Quick Answer: Shopify product recommendations use built-in algorithms to display related products based on order history and product data, though merchants can also manually curate complementary items or use third-party apps for advanced personalization. Native features work well for established stores with sufficient data, while newer or larger catalogs often benefit from specialized recommendation apps that offer enhanced AI-driven personalization and control.
Picture this: A customer lands on your store, falls in love with a vintage band tee, adds it to cart, and… leaves. You had the perfect leather jacket that would’ve gone with it, the one they’d have totally bought. But they never saw it. That’s basically leaving money on the table, and it happens way more often than most merchants realize.
Product recommendations aren’t just nice-to-have features anymore. They’re the digital equivalent of a knowledgeable sales associate who knows exactly what pairs well with what. Except they work 24/7, never take breaks, and don’t awkwardly hover while customers browse.
Whether you’re running a boutique with fifty products or a sprawling catalog with thousands of SKUs, understanding how to leverage recommendations effectively can transform browsers into buyers and single purchases into cart-stuffing shopping sprees.
What Are Shopify Product Recommendations?
Shopify product recommendations are automated or manually curated product suggestions displayed to shoppers while they browse your store. Think of them as your store’s built-in shopping assistant, suggesting items based on what customers are viewing, what others have purchased together, or what you strategically want to highlight.
The platform offers native recommendation features that analyze order patterns, product descriptions, and customer behavior to generate suggestions automatically. These typically appear as “You might also like” or “Related products” sections on product pages, though theme design determines exact placement and styling.
Here’s what makes recommendations powerful: they tap into shopping psychology. When someone’s already interested in a product, they’re mentally primed to consider related items. It’s the difference between a $45 order and a $120 order, all from showing the right complementary pieces at exactly the right moment.
Native vs. Third-Party Recommendation Systems
Shopify’s built-in system handles the basics competently, but it’s kinda like the difference between a standard sedan and a sports car. Both get you there, but the experience and control levels differ significantly.
Native recommendations require zero setup cost and integrate seamlessly with your theme. They analyze your store’s transaction data automatically and start suggesting products without any manual configuration. For stores with consistent sales patterns and straightforward catalogs, this often provides sufficient functionality.
Third-party apps enter the picture when merchants need sophisticated features like cross-selling logic, behavioral personalization, or granular control over recommendation algorithms. Apps like Glood.AI and SF Product Recommendations specialize in ai product recommendations ecommerce, offering machine learning models trained specifically for conversion optimization.
How Shopify’s Native Product Recommendations Work
Let’s pause for a sec and break down what’s actually happening behind the scenes with Shopify’s built-in system. It’s not magic, though it can feel that way when it starts driving additional revenue.
The algorithm examines historical order data, looking for products frequently purchased together or in sequence. If fifty customers bought the same sneakers and socks combo, the system learns that pattern and suggests socks when someone views those sneakers. Product titles, descriptions, and tags also feed the algorithm, helping it identify thematically related items.
Related Products: The Automatic Option
Related products generate automatically once your store accumulates sufficient transaction history. Shopify’s algorithm does the heavy lifting, identifying correlations between products without merchant input.
The catch? New stores sit in a weird limbo period where the algorithm lacks data to work with. You might see generic suggestions or nothing at all until you’ve processed enough orders for patterns to emerge. This data dependency means stores in their first few months often benefit more from manual curation or third-party solutions.
Additionally, you can’t exclude specific products from appearing as recommendations using native features alone. If you’ve got a clearance item you don’t want promoted, you’ll need theme customization or app assistance to remove it from suggestion pools.
Complementary Products: Manual Control
Complementary products flip the script, giving merchants direct control over what gets recommended. You manually select which products appear as suggestions for specific items, creating curated pairings based on your merchandising strategy rather than algorithmic guesswork.
This approach works brilliantly for strategic cross-selling. Running a coffee shop? Manually pair each coffee variety with the appropriate brewing equipment. Selling skincare? Connect serums with their complementary moisturizers based on skin type.
The downside is obvious: it’s time-intensive. For stores with extensive catalogs, manually configuring complementary products for every item becomes a significant operational burden. One clothing merchant with thousands of SKUs would spend weeks just setting up initial pairings, let alone maintaining them as inventory shifts.
Why Product Recommendations Matter for Your Store’s Growth
Here’s the simple version: recommendations directly impact two critical metrics—average order value and conversion rate. They work because they reduce decision friction and introduce options customers might not have discovered through browsing alone.
When someone lands on your store hunting for a specific product, they’re focused on that single item. Recommendations expand their consideration set, showing complementary pieces that genuinely enhance their purchase or offer alternatives they hadn’t considered.
For merchants operating with strategic intent, recommendations also provide merchandising control. You decide which products get visibility, which slow-moving inventory needs a boost, and how to guide customers through your catalog logically.
The Revenue Impact
Smart recommendations turn single-item purchases into bundled sales. A customer buying a yoga mat sees a matching carrying strap and foam roller. Someone purchasing a winter coat gets shown scarves and gloves. Each additional item represents incremental revenue that wouldn’t have materialized without the suggestion.
Beyond immediate sales, recommendations improve customer satisfaction by helping shoppers discover products that genuinely enhance their purchase. A customer who buys a camera and lens cleaning kit because of a recommendation is better equipped and more likely to return for future purchases than someone who bought just the camera and later realized they needed cleaning supplies.
Shopify’s analytics track “Product recommendation conversions over time” in behavior reports, letting you measure actual performance rather than guessing at impact. This data-driven visibility helps refine your approach based on what’s actually working versus what you assume should work.
Choosing Between Native Features and Third-Party Apps
The decision isn’t really “which is better” but rather “which fits your store’s current stage and needs.” Native features make sense for certain scenarios, while apps solve problems the built-in system wasn’t designed to address.
When Native Recommendations Suffice
Start with Shopify’s built-in features if you’re running:
- Established stores with transaction history: The algorithm needs data to learn patterns, so stores with months of sales data see better results than brand-new shops.
- Manageable catalogs: If you’ve got fifty to a few hundred products, manually curating complementary items remains feasible without drowning in busywork.
- Straightforward merchandising needs: Stores selling clearly related products (like a tea shop pairing flavors with brewing accessories) work well with basic recommendation logic.
- Budget-conscious operations: Native features cost nothing beyond your Shopify subscription, making them ideal when cash flow is tight or you’re validating product-market fit.
The Shopify Search & Discovery app extends native functionality, letting you customize how recommendations appear and providing some additional control without third-party costs.
When to Consider Third-Party Apps
Apps fill gaps where native features fall short, particularly for stores with complex needs or aggressive growth targets. Consider specialized solutions when you need:
- Advanced personalization: Apps leverage sophisticated AI models that consider browsing behavior, demographic data, and real-time interactions beyond simple purchase correlations.
- Large or complex catalogs: Stores with thousands of SKUs need smarter segmentation than manual curation allows, especially in categories like apparel where variations explode quickly.
- A/B testing capabilities: Apps let you test different recommendation strategies against each other, optimizing based on actual performance data rather than intuition.
- Multi-channel integration: Connecting recommendations with email flows, SMS campaigns, or advertising platforms requires app-level functionality beyond native features.
Glood.AI positions itself around AI-driven personalization, focusing on increasing conversions through intelligent product matching. SF Product Recommendations (StoreFrog) emphasizes user-friendly interfaces for merchants who want power without complexity.
Learn more in AI-Powered Ecommerce: How Smart Automation Improves Conversion Rates.
Implementing Shopify Product Recommendations Strategically
Installation is the easy part. Making recommendations actually work requires strategic thinking about placement, product selection, and ongoing optimization. Too many merchants flip on recommendations and assume the job’s done, then wonder why they’re not seeing results.
Strategic Placement Matters
Product pages remain the most common recommendation location, but they’re not the only valuable touchpoint. Consider recommendations on:
- Cart pages: Catch customers right before checkout with last-minute additions that complement what’s already in their cart.
- Collection pages: Help browsers discover related categories or curated product sets based on what they’re viewing.
- Thank you pages: Capitalize on post-purchase satisfaction by suggesting items for their next order.
- Homepage sections: Feature trending combinations or seasonally relevant pairings for new visitors.
Theme compatibility determines how easily you can implement recommendations in various locations. Modern themes like Dawn include built-in recommendation sections, while older themes might require code modifications or app-provided widgets.
Mixing Automation with Manual Curation
The most effective approach combines algorithmic suggestions with human merchandising expertise. Let automation handle the baseline recommendations, then layer in manual curation for high-value products, seasonal promotions, or strategic inventory management.
For example, allow automated recommendations to suggest accessories for standard products, but manually curate pairings for your hero products or items you’re specifically trying to move. This hybrid strategy provides scale without sacrificing strategic control over key products.
