Intro
AI-powered product recommendation tools are changing how eCommerce businesses help customers discover products and complete purchases. By analysing browsing behaviour, previous orders, product attributes and real-time shopping activity, these platforms can recommend relevant items throughout the customer journey. Established providers such as Nosto, Bloomreach and Dynamic Yield compete with emerging solutions that use conversational AI, predictive models and automated personalisation. Their aim is to make online shopping more relevant while helping retailers increase conversion rates, average order value and customer retention.
However, not every AI recommendation engine delivers the same results. Some are easier to implement, while others offer greater control over complex product catalogues and customer data. Accuracy also depends on the quality of the available data, the relevance of the recommendation model and how effectively recommendations are tested. This review compares leading AI-powered product recommendation tools in 2026, examines their usability and commercial value, and explains how eCommerce businesses can calculate return on investment (ROI) before committing to a new platform.
Lets Dive In
Why AI Product Recommendations Matter in eCommerce in 2026
Online shoppers often face a large number of products, competing offers and similar choices. When customers struggle to find relevant items, they may leave without purchasing. Product recommendation engines address this problem by helping shoppers discover products that match their interests, needs and purchasing behaviour.
Traditional recommendation systems often rely on fixed rules, best-selling products or basic associations between items. AI-powered alternatives can combine several signals, including product views, search queries, previous purchases, cart activity, customer preferences and contextual information. The system can then adjust recommendations as shopping behaviour changes.
For example, a customer browsing running shoes might receive recommendations for similar footwear, suitable running socks or products that complement their previous purchases. A returning customer could see relevant replenishment products, while a first-time visitor might receive popular items or suggestions based on the products they explore during the session.
Research published by Bloomreach highlights the commercial potential of product recommendations and explains how retailers can use different recommendation strategies across the customer journey. However, results vary by retailer, product category, implementation and customer behaviour. AI should therefore be treated as a tool for improving product discovery, rather than a guarantee of higher sales.
How AI-Powered Recommendation Engines Work
Behavioural Data and Personalisation
Many recommendation engines use behavioural data to estimate what a customer might want next. Signals can include product views, search activity, clicks, purchases and items added to a basket. Machine learning models identify patterns in these interactions and use them to rank relevant products.
Some systems also use product attributes such as brand, colour, size, price and category. These details help the engine recommend products that are similar to those a customer has viewed or that complement an existing purchase.
The quality of these recommendations depends on reliable product information and sufficient behavioural data. Poor product descriptions, duplicate catalogue entries or missing stock information can weaken results. Retailers should ensure that the recommendation engine uses accurate product feeds and excludes unavailable items where appropriate.
Predictive and Generative AI
Predictive AI estimates which products a shopper is most likely to engage with or purchase. It can help determine the order of recommended items, identify complementary products and adapt suggestions to changing customer interests.
Generative AI adds another layer. Conversational shopping assistants can interpret questions expressed in everyday language and guide shoppers towards suitable products. Instead of clicking through multiple filters, a customer might ask for a lightweight jacket suitable for rainy weather or a gift within a particular budget.
These approaches can improve product discovery, but they are not interchangeable. Predictive recommendation models rank products based on data and learned patterns, while generative AI can interpret requests and explain suggestions. The strongest implementations combine relevant product retrieval with reliable catalogue information and clear commercial controls.
Leading AI-Powered Product Recommendation Tools in 2026
Nosto: Personalisation and Merchandising for Online Stores
Nosto offers AI-powered product recommendations alongside search, merchandising and broader commerce personalisation features. Its recommendation engine uses shopper behaviour and product information to deliver suggestions across different parts of an online store.
Retailers can use Nosto for personalised product suggestions, complementary products, bestsellers, replenishment recommendations and related items. Merchandising controls allow teams to promote selected products, adjust rankings and account for factors such as stock availability, product category and commercial performance.
