also adjust the site search process in real time by prioritizing some results according to each user's purchase habits and taste.

Easier content or product research for a smoother user experience
Zorglaser gunsearched by ZorgSimilar keywordslaser pistolsuggested to ZorgHigher context understanding capabilitiesConsideration of a broader range of synonymsSearch prioritization based on users' purchase habitsZorglaser gunsearched by ZorgSimilar keywordslaser pistolsuggested to ZorgHigher context understanding capabilitiesConsideration of a broader range of synonymsSearch prioritization based on users' purchase habits

Scheme title: Machine learning-driven smart search engine

Pricing optimization

Several ecommerce platforms and online stores leverage machine learning to optimize their pricing strategies and personalize discounts or other promotions. This technique, usually referred to as dynamic pricing, involves periodic and customized price adjustments (even every few minutes, in the case of Amazon) based on personal user data, pricing history of similar products, sales trends, competitors' offers, demand compared to supply, and more.

Maximized revenues and minimized customer churn
Static pricing (single price point)PriceDemand1standard priceThe revenueDynamic pricing (multiple price point)PriceDemand3standard price1premium price5discount price24The revenue

Scheme title: Static vs dynamic pricing

Chatbots and virtual assistants

Compared to standard, rule-based bots which can only solve simple queries, modern chatbots powered by natural language processing also understand the context in which they operate and, above all, learn from experience, i.e. from previous interactions with other users. This enables chatbots to maintain a realistic conversation and completely replace humans in a variety of customer support tasks. For example, they can clarify available shipping options, ask questions to understand customer preferences, and offer coupons depending on their responses.

Greater interactivity and non-stop support, superior customer retention
ChatbotInteraction channelWeb pageSmart agentLive chat, SMMobile appEmailKioskActivity stream Interaction channelHuman machine UXTextVoiceVideoChatbotListening (NLP) “When will I receive my new starship?”Chatting (NLG)“Delivery is scheduled for next Monday.” Knowledge -based dataMachine learningBusiness logic

Scheme title: How chatbots operate

Contextual shopping

Another significant example of machine learning in the ecommerce industry, specifically applied to route customers towards products while offering them a more interactive experience, comes from contextual shopping solutions. Powered by machine learning and computer vision technologies, these applications identify and highlight specific products or brands that appear in online videos. Sellers can embed links to the related product page, enabling users to purchase such items without leaving multimedia content.

Increased customer engagement, better lead conversion
Contextual shopping

Image title: A fashion show video with an overlay linking to mentioned products

Trend analysis and restocking

ML can help retailers match supply and demand by predicting upcoming ecommerce sales trends and planning out restocking. This also comes in handy for optimizing the catalog, therefore reducing storage utilization and the risk of food spoilage. The variables considered include economic conditions, seasonality-driven purchase patterns, social media sentiment, product reviews, and ratings. All these data points come from various sources, such as social media, ecommerce websites, or POS solutions.

Optimized inventory management and minimized waste

Scheme title: Data sources for inventory optimization
Data source: — The age of with Leveraging AI to connect the retail enterprise of the future

Delivery optimization

Machine learning in retail and ecommerce facilitates product distribution via delivery optimization solutions. They process data from prior cases and suggest the best expedition methods and conditions (such as free or one-day delivery) according to customer expectations. As for actual shipping, the process can be accelerated through machine learning-based route planning. These solutions analyze real-time traffic data, weather conditions, and drivers' experience and performance to recommend the fastest route.

Improved logistics, including faster deliveries
Delivery optimization

Image title: Onfleet’ route optimization solution
Data source: — Power your retail & eCommerce deliveries

Self-driving vehicles

Another embodiment of machine learning aimed at improving product delivery involves deploying self-driving vehicles powered by ML algorithms, deep learning, and computer vision. However, we are still in the realm of experimentation, rather than at a full implementation stage. Still, several pioneering companies like Amazon and Kroger are investing in these technologies with rather promising results, especially to speed up last-mile deliveries and minimize their costs.

Faster and cheaper product delivery
Self-driving vehicles

Image title: Kroger’s self-driving vehicle for grocery delivery
Data source: — Kroger has launched self-driving grocery deliveries in Arizona

Fraud detection

Many companies have already turned their gaze to ML as a potential weapon to protect their ecommerce portals from a wide range of criminal actions, including identity theft and fraudulent electronic payments. ML algorithms can spot recurring patterns among the data they process, but also notice if something "breaks the rules". Indeed, ML in fraud detection is commonly deployed to detect anomalies among the credit card accounts under scrutiny (such as an increasing frequency of transactions) that may be signs of fraudulent attempts.

Enhanced corporate security and safer transactions