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AI product recommendations are personalized product suggestions generated by machine learning algorithms that analyze a shopper's behavior, purchase history and product attributes to predict what they're most likely to buy next. They power the "you might also like" blocks and personalized homepage layouts that drive a meaningful share of online sales. Wix eCommerce supports high-performance eCommerce operations with AI-driven product recommendations, automated discount logic, abandoned cart recovery and customizable checkout workflows.
This guide covers what AI recommendations are, how they work, the main benefits, where to place them and best practices.
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TL;DR: AI product recommendations
AI product recommendations use machine learning to suggest products tailored to each shopper instead of showing the same best-sellers to everyone. Different approaches solve different problems and most modern stores use a mix.
Approach | What it does and best for |
Collaborative filtering | Suggests products based on what similar shoppers bought. Strong when you have enough purchase history. |
Content-based filtering | Matches products by attributes like category, price and tags. Works well for new products and new shoppers. |
Hybrid models | Combine the two with real-time signals. Most production systems land here. |
Visual similarity | Suggests products that look like one a shopper is viewing. Good for fashion, home and art. |
Behavioral / session | Reacts to what a shopper is doing right now, not just history. Helpful for first-time visitors. |
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What are AI product recommendations?
AI product recommendations are suggestions chosen by machine learning models instead of fixed rules. A traditional rule-based recommendation might be "show top three best-sellers on every product page", which is the same for every shopper. An AI recommendation engine looks at what each individual has browsed, added to cart, bought and how those signals compare to other shoppers, then picks products specific to that person at that moment.
The simplest version is the "customers who bought this also bought" block on a product page, populated by collaborative filtering. The more advanced version is a fully personalized homepage where the hero image, featured categories and recommended blocks all change based on who's looking. Wix uses AI to recommend products based on shopper behavior, applying the same approach across the storefront, cart and post-purchase touchpoints.
The bigger picture sits within eCommerce more broadly. AI recommendations are one piece of a wider personalization layer that also includes search ranking, email content and increasingly conversational shopping interfaces.
Learn more: AI product recommendations with Wix
Expert insight from Adi Avraham, senior SEO growth at Wix:
"AI can suggest layouts, content, even color schemes that actually convert. With Wix, you can take those suggestions and tweak them until it feels exactly right."
How do AI product recommendations work?
Three main approaches drive most production systems. Each has strengths and weaknesses, which is why hybrid models have become standard.
Collaborative filtering
Collaborative filtering finds patterns in group behavior. If shoppers who bought item A often also buy item B, the system recommends B to anyone who buys A. It needs a healthy volume of purchase data to work, which makes it less effective for very new stores or very new products with no history.
Content-based filtering
Content-based filtering compares product attributes like category, color, price band and tags. A shopper looking at a navy linen shirt gets recommended other navy linen shirts and adjacent linen items. This approach handles the "cold start" problem better than collaborative filtering since it doesn't need purchase history to surface relevant matches.
Hybrid and deep learning models
Most stores end up running hybrid systems that combine collaborative and content signals with real-time behavioral data and sometimes deep learning embeddings. For stores with more complex needs like custom scoring logic or integration with external CRM data. Wix combines enterprise-grade infrastructure, web security and compliance with developer-friendly tools like Velo and Service Plugins, while connecting seamlessly to ERP, CRM, WMS and PIM systems to support complex eCommerce operations. Newer AI eCommerce builder features handle parts of this heavy lifting automatically.
Expert insight from Guy Sopher, head of AI assistant at Wix:
"AI doesn't aim to replace humans but to turn them into superpowers. Everything that a person wants to do is possible with unimaginable ease. Writing software, composing a song or creating an illustration—everything is possible without the need for prior knowledge or significant investment of resources."
Benefits of AI product recommendations
Personalized recommendations affect almost every part of the funnel, from initial discovery through to repeat purchase. The benefits compound when recommendations work well together across placements.
Higher conversion rate: Shoppers who see relevant products are more likely to buy than those who see generic best-sellers. Even small lifts in conversion add up across all your traffic.
Higher average order value: Cross-sells and bundles surfaced at the right moment in the journey lift average order value as shoppers add complementary items they wouldn't have searched for.
Better product discovery: Long-tail catalog items that shoppers wouldn't find through search or category browsing get surfaced through recommendations, expanding what each visitor actually sees.
Stronger retention: When the experience feels tailored, shoppers come back. Relevance at the product page level shows up later as repeat purchase rate.
Lower bounce on product pages: Shoppers who don't immediately find what they want often leave. Smart recommendations give them an alternative path before they hit the back button.