Usability: Nosto is designed to help eCommerce and merchandising teams manage recommendation experiences through a visual interface. Its ready-made algorithms and configurable placements can reduce the amount of custom development required. Nevertheless, businesses still need to configure their catalogue, connect data sources and decide how recommendations should support their merchandising strategy.
Accuracy: The platform can use real-time behaviour and product attributes to make more relevant suggestions. Retailers can improve performance by providing accurate catalogue data, tracking customer interactions and testing different recommendation strategies. Personalisation can be less precise for first-time visitors with little behavioural history, so contextual signals and popular products remain useful.
ROI potential: Nosto can support higher conversion rates, average order value and revenue per visitor when its recommendations improve product discovery. In a published case study, fashion brand Cynthia Rowley reported a 15% increase in conversion rate and an 8% increase in revenue per visitor after testing a personalised recommendation strategy against its previous approach. These are vendor-published case-study results, not a guarantee of comparable performance for other retailers. Read the Cynthia Rowley case study.
Best suited to: Growing and established eCommerce brands that want recommendations, search and merchandising to work together without building every capability internally.
Bloomreach: AI-Driven Product Discovery and Personalisation
Bloomreach combines AI-powered recommendations with product discovery, search, merchandising and customer experience personalisation. Its Loomi AI capabilities are designed to help retailers interpret shopping intent and deliver relevant experiences across the customer journey.
The platform supports recommendations such as similar products, bestsellers, trending items and suggestions based on previous shopping activity. Retailers can also combine automated recommendations with merchandising rules and curated product collections.
Usability: Bloomreach provides tools for managing recommendation placements and adjusting merchandising strategies. Its broader feature set can reduce the need to coordinate several separate systems, although implementation may require more planning than a lightweight recommendation app. Businesses should consider their technical resources and the time needed to configure data and workflows.
Accuracy: Bloomreach can use customer interactions, search intent, browsing activity and purchase history to improve product relevance. The quality of results depends on how well the platform understands the catalogue and how effectively retailers combine automated ranking with business rules.
ROI potential: Bloomreach can help businesses improve product discovery, increase basket value and reduce manual merchandising work. Its published Sur La Table case study reports an 11.5% increase in category average order value and a 7.6% increase in search average order value following its discovery improvements. These results relate to the retailer’s reported implementation and should not be treated as typical outcomes for every business. Read the Sur La Table case study.
Best suited to: Mid-market and enterprise retailers with extensive catalogues, sophisticated merchandising requirements and a need to connect recommendations with search and customer experience management.
Dynamic Yield: Experimentation and Personalised Experiences
Dynamic Yield provides personalisation and optimisation capabilities that can be used to tailor product recommendations, content and shopping experiences. Its platform is relevant to retailers that want to test different recommendation strategies and adjust experiences for distinct customer segments.
Recommendation strategies can incorporate product popularity, customer affinities, browsing behaviour and other contextual signals. Retailers can use these options to deliver related products, personalised selections and targeted experiences across the shopping journey.
Usability: Dynamic Yield offers substantial flexibility, but the breadth of its capabilities means that teams may need training and a clear experimentation process. Organisations should establish who manages recommendations, who approves changes and how results are monitored.
Accuracy: Its personalisation and recommendation approaches can account for customer behaviour and product relationships. Accuracy should be evaluated through relevant metrics, such as product clicks, add-to-cart rates and completed purchases, rather than the apparent relevance of suggestions alone.
ROI potential: Dynamic Yield can support conversion optimisation by allowing retailers to test and refine experiences. The commercial benefit depends on traffic volume, the quality of experiments and whether personalisation improves incremental performance after accounting for implementation and operating costs.
Best suited to: Larger retailers and eCommerce teams with established analytics capabilities, structured testing programmes and resources to manage ongoing optimisation.
Clerk.io: Behaviour-Based Recommendations for Growing Stores
Clerk.io offers AI-powered product recommendations, search and personalisation features for online retailers. Its approach connects product information with customer interactions to help shoppers find relevant items across the store.
Clerk.io can support recommendations based on browsing and purchasing behaviour, related products, bestsellers and other shopping signals. Retailers can also use its wider capabilities to connect product discovery with email and customer engagement workflows.
Usability: Clerk.io is designed for eCommerce use cases and can reduce the need to build recommendation logic from scratch. Businesses should still assess integration requirements, catalogue setup and the amount of manual control available to their merchandising teams.
Accuracy: Behaviour-based suggestions can become more relevant as the system receives useful data about customer interests and product relationships. New stores or retailers with limited traffic may have less information available, making product attributes and popularity-based recommendations important starting points.
ROI potential: Clerk.io may help improve product discovery and reduce the effort involved in managing search and recommendations. Its value is strongest when the business can demonstrate measurable improvements in conversion, basket size or repeat purchases.
Best suited to: Small and medium-sized eCommerce businesses seeking an integrated approach to search, recommendations and personalisation.
Google Vertex AI Search for Commerce: A More Technical Approach
Google Cloud Vertex AI Search for Commerce provides search and recommendation capabilities for retailers that want to build AI-supported shopping experiences using Google Cloud infrastructure.
The solution can support personalised recommendations, product discovery and search experiences using retail catalogue and customer interaction data. It is particularly relevant to organisations that need to integrate recommendation models into a broader technology environment.
Usability: Compared with a plug-and-play eCommerce application, a cloud-based implementation can require more technical expertise. Teams may need to manage data ingestion, event tracking, model configuration, testing and integration with the storefront.
Accuracy: The system’s effectiveness depends on the quality of the product catalogue, the quantity and relevance of interaction data, and the suitability of the model for the retailer’s objectives. Businesses need to monitor recommendation performance and ensure that the system reflects current prices, availability and product details.
ROI potential: Vertex AI Search for Commerce can be attractive to organisations with engineering resources and complex requirements. However, implementation and ongoing cloud costs must be weighed against the benefits of greater control and integration flexibility.
Best suited to: Data-driven retailers with technical teams that want to integrate AI search and recommendations into a customised shopping experience.
Emerging AI Recommendation Trends Worth Watching
Conversational Product Discovery
Conversational shopping is an important area to watch as generative AI becomes more capable of interpreting customer needs. Instead of relying entirely on filters and product grids, shoppers can describe what they want and receive relevant suggestions.
For example, a customer shopping for home office equipment might ask for a compact desk, an ergonomic chair and accessories within a fixed budget. A conversational tool can help narrow the options and explain why particular products may be suitable.
However, conversational interfaces must be grounded in accurate catalogue data. If an assistant invents a feature, misstates a price or recommends an unavailable item, the experience can undermine trust. Retailers should check how tools retrieve product information, handle uncertain requests and direct shoppers to current product pages.
Real-Time and Contextual Recommendations
Recommendation systems are moving beyond historical purchase data towards more contextual signals. Current browsing activity, the page a customer is viewing, product availability and the purpose of a visit can all help determine which products should appear.
This is especially useful for retailers with seasonal products, changing inventories or customers who shop for different needs on each visit. Contextual recommendations can also help first-time visitors who have not yet built a substantial shopping history.
Retailers should balance personalisation with customer choice. Recommendations should remain useful and transparent, rather than making shoppers feel that every interaction is being tracked unnecessarily.
Visual Similarity and Product Matching
Visual recommendation capabilities are useful for fashion, furniture, home décor and other categories where appearance strongly influences purchasing decisions. These tools can identify products with similar colours, shapes, styles or design characteristics.
Visual similarity can help customers find alternatives when an item is unavailable or discover related products that match their preferences. Accuracy depends on image quality, catalogue consistency and whether the model understands the features that matter to customers.
Comparing Usability, Accuracy and ROI
No single AI recommendation platform is the best choice for every eCommerce business. A retailer with a small catalogue and limited technical resources may prefer a simple application, while a global brand may need a broader personalisation platform with sophisticated controls.
