TL;DR
- AI product recommendations are intelligent, data-driven product suggestions that use machine learning algorithms to predict what shoppers want, creating tailored experiences that drive more sales.
- AI product recommendations work by using collaborative, content-based, or hybrid filtering methods to recommend items each shopper is most likely to buy.
- The benefits of implementing AI product recommendations include improved user experience and retention, increased average order value through up-and cross-selling, and higher conversions.
- Maropost Merchandising Cloud helps ecommerce businesses improve customer experience and increase sales by combining personalized product recommendations with dynamic displays and AI-powered search.
Today’s online shoppers increasingly expect highly personalized experiences based on their preferences, buying history, and browsing behavior. And the numbers back it up. According to McKinsey, 71% of consumers want a personalized experience, and 76% get frustrated when this doesn’t happen. That’s where AI-powered product recommendations step in.
But here’s the kicker: while most ecommerce businesses are actually sitting on goldmines of customer data, they lack the tools to turn that valuable data into personalized shopping experiences. The good news? Today, nearly any ecommerce brand can easily implement an AI-powered product recommendation engine and start driving more sales almost instantly.
In this article, we’ll break down what AI product recommendations are, how they work under the hood, and what benefits you can expect from the AI product recommendation system. On top of that, we’ll show you how implementing Maropost Merchandising Cloud can help you drive even more sales by combining product recommendations with AI-powered search.
What are AI product recommendations?
AI product recommendations are product suggestions generated by Artificial Intelligence (AI) and Machine Learning (ML) algorithms to help online shoppers discover products they are likely to buy. These algorithms analyze customer browsing behavior, purchase history, and engagement patterns to automatically suggest relevant products, enhancing customer experience and helping you boost conversions and increase the average order value.
As an online shopper, you’ve surely seen them in action: those are “You might also like”, “Customers who bought this also purchased”, and other types of popups or widgets sprinkled across ecommerce websites.
It’s just like having a smart sales assistant who never forgets customer preferences, never sleeps, and is always ready to help shoppers make faster purchase decisions whenever they visit your ecommerce website.
This sales assistant watches how shoppers interact with your online store: what products they browse, how long they stay on every page, what they add to their shopping carts, and what they buy or don’t buy. And it then uses this information to predict what shoppers are most likely to purchase next and deliver relevant recommendations at the right time, serving thousands of shoppers simultaneously.
The best part is that the AI recommendation system can process historical data across thousands (or millions) of interactions, learn from patterns in shopper behavior, and become smarter over time – adapting to changing trends and shopper preferences automatically.
How do AI-powered product recommendations work?
AI product recommendation systems aren’t based on guesswork. They collect data and use machine learning algorithms to analyze this data and deliver relevant product suggestions based on these approaches:
1. Collaborative filtering
With collaborative filtering, the algorithm identifies patterns across your entire customer base, linking products based on shared behavior. This is basically the “Customers who bought this also purchased” approach. For example, if shoppers often purchase yoga mats and water bottles together, the algorithm recognizes this trend and recommends water bottles to future customers browsing yoga mats.
2. Content-based filtering
Instead of looking at what other users bought, content-based filtering focuses on the specific attributes and features of products a user has shown interest in to recommend similar items. For example, if someone consistently views black dresses in size S, the algorithm will prioritize showing them other black dresses in size S.
3. Hybrid filtering
As the name suggests, the hybrid filtering approach uses both methods to provide more accurate, relevant, and diverse recommendations. Netflix is an excellent example of a brand using the hybrid filtering approach. Their recommendation engine combines both content-based (what genres you prefer) and collaborative (what similar users watched) methods. As a result, over 80% of viewing activity on Netflix comes from recommendations rather than user search.
What are the benefits of implementing AI product recommendations?
AI-powered recommendations are truly reshaping ecommerce. And the numbers say it all: the global recommendation engine market is expected to grow from USD 7.34 billion in 2025 to over USD 119.43 billion by 2034, according to Precedence Research.
The reason for this impressive growth is simple – AI product recommendations are solving the real problems ecommerce brands deal with every day.
✔️ Improve user experience and customer retention:
First and foremost, shoppers want personalization, and an AI product recommendation engine helps you deliver just that. It saves customers time by surfacing products they genuinely want, right when they’re looking for them. And when the experience improves, so does customer loyalty and retention. According to the State of Personalization report from Twilio/ Segment, 62% of business leaders cite improved customer retention as a benefit of personalization efforts.
