Imagine a shopper entering your online store and feeling like the entire experience was crafted just for them. 

With the expansion of AI and machine learning, personalized product recommendations have become the go-to tool for businesses that want to boost customer engagement and drive more revenue.

Recent studies show that:

• Product recommendations can account for up to 31% of an ecommerce store’s revenue.
• Engagement with personalized recommendations can increase average order value (AOV) by 369%.

This article will explore how AI-driven recommendation engines transform retail. We'll explore the top 5 product recommendation engines, so you can choose the right fit for your business. We'll also touch on how these tools support upselling, cross-selling and smarter personalization.

What is a product recommendation engine?

Think of a product recommendation engine as your top-performing salesperson.

A recommendation system analyzes customer data and behavior to show products your visitors will most likely love and buy.

How it works

Product recommendation systems ingest behavioral data: clicks, carts, conversions, wish lists and even dwell time. Then, they find patterns, e.g., “People who bought noise-canceling headphones also tend to buy these ergonomic chairs”.

Modern engines typically use:

• Artificial intelligence & machine learning: To continuously improve results based on real-time user behavior, preferences and outcomes.
• Rules-based systems: These offer more manual control, with if-this-then-that logic.

Where recommendations appear

These engines follow the customer journey. Recommendations are:

• On your website: Homepage, product pages or cart page
• In email campaigns: Abandoned cart reminders or post-purchase suggestions
• In SMS messages: Personalized nudges like “Hey, forgot this?”
• In ads: When that perfect pair of shoes follows your site visitors across the internet

Types of recommendations

Not all suggestions work equally well. Here are some high performers:

• Frequently bought together: A classic recommendation type that suggests similar or complementary products, like pairing batteries with electronics or belts with trousers.
• Recently viewed: Keeps track of previously explored items to make it easier for shoppers to return to them.
• Customers also bought: Recommendations based on what other users and shoppers frequently purchased alongside the current item; it uses crowd behavior and collaborative filtering to build trust and boost conversion rates.
• Personalized for you: Tailored product suggestions based on a user's behavior, preferences, past purchases and real-time activity.

When done right, recommendation engines improve user experience and quietly grow revenue, one “You might also like…” at a time.

Modern product recommendation engines vs outdated recommendations

Showing everyone the same ten “best-selling” products doesn’t cut it anymore.

Traditional product recommendation engines are easy to implement, but they lack context. They treat all users equally, ignoring intent, timing and individual preferences. The result? Missed opportunities, irrelevant suggestions and bounces.

Modern hybrid recommendation systems use content-based, context-aware data filtering systems to understand purchase history and user behavior. Here's what we mean:

• Abandoned cart recovery with smart upsell: A user adds a coffee machine to their cart but doesn't check out. Instead of sending a bland reminder, modern engines send a personalized email or WhatsApp message that includes the machine plus a recommended milk frother (content-based filtering systems).
• Product discovery without filters: Intelligent engines help new users discover products based on subtle cues, like what they linger on, what they ignore and what others from the same segment explore (collaborative filtering systems). The result is a more intuitive experience that feels like a well-curated conversation.
• Bundles that convert: Dynamic bundling uses actionable data insights to create relevant product pairings. Like wireless earbuds suggested with a gym bag for a fitness enthusiast, not just because the buds are “frequently bought together”, but because the engine understands that customer's lifestyle. This bundling is about increasing average order value with combinations that make sense to the specific customer.

Top 5 product recommendation engines

Whether you're optimizing for conversions, customer retention or want a better user experience, choosing the right recommendation engine is essential. Here are five that commerce teams trust.

Loadstone

Enterprise businesses need more than a plug-and-play customer data widget. You need an entire system that understands, adapts to and acts on customer behavior in real time, delivering personalized experiences without bogging down.

Enter: Loadstone.

Built by e-commerce professionals with 12+ years of expertise, it's a holistic, composable martech ecosystem that combines advanced AI, personalization and omnichannel strategies under one platform.

It's ideal for large-scale retailers, multichannel commerce brands and enterprises managing many high-traffic customer segments and multi-platform experiences.

