15 Product Recommendation Examples That Convert (2026)
Product recommendations account for up to 31% of ecommerce revenue, according to Barilliance research.
That’s nearly a third of total sales coming from “you might also like” and “frequently bought together” sections.
I’ve spent years working with ecommerce store owners, and those who consistently get product recommendations right outperform those who don’t. Not by a little. By a lot.
This guide breaks down 15 product recommendation examples from real brands that are using personalized suggestions to increase average order value, reduce cart abandonment, and keep customers coming back.
You’ll see exactly what each brand does, why it works, and how to apply the same approach to your store.
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What Are Product Recommendations?
A product recommendation is a suggestion shown to a shopper based on their browsing behavior, purchase history, or what similar customers have bought.
You see them as “you may also like” rows on product pages, “frequently bought together” bundles, add-on offers in the cart, quiz results, and follow-up emails after an order.
These suggestions show up on homepages, product detail pages, cart pages, checkout flows, and even in post-purchase emails.
Think of it like a smart salesperson who watches what a customer picks up, then walks over with something that pairs perfectly.
Except this salesperson never sleeps, never forgets, and gets better with every interaction.
For ecommerce stores, product recommendations are one of the fastest ways to lift average order value without spending more on traffic.
How Product Recommendation Engines Work
Behind every “you might also like” section is an algorithm doing math.
Here’s a simplified breakdown of the three main approaches.
Collaborative filtering
This method looks at what groups of customers do.
If 500 people who bought Product A also bought Product B, the system recommends Product B to the next person who adds Product A to their cart.
It’s pattern matching at scale. Netflix uses this heavily. So does Amazon.
The limitation? It needs a lot of purchase data to work well. New stores with low traffic won’t get accurate results from collaborative filtering alone.
Content-based filtering
Instead of looking at customer behavior, this approach analyzes the product itself.
It considers attributes like category, brand, price range, color, material, and features.
If someone views a black leather wallet, the system recommends other black leather goods.
Straightforward and reliable, even for new stores with limited purchase data.
Hybrid systems
Most modern ecommerce platforms combine both approaches.
Shopify, BigCommerce, and WooCommerce apps typically use hybrid models that factor in browsing behavior, purchase patterns, and product attributes simultaneously.
The result is more accurate, more relevant suggestions that improve as the system collects more data about your customers.
Also check: 15 Product Recommendation Software That Actually Work
15 Product Recommendation Examples From Real Brands
I’ve organized these by strategy type, not just brand name.
Each example shows a specific recommendation tactic you can steal for your own store.
1. Kylie Cosmetics: “Don’t Forget” cart cross-sell
Kylie Cosmetics adds a “Don’t Forget” section on their cart page when someone has a matte lipstick in their order. The recommendation? A lip oil that complements it.
The product ratings displayed alongside the suggestion add social proof right when the customer is about to check out.
Why it works: The recommendation is contextually relevant (lip oil pairs well with lipstick), the placement is strategic (the cart page, where commitment is high), and the star ratings reduce hesitation. Simple, effective, high-converting.
2. Wandering Bear: Subscription upsell on cart page
This coffee company doesn’t just suggest another product.
They promote “The Pack Membership” directly on the cart page, with subscription pricing, exclusive access, and a limited-time incentive.
Why it works: Turning one-time buyers into subscribers is the highest-ROI recommendation you can make. One customer lifetime value calculation will tell you why. The scarcity element (limited offer) creates urgency to act now rather than “maybe later.”
3. Tarte: