What if you could increase order value and repeat purchases without asking shoppers to do more work? An AI recommendation system helps your store surface the right products at the right moment, turning browsing data and product signals into more relevant shopping experiences.
Ecommerce stores can leverage artificial intelligence to analyze customer data and deliver more relevant recommendations. For many businesses, an AI recommendation system may provide the boost your sales and marketing teams need—and it can automate much of the recommendation process.
An AI recommendation system—also known as a recommendation engine—uses machine learning algorithms to suggest relevant products, services, or content to potential customers online.
AI recommendation systems collect and analyze data points like demographics, a user’s past behavior (reviews, ratings, search history, past purchases) and product attributes, creating personalized recommendations.
A wide range of industries use AI-powered recommendation engines:
For example, an ecommerce merchant might rely on an AI recommendation system to display data-driven suggestions to site visitors. On its storefront, Gymshark features recommendation collections and product suggestions.
people also boughtto guide product discovery. Source: Gymshark recommendations
AI recommendation systems can increase sales by helping shoppers discover relevant products faster, raising average order value with cross-sells and bundles, and encouraging repeat purchases through more personalized experiences.
A good AI-powered recommendation system can personalize your online store experience, which can support repeat purchases and higher satisfaction. In recent years, consumer research from Statista’s personalization in ecommerce coverage has continued to show strong demand for more relevant shopping experiences. One Statista survey of online consumers found that many shoppers wanted personalized product recommendations, highlighting demand for more tailored ecommerce experiences.
By personalizing the shopping experience, AI recommendation systems can also support customer retention and improve the overall customer experience. In practice, this can translate into measurable gains: beauty company Orveon Global reported an immediate 10% to 15% lift in average order value after rolling out AI-powered merchandising and product recommendations across its brands.
“Immediately—and this was consistent across every brand—we saw an AOV lift between 10% to 15% for each brand. So I think our ability to cross-sell with Nosto live drove an immediate sales lift.”
— Carney Nir, VP of ecommerce and site experience at Orveon Global (Source)
Key benefits include:
Here are a few practical ways to apply recommendations across your store:
Real-world example: Finisterre used Shopify customer insights and AI tools with its customer data platform to target shoppers based on previous purchase behavior and location, helping customers find relevant products faster across touchpoints. That’s a useful reminder that recommendation quality often improves when you combine product signals with context like geography and purchase history.
If you are just getting started, begin with one placement and measure it carefully before expanding across the full customer journey.
You can generate personalized recommendations for potential customers using one of three main types of AI recommendation systems. In general, content-based systems are useful when you have limited user-behavior data, collaborative systems work best when you have a larger volume of customer interactions, and hybrid systems are often the best fit for more mature stores that want broader coverage and better accuracy.
| Type | How it works | Best for | Main limitation | Example use case |
|---|---|---|---|---|
| Content-based | Recommends items with attributes similar to products a shopper has viewed or purchased | Stores with rich product data and limited behavior data | Can become too narrow if it only suggests similar items | Showing similar skin care products based on ingredients or product type |
| Collaborative | Uses patterns from many shoppers’ actions to suggest what similar users tend to buy | Stores with higher traffic and more order history | Needs enough user activity to perform well | Showing “customers also bought” suggestions based on past orders |
| Hybrid | Combines product attributes with shopper behavior signals | Growing or mature stores that want stronger accuracy | Can be more complex to set up and tune | Combining related-product suggestions with frequently bought together bundles |
A recommendation engine using content-based filtering algorithms makes recommendations based on the specific characteristics (features, categories, descriptions) of items a user already likes, rather than what other users do. By analyzing product details such as category, material, price range, and tags, the system can recommend items with similar attributes to those a shopper has previously engaged with.
This works particularly well for niche markets with a limited customer base because there may not be enough browsing, review, or purchase data to power strong behavior-based recommendations yet. In those cases, product attributes do more of the work. For example, a specialty tea store with a small audience can still recommend similar loose-leaf blends based on flavor profile, caffeine level, origin, and brewing style.
For example, merchants in the Shopify app ecosystem can use tools that offer AI-based product recommendations, such as Shopcast. It is one example among many apps in the Shopify App Store, not a Shopify endorsement.
Collaborative filtering predicts a user’s preferences based on the behavior of similar users. Collaborative filtering systems may consider browsing history, purchase history, or ratings. This type of recommendation engine suggests products it thinks a shopper will like based on customer data from users with similar tendencies.
For example, a clothing brand might recommend a new clothing line to a particular user based on recent fashion purchases of users with similar tastes. As opposed to content-based filtering, collaborative filtering is useful for suggesting items not directly related to products the user has viewed but that similar customers have purchased.
For example, a frequently bought together app can analyze store sales history, new orders, and product updates in real time to recommend products commonly purchased together.
Hybrid recommendation systems combine collaborative and content-based filtering to recommend relevant content, products, and services to users. For example, hybrid systems could predict the type of cookware that might interest a customer based on what other users with similar preferences have chosen, and specific content-based connections in product features, materials, or sizes.
By collecting data about similarities between users and content-based factors like product descriptions, a hybrid recommendation system combines multiple filtering methods to get a highly informed and personalized recommendation.
Many AI recommendation engines rely on a hybrid approach, applying both collaborative and content-based filtering, a pattern discussed in recent research on recommender systems. For example, some Shopify apps use AI and order history to generate frequently bought together recommendations, while others create recommendations and bundles based on order history and sales data. Large catalogs can also benefit from pairing recommendations with smarter search: after adopting Shopify’s Google Cloud Discovery AI integration, Rainbow Shops reported a 48% increase in site search volume, underscoring how AI-driven discovery tools can complement recommendation systems.
An AI recommendation system uses shopper behavior and product data to suggest items a customer is more likely to want. In ecommerce, it often appears on homepages, product pages, cart pages, checkout flows, and post-purchase messages.
It can improve product discovery, increase average order value through cross-sells and bundles, and encourage repeat purchases with more relevant suggestions. Start by testing one high-intent placement, then track click-through rate, conversion rate, and average order value to confirm impact.
The amount depends on the method you use. Content-based systems can work with strong product data alone, while collaborative systems usually need more traffic, browsing activity, and order history before recommendations become reliable.
Recommendation systems can raise privacy and security concerns when they rely on large amounts of customer data, and they may struggle with cold-start problems for new stores or new products. To reduce risk, disclose data use clearly, obtain consent where required, follow laws such as the GDPR, and review recommendations regularly for relevance and variety.
Start with one simple recommendation placement, such as related items on product pages or bundles in the cart. Small stores often get the fastest results by combining clean product data with straightforward rules, then improving the setup based on performance.
An AI recommendation system can help you make product discovery easier, lift average order value, and create a more personalized experience that keeps customers coming back. The biggest gains usually come from matching the right recommendation type to your store’s data and placing suggestions where buying intent is strongest.
Start with one placement, measure the results, and refine from there. If you’re ready to build smarter shopping experiences with tools designed for commerce, explore Shopify and start testing AI-powered recommendations in your store today.