There's a reason why our customers stay with us for an average of 6+ years: our software adapts to your needs, handling millions of users and infinite possibilities.

Key features

The Loadstone product we’re focusing on today is the AI Recommender System that delivers personalized shopping experiences across digital and physical retail environments.

It uses over 35 advanced algorithms to analyze user behavior, preferences and contextual data, enabling precise product suggestions that drive engagement and sales. Here are some of its key features:

• Personalized, relevant recommendations: Machine learning understands individual customer journeys, offering tailored product suggestions that increase average order value and conversion rates
• Cross-sell and up-sell capabilities: Loadstone identifies complementary and premium products that customers are likely to purchase together, enhancing cart value and customer satisfaction
• Real-time adaptation: Recommendations reflect changing preferences and behaviors based on user interactions
• Scenario-based logic: Business rules handle specific scenarios, such as promoting back-in-stock items or seasonal products, ensuring relevance and timeliness
• Multi-channel integration: The system integrates with web platforms, mobile apps, email campaigns and in-store point-of-sale systems, providing a consistent customer experience across all touchpoints
• Comprehensive analytics dashboard: Monitor recommendation performance, track customer interactions and get insights into shopping trends

Loadstone AI Commerce Search can double or even triple your conversion rates with to its modular, composable architecture. It consists of interchangeable components—recommendation engine, audience segmentation, real-time messaging and product bundling—that you can activate individually or integrate. For example, get the Loadstone loyalty campaign module alongside your existing CRM; then plug in its promotional marketing for dynamic targeting.

💡Curious how Loadstone fits into your tech stack? Talk to our expert or get a quote.

Integrations

Loadstone's open API architecture gives you freedom. Whether you're using Salesforce Commerce Cloud or Shopify Plus, Loadstone can connect to it so you can:

• Plug into CDPs, CRMs, ESPs and ad platforms: Get behavioral data from all touchpoints, like online store, mobile app and support channels and feed it into Loadstone's engine.
• Get streamlined workflows across teams: Marketing, merchandising and data teams can work from the same source of truth without switching between systems.
• Integrate with in-store systems: Bridge the gap between online and offline by connecting Loadstone to your POS or inventory management systems, enabling unified customer experiences across channels.

Considering a switch from Salesforce? Check out our breakdown of the top Salesforce competitors and how they compare in features, flexibility, and pricing.

Dynamic Yield

Dynamic Yield is an AI-powered personalization platform designed for large retailers. The tool helps create tailored experiences across every digital touchpoint.

It's best for omnichannel retailers and enterprise brands looking for personalization and experimentation across web, mobile apps, email and kiosks.

Key features

• AI-powered product recommendations: Multiple algorithms (collaborative filtering, deep learning and affinity-based models) deliver personalized recommendations that adapt in real time.
• Experience OS: Dynamic Yield centralized platform lets you create product carousels, banners and overlays that can be customized, tested and optimized.
• Server-side APIs: Dynamic Yield's server-side architecture ensures personalization at scale without slowing down performance or breaking design systems.

Integrations

Dynamic Yield offers integrations and flexible APIs. You can connect it to:

• CDPs and analytics tools: Segment, mParticle, Google Analytics, Adobe Analytics and more
• E-commerce platforms: Shopify Plus, Salesforce Commerce Cloud, Magento and custom platforms
• Messaging and A/B testing tools: Braze, Iterable or Optimizely to test experiences across channels

Algolia Recommend

Algolia Recommend is an AI-driven product discovery platform. It has a developer-friendly ecosystem that provides a recommendation engine.

It's best for commerce teams and developers looking to build customized recommendation strategies and experiences within a modern headless or composable architecture.