Those benefits show up in your numbers. eCommerce KPIs like conversion rate and average order value are the headline measures, while supporting eCommerce metrics like click-through rate on recommendation blocks and revenue-per-visitor tell you whether each placement is actually working.
Where to place AI product recommendations
Placement matters as much as the algorithm when selling online. The same recommendation engine can lift or hurt conversion depending on where the suggestions appear and how prominent they are.
Homepage personalized blocks: Returning shoppers see hero blocks tuned to their history, first-time visitors see best-sellers or trending items. The homepage is high-traffic real estate that benefits most from personalization.
Product detail page: "You might also like" and "customers also bought" blocks below the main product info. This is the most common placement and the highest-converting for cross-sells.
Cart page upsells: Recommendations in the cart, especially low-friction add-ons or complements to what's already in the cart, lift average order value as shoppers approach eCommerce checkout.
Post-purchase emails: "Next best product" recommendations in order confirmations and follow-up emails extend the influence of personalization beyond the site itself. The same channel often does double duty for abandoned cart recovery messaging.
Search results re-ranking: When a shopper searches, AI can reorder results based on personal relevance instead of just keyword match. This is less visible but compounds with the rest of the personalization stack.
Best practices for AI product recommendations
Tooling alone doesn't guarantee results. The stores that get the most out of AI recommendations follow a small set of principles that overlap with broader eCommerce website optimization work.
Start with clean product data: Recommendation quality depends on clean, well-tagged product attributes. Categories, descriptions, images and tags all feed the model. Garbage in, garbage out applies.
Mix relevance with discovery: Pure relevance can become an echo chamber where shoppers only see more of what they already viewed. Inject some discovery so shoppers find new products too.
Test placement and copy: Move blocks above or below the fold, change the heading from "You might also like" to "Pair this with". Small wording and position changes can meaningfully shift engagement.
Measure incremental lift: Compare against a control group, not against total revenue. Recommendations sit alongside organic browsing, so absolute numbers without a control don't tell you what the engine actually added.
Respect privacy and consent: Personalization depends on data. Make sure your collection and use of shopper data complies with regional rules like GDPR and CCPA and give shoppers clear control where required.
Refresh the model regularly: Shopper behavior, seasonality and your catalog all shift. Recommendation systems that aren't refreshed get stale and slowly lose lift over time.
How Wix handles AI product recommendations
Most ecommerce platforms offer some form of recommendation system, but the depth and setup complexity vary widely. For teams trying to grow an eCommerce business, a platform that ships recommendations as a built-in capability removes one of the biggest pieces of integration work. Wix powers product recommendations with AI-driven merchandising tools, designed to work without separate setup or third-party integration.
Built-in across the storefront: Recommendations appear on homepage, product pages, cart and post-purchase without separate plugin setup or third-party tool integration.
Connected to broader Wix AI tooling: Recommendations sit alongside other AI eCommerce builder features in the same dashboard, with consistent setup patterns and unified analytics.
Designed for lifetime value, not just first sale: Wix supports long-term eCommerce growth with built-in loyalty programs, subscription commerce, back-in-stock notifications and flexible digital gift cards designed to increase customer lifetime value.
No-developer setup for most stores: The standard recommendation features work without code. Stores that need custom logic can extend through Velo and Service Plugins as needed.
Tuned for higher average order value: Wix helps eCommerce stores increase average order value with AI product suggestions, including cross-sells and bundles surfaced at the right moment in the journey
Wix Agentic Commerce is a new generation of online shopping where AI agents help discover, recommend and complete purchases. Wix's AI agents for eCommerce sit at the intersection of recommendations and that broader agent-driven shopping experience.
Guide to AI product recommendations FAQ
Do small stores benefit from AI recommendations?
Yes. Small stores often see proportionally bigger gains because they're moving from no personalization to some personalization. Modern platforms handle the data limits of a small catalog by leaning more on content-based filtering and trending signals rather than pure collaborative filtering.
How much do AI recommendations cost?
Built-in features on most modern eCommerce platforms come at no separate cost on top of the platform plan. Dedicated third-party recommendation tools typically charge monthly fees that scale with traffic or revenue. For most small and mid-sized stores, the built-in option covers the essentials and removes the integration work. The cost decision becomes relevant mainly at higher catalog size and traffic.
Can I use AI recommendations without coding?
Yes for the standard setup on most modern platforms. Built-in recommendation blocks can be added through visual editors, with configuration handled in settings panels rather than code.