✔️ Increase average order value through upselling and cross-selling:
Next, AI product recommendations help you increase the average order value through smart upselling and cross-selling. Based on the same report mentioned above, 80% of business leaders say consumers spend more (38% on average) when their experience is personalized.
Instead of hoping your customers stumble upon the items they need, AI steps in to offer helpful suggestions that fit perfectly with what they’re already buying. For example, someone buying running shoes might see suggestions for athletic socks, a water bottle, and a fitness tracker. That can turn a single purchase into a complete workout set!
✔️ Drive higher conversation rates and sales:
Finally, AI-powered personalized product recommendations help you dramatically improve conversions and sales. When shoppers instantly see products that actually match their needs and interests without having to look for them on your website, they are more likely to make a purchase.
Retail giants like Amazon and Walmart have been actively using AI algorithms for years to provide tailored recommendations. For example, Amazon’s product recommendation engine, powered by AI, is responsible for 35% of the company’s total sales. That’s literally billions of dollars generated by simply showing customers the right products at the right time in their purchase journey.
Start implementing AI product recommendations with Maropost Merchandising Cloud today
With an AI product recommendation system in place, you’re not just showing related products to your customers. You personalize product recommendations based on their unique behaviors, creating the experience that generic product displays simply can’t match. On top of that, you’re improving loyalty and retention, increasing conversions, and driving more sales through upsell and cross-sell opportunities.
If you’re ready to get started with an AI product recommendation system, here’s what Maropost Merchandising Cloud delivers – and why it’s the top solution for ecommerce brands:
- Dynamic product displays: Easily tailor product suggestions to your shoppers with a wide range of dynamic widgets, including ‘Hot Sellers’, ‘Recently Viewed’, ‘Newest Products’, ‘Frequently Purchased Together’, ‘Customers Who Viewed This Also Viewed’, and ‘Customers Who Viewed This Ultimately Bought’. With full control over how products are displayed, you can inspire more clicks, conversions, and repeat purchases.
- AI-powered personalized recommendations: Go beyond standard merchandising and deliver truly personalized AI product recommendations based on each shopper’s purchase history, unique behavior, and preferences. Enable more tailored shopping experiences, capitalize on upselling and cross-selling opportunities, and drive more revenue for your ecommerce business without manual curation.
- AI-driven search and product discovery: Improve product discoverability and help customers find relevant products faster with AI-powered search. Features like autocomplete, spell correction, and semantic search ensure shoppers get relevant results even with typos or vague searches, while AI-driven personalized ranking continuously learns from behavior to boost conversion-likely products to the top of search results.
- A complete unified commerce platform: Merchandising Cloud isn’t just an AI product recommendations system. It’s part of Maropost’s Unified Commerce platform that brings together ecommerce management, marketing automation, and customer data across all touchpoints in one place. Ultimately, it eliminates data silos and the need to juggle multiple disconnected systems to run your ecommerce business.
Book a demo now to see Maropost Merchandising Cloud in action and discover how it can help you improve customer experience, increase conversions, and drive more revenue with AI-powered search, product discovery, and personalized product recommendations.
Frequently asked questions
What's the difference between rule-based and AI-powered recommendations?
Rule-based product recommendations operate on predefined “if-then” logic, like if a user views product A, then recommend product B. They are predictable and simple to implement, but they provide the same product suggestions to all shoppers because they don’t learn or adapt to individual user behaviors.
AI-powered recommendations, on the other hand, use machine learning to analyze customer behavior in real time, predict what each shopper is likely to buy, and deliver personalized suggestions to each user. Ultimately, AI recommendation systems create more personalized experiences and deliver better performance.
What data do I need to start using AI product recommendations?
To start using AI product recommendations, you need data on your products, customer interactions, and transactions. That includes product catalog details, purchase history, browsing behavior, search queries, time spent on pages, cart additions, customer engagement metrics, and demographic details. The more clean and accurate data you have on what your shoppers do and what products they like – the better your AI recommendation system can learn and personalize its suggestions to each individual user.
How long does it take to see ROI from AI product recommendations?
That depends on the complexity of your implementation, product catalog size, traffic volume, and the quality of your data. But most ecommerce businesses start seeing measurable results (increased conversion rates and average order value) within a few weeks after implementing an AI product recommendation engine.
As the AI learns from more customer interactions over time, you can expect to see greater ROI within 3-6 months. High-traffic websites with diverse catalogs typically see results faster as algorithms have more data to learn from.