Regular review prevents stale recommendations. Products sell out, seasons change, and inventory shifts. Schedule monthly audits of your manually curated complementary products to ensure suggestions remain relevant and in-stock items get priority.
Leveraging Analytics for Continuous Improvement
Shopify’s behavior reports show which recommendations drive conversions and which fall flat. This isn’t set-it-and-forget-it territory—successful merchants treat recommendations as an ongoing optimization project.
Track metrics like:
- Conversion rate on recommended products versus non-recommended items
- Average order value for orders including recommended products
- Click-through rates on recommendation sections
- Which product pairings generate the most additional revenue
Use this data to identify high-performing recommendation types and double down on what works. If you notice certain product categories convert exceptionally well when paired together, expand that strategy across similar items.
Check out Ecommerce Conversational AI: Turning Chatbots into Sales Assistants for complementary strategies that boost engagement.
Common Myths About Product Recommendations
Let’s clear up some misconceptions that trip up merchants new to recommendations. These myths lead to unrealistic expectations or missed opportunities because people don’t understand how the systems actually function.
Myth: Recommendations Work Immediately for New Stores
Reality check: Shopify’s native algorithm needs transaction data to identify patterns. Launch your store today, and you won’t see meaningful automated recommendations tomorrow. The system requires enough purchase history to detect which products correlate with each other.
New stores should either manually configure complementary products from day one or use third-party apps with pre-trained AI models that don’t depend solely on your store’s historical data. Expecting instant results from native automation sets you up for disappointment.
Myth: More Recommendations Always Equal More Sales
Flooding every page with twelve different recommendation widgets doesn’t automatically boost revenue. It usually just overwhelms customers and dilutes attention from your primary conversion goals.
Strategic restraint often outperforms recommendation overload. Place suggestions thoughtfully where they enhance rather than distract from the customer journey. Quality and relevance matter more than quantity—three highly relevant suggestions beat ten random ones every time.
Myth: Recommendations Only Work on Product Pages
While product pages remain the most common location, limiting recommendations to that single touchpoint ignores valuable opportunities throughout the customer journey. Cart pages can surface last-minute additions. Collection pages can suggest complementary categories. Email flows can remind customers of items they viewed alongside products they purchased.
Multi-channel recommendation strategies, especially those integrating with email platforms like Klaviyo, extend the value beyond your storefront. A customer who didn’t bite on a recommendation during their site visit might convert when they see that same suggestion in a personalized email three days later.
Myth: Set It and Forget It
The “install and ignore” approach leaves significant revenue on the table. Inventory changes, products get discontinued, seasons shift, and customer preferences evolve. Recommendations that worked brilliantly six months ago might be suggesting out-of-stock items or pairings that no longer make sense.
Treating recommendations as an active merchandising tool rather than passive automation means regular review, testing, and refinement based on performance data and inventory reality. Active management separates merchants who see modest results from those who extract serious revenue from recommendations.
Real-World Scenarios: When Different Approaches Shine
Theory matters, but let’s ground this in practical scenarios that mirror what actual merchants face when implementing shopify product recommendations.
Scenario: The New Boutique
A clothing boutique launches with a carefully curated collection of about eighty pieces. They’ve got no transaction history, so native automated recommendations won’t kick in for weeks or months.
Best approach: Manually configure complementary products for hero items and best sellers. Pair each dress with appropriate accessories, shoes with suitable apparel, and jewelry with outfits that showcase them well. This creates immediate value while the store builds the transaction history needed for automated recommendations to become effective.
As sales data accumulates, gradually transition toward hybrid automation, letting the algorithm handle standard items while maintaining manual curation for featured products and new arrivals.
Scenario: The Growing Catalog
An established store selling outdoor gear has grown from two hundred to over two thousand products. Manually managing complementary products for everything has become impossible, and generic “related products” feel increasingly random given the catalog diversity.
Best approach: Implement a specialized app with robust AI capabilities for handling large catalogs. Use the app’s automation for the bulk of the catalog while maintaining manual curation for high-value items like premium tents, technical jackets, and popular hiking boots.
Leverage category-specific recommendation logic so customers viewing backpacks see relevant hydration systems and sleeping bags rather than unrelated products like kayaking gear. Scale requires smarter segmentation than basic algorithms provide.