Key features

• Behavior-based product recommendations: Algolia Recommend utilizes user preferences and behavior, click-through data and product data relationships to generate suggestions like Trending Products and Related Items.
• AI-Powered and real-time: Algolia's AI models update in real time, reflecting changes in inventory, similar user behavior or trends.
• Built-in merchandising controls: Fine-tune the recommendation logic with rule-based overrides, pinning, and exclusions

Integrations

Algolia Recommend is modular; it can work in a composable or headless environment:

• Frontend frameworks: React, Vue, Angular and other JavaScript frameworks
• Commerce platforms: Shopify, Magento, Salesforce Commerce Cloud and custom storefronts
• Third-party data: CDPs, analytics tools and personalization engines

Not sure whether you need a CDP or a CRM? Our guide breaks down the key differences between CDP vs. CRM to help you choose the right tool for your business.

Nosto

Nosto is a commerce experience platform that helps retailers deliver personalized shopping journeys. It combines real-time data, AI and merchandising tools into one cohesive system. It's best for mid-sized to large commerce brands.

Key features

• AI-powered product recommendations: The Nosto recommendation engine uses behavioral data, previous purchases and machine learning algorithms.
• Segmentation and targeting: Customer segments based on in-session behavior, lifetime value, geo-location, device and more.
• Visual merchandising: Drag-and-drop merchandising and pinning specific products within recommendation widgets.
• A/B testing and analytics: Native testing tools allow teams to experiment with recommendation logic and segment strategies.

Integrations

Nosto is built for commerce, so it works with multiple e-commerce platforms and marketing tools:

• E-commerce platforms: Shopify Plus, Magento, BigCommerce, Salesforce Commerce Cloud and others
• Email and marketing tools: Klaviyo, Mailchimp and HubSpot in email campaigns
• CDPs and analytics: Segment, Google Analytics and other data tools

Klevu

Klevu combines AI-powered search, product discovery and personalized recommendations in a commerce-optimized platform.

It's built for e-commerce and is best for retailers who want to turn every search and navigation interaction into a personalized product discovery opportunity.

Key features

• AI product recommendations: The Klevu recommendation engine uses machine learning to offer personalized suggestions. These adapt in real time based on user behavior and product popularity.
• Search and recommendation synergy: Similar to Loadstone, Klevu's search and recommendation engines work together. For example, if a customer searches for “black running shoes”, the system recommends related accessories or similar styles based on intent.
• Visual recommendations: Klevu includes AI visual search, allowing users to upload images and receive lookalike product suggestions.

Integrations

Klevu is designed for commerce-first integration. It supports major platforms and has flexible APIs for custom setups:

• E-commerce platforms: Shopify, BigCommerce, Magento, Salesforce Commerce Cloud and headless environments
• Marketing and personalization tools: Klaviyo, Yotpo and Segment
• Open API access: API access to embed recommendations and search logic in the customer journey

The best product recommendation engines reviewed

A modern, foolproof product recommendation engine

A product recommender system is more than an upsell tool. It's a driver of revenue and customer loyalty.

As shopper expectations rise and digital journeys become more complex, you need a solution beyond generic suggestions—one that understands user data, intent, adapts in real time and scales with you.

Choosing the right platform depends on your brand's needs: the level of personalization you want and the flexibility you need. Pick a tool that fits not just your stack, but your strategy.

For enterprise retailers operating at scale, Loadstone delivers measurable impact. Its intelligent product recommendation engine drives higher conversions, larger basket sizes and stronger customer loyalty. Moreover, its composable architecture ensures integration across channels and systems, adapting as your business evolves.

Explore what Loadstone can do for your team, talk to our expert and get a quote today.

FAQs

What is a product recommendation engine? 

It’s a system that suggests relevant products to users based on their behavior, preferences or context. It helps businesses personalize the shopping experience and increase conversions.

What is an example of a recommendation engine? 

Loadstone is a powerful example. Enterprise retailers use it to deliver real-time, AI-driven product suggestions across web, mobile, email and even in-store experiences.

How does a recommendation engine work? 

It analyzes data like browsing history, purchase behavior and contextual signals using AI and machine learning to suggest products that are most likely to convert. Loadstone’s engine goes further by integrating into the full customer journey.

What is the best recommendation engine? 

For enterprise retailers seeking excellent performance, flexibility and deep personalization, Loadstone stands out with its composable architecture and intelligent, customizable recommendation capabilities.

‍