Usability should be assessed by looking at implementation time, ease of managing product placements, integration requirements and the amount of specialist support needed. A system that offers advanced AI features but requires extensive manual work may not deliver the expected commercial benefit.
Accuracy should be measured against real customer behaviour. Retailers can examine whether recommended products receive clicks, lead to add-to-cart events and generate completed purchases. They should also check for irrelevant suggestions, out-of-stock items, excessive repetition and recommendations that conflict with customer needs.
ROI requires a broader assessment. The platform may increase revenue, but it can also introduce software costs, integration work, additional analytics requirements and ongoing maintenance. A successful deployment should demonstrate incremental value after these expenses are considered.
The most effective evaluation combines usability, accuracy and financial performance. A platform that delivers a small but reliable improvement with low operating costs may be more valuable than a sophisticated solution whose results are difficult to measure.
How to Measure the ROI of AI Product Recommendations
Conversion Rate and Average Order Value
Conversion rate measures the proportion of visits that result in a purchase. Average order value (AOV) measures the average amount spent per order. Recommendation tools may improve either metric by helping customers find relevant products or discover complementary items.
Retailers should avoid assuming that every purchase involving a recommendation was caused by the tool. Customers may already have intended to buy the product. A controlled experiment can help estimate the incremental effect of recommendations.
Revenue per Visitor and Gross Margin
Revenue per visitor helps businesses assess how much revenue each visit generates. It can reveal improvements that are not fully captured by conversion rate alone.
Gross margin is also important. A recommendation system might increase sales by promoting heavily discounted products, yet reduce profitability. Retailers should consider product margins, returns, fulfilment costs and promotional spending when evaluating results.
A Practical ROI Example
Consider an online retailer receiving 100,000 visits per month, with a baseline conversion rate of 2% and an average order value of $80.
<escape>| Metric | Before recommendations | After a hypothetical 10% relative conversion lift |
|—|—:|—:|
| Monthly visits | 100,000 | 100,000 |
| Conversion rate | 2.0% | 2.2% |
| Orders per month | 2,000 | 2,200 |
| Average order value | $80 | $80 |
| Monthly revenue | $160,000 | $176,000 |
| Incremental monthly revenue | — | $16,000 |</escape>
In this illustrative example, a 10% relative improvement in conversion rate increases monthly revenue by $16,000, assuming traffic and average order value remain unchanged. The figures are hypothetical and are not a forecast of what a particular tool will achieve.
If the recommendation platform costs $3,000 per month to operate, the additional revenue would exceed the platform fee. However, this is not yet a complete ROI calculation because revenue is not profit. The retailer must also account for product costs, fulfilment, returns, implementation and other expenses.
A more useful calculation is:
ROI = (Incremental gross profit − total implementation and operating costs) ÷ total implementation and operating costs × 100
Retailers should run controlled A/B tests where possible, compare similar customer groups and allow enough time to account for normal changes in traffic and demand. They should also monitor results across mobile and desktop, new and returning customers, and different product categories.
Common Challenges When Implementing AI Recommendations
Incomplete or Inaccurate Product Data
Recommendation engines depend on accurate product information. Missing attributes, duplicate records and outdated stock data can produce irrelevant suggestions. Retailers should establish a reliable product feed and regularly check data quality.
Limited Customer Data and the Cold-Start Problem
New customers may have little browsing or purchase history. New products may also lack interaction data. Systems can address this by using product attributes, category relationships, popular products and contextual signals until more evidence becomes available.
Privacy and Customer Trust
Personalisation must respect applicable privacy and data protection requirements. Businesses should review consent practices, data retention, access controls and the information shared with third-party providers. The objective is to make shopping more useful without collecting or using more personal data than necessary.
Over-Personalisation and Poor Recommendations
Highly personalised suggestions can become repetitive or too narrow. A customer who buys a gift may not want to see similar products on every future visit. Retailers should test recommendation diversity, provide useful alternatives and retain suitable manual merchandising controls.