Scenario: The Multi-Channel Merchant
A beauty products store actively markets through email, SMS, and social advertising. They want recommendation consistency across channels—what customers see on-site should align with what appears in abandoned cart emails and post-purchase flows.
Best approach: Choose an app that integrates with marketing automation platforms. Connect recommendations to Klaviyo flows so the same intelligent suggestions appear in browse abandonment emails, post-purchase cross-sells, and win-back campaigns.
This creates a cohesive experience where customers encounter strategically aligned product suggestions regardless of touchpoint. The recommendation engine becomes the central intelligence powering suggestions across the entire customer journey.
For more on automating customer interactions, see How to Use Chatbot for Ecommerce Sales and Conversions.
Technical Considerations and Common Challenges
Implementation isn’t always plug-and-play smooth. Merchants frequently encounter technical hurdles that require troubleshooting or workarounds. Being aware of common issues helps you plan accordingly rather than getting blindsided mid-implementation.
Theme Compatibility Issues
Not all themes support recommendation sections equally. Older or heavily customized themes might lack the sections or blocks needed to display recommendations without code modifications. Before committing to a recommendation strategy, verify your theme supports the placements you’re planning.
If your theme doesn’t natively support recommendations in your desired locations, you’ve got three options: add code yourself (or hire a developer), switch to a theme with better support, or use an app that injects recommendations via widgets regardless of theme architecture.
Excluding Products from Recommendations
Sometimes you don’t want certain products appearing as recommendations—clearance items you’re phasing out, products with fulfillment issues, or items that don’t pair well with anything. Shopify’s native system offers no built-in exclusion functionality.
Workarounds include using product tags combined with custom code to filter out tagged items, or switching to apps that provide exclusion lists as a standard feature. This limitation catches merchants off guard more often than it should.
Performance and Page Speed
Recommendations add computational work—algorithms run, data gets fetched, and additional content loads. Poorly optimized implementation can slow page load times, which ironically hurts conversions more than recommendations help.
Choose lightweight solutions and test page speed before and after implementation. If you notice significant slowdown, optimize by limiting the number of recommendations displayed, lazy-loading recommendation sections, or switching to a more performance-conscious app.
What’s Next? Evolving Your Recommendation Strategy
Product recommendations aren’t a “set once” tactic. As your store grows, your approach should mature alongside it. Think of this as a progression rather than a destination.
Start simple with Shopify’s native features if you’re new or working with a manageable catalog. Get comfortable with how recommendations impact customer behavior and monitor which products benefit most from strategic pairing.
Graduate to hybrid approaches as your catalog expands, combining automated suggestions with manual curation for high-value products. This gives you scale without sacrificing control over your most important merchandising decisions.
Eventually, consider sophisticated third-party solutions when you’re ready to leverage advanced personalization, multi-channel integration, or complex segmentation. These tools make sense once you’ve validated that recommendations drive meaningful revenue and you’re ready to optimize aggressively.
The broader context? Recommendations represent just one piece of ecommerce personalization. As customer expectations evolve toward more tailored shopping experiences, combining smart product suggestions with other automation—like conversational AI, dynamic content, and behavioral triggers—creates competitive advantages that generic stores can’t match.
For deeper insights on leveraging AI across your store, explore how recommendation engines are transforming retail.
Frequently Asked Questions
What are Shopify product recommendations?
Shopify product recommendations are automated or manually curated product suggestions displayed to customers based on algorithms analyzing order history, product relationships, and browsing behavior. They appear as sections like “You might also like” or “Related products” throughout your store.
Do I need an app for product recommendations on Shopify?
Not necessarily—Shopify includes native recommendation features that work well for stores with sufficient transaction history and manageable catalogs. Apps become valuable when you need advanced personalization, better control over recommendation logic, or integration with marketing platforms.
How long before Shopify’s automated recommendations start working?
Automated recommendations require enough order data for the algorithm to identify patterns between products, typically several weeks to months depending on your sales volume. New stores should use manual complementary products or AI apps until sufficient data accumulates.
Can I control which products appear in recommendations?
Partially—you can manually set complementary products for specific items, but Shopify’s native related products generate automatically with no built-in exclusion feature. Controlling or excluding specific products from automated suggestions requires theme customization or third-party apps.
Where should I display product recommendations in my store?
Product pages are the most common and effective location, but strategic placement on cart pages, collection pages, and thank you pages can drive additional conversions. Multi-channel approaches extending recommendations into email flows and marketing automation perform even better.