Developing AI and eCommerce Skills Through Online Learning
Professionals working in eCommerce can strengthen their career prospects by learning how recommendation systems operate and how their performance should be evaluated. Useful skills include customer analytics, conversion rate optimisation, product data management, experimentation and AI literacy.
Marketing and merchandising teams do not necessarily need to build machine learning models themselves. However, they should understand how customer signals influence recommendations, how to identify poor-quality results and how to test whether personalisation improves business outcomes.
More technical professionals may benefit from learning Python, SQL, machine learning and recommender-system evaluation. These skills can support the development of custom recommendation models and more advanced integrations.
Online courses offer a practical way to develop these capabilities. The best choice depends on whether the learner wants to manage eCommerce AI tools, implement recommendation systems or understand the commercial principles behind personalisation.
Recommended Online Courses to Build AI eCommerce Skills in 2026
AI for E-Commerce: No-Code Tools for Sales & Marketing — Coursera
Platform: Coursera
Level: Beginner
Focus: No-code AI tools, smart product recommendations, customer analysis, eCommerce marketing and sales optimisation.
This practical course is designed for eCommerce professionals, business owners and marketers who want to use AI without programming. It covers product recommendations, AI-assisted customer experiences, customer segmentation and other applications that can support online sales.
It is a useful starting point for learners who want to understand how AI tools can be applied to everyday store operations.
Course link: AI for E-Commerce: No-Code Tools for Sales & Marketing — Coursera
Building Recommender Systems with Machine Learning and AI — Udemy
Platform: Udemy
Level: Intermediate
Focus: Recommendation algorithms, collaborative filtering, deep learning and Python-based recommender systems.
Created by Frank Kane and the Sundog Education team, this course provides a more technical introduction to building recommendation systems. The listing reports a 4.6/5 rating from approximately 3,948 ratings and more than 51,000 students, with an August 2026 update.
It is best suited to learners who want to understand how recommendation engines work beneath the user interface and develop practical machine learning skills.
Course link: Building Recommender Systems with Machine Learning and AI — Udemy
Building AI-Powered Retail Search and Recommendations with Vertex AI Search for Commerce — LinkedIn Learning
Platform: LinkedIn Learning
Level: Intermediate
Focus: Retail data ingestion, recommendation models, personalised placements, deployment and performance monitoring.
This course explores how to configure AI-powered search and recommendations using Google’s retail technology. It covers data preparation, model selection, deployment and performance monitoring.
The listing reports a 4.8/5 rating from nine reviews, so the rating is encouraging but based on a relatively small sample. It is particularly relevant to technical professionals and operations specialists working with cloud-based eCommerce systems.
Course link: Building AI-Powered Retail Search and Recommendations with Vertex AI Search for Commerce — LinkedIn Learning
Final Thoughts
AI-powered product recommendation tools are becoming an important part of eCommerce optimisation in 2026. Established platforms such as Nosto, Bloomreach and Dynamic Yield offer sophisticated personalisation and merchandising capabilities, while Clerk.io provides an integrated option for growing online stores. Google Vertex AI Search for Commerce is worth considering for businesses that need more technical control over AI-powered product discovery.
The right choice depends on the retailer’s catalogue, traffic, data quality, budget and internal expertise. Usability determines how easily teams can deploy and manage recommendations, while accuracy depends on relevant data and suitable models. ROI ultimately depends on whether the tool generates incremental profit that justifies its total cost.
Retailers should avoid choosing a platform simply because it advertises advanced AI. Instead, they should establish clear performance goals, test recommendations against a reliable baseline and evaluate the impact on conversion rate, average order value, revenue per visitor and gross margin. Businesses that combine relevant recommendations with reliable product data, careful experimentation and responsible data practices will be better positioned to improve product discovery and build more effective online shopping experiences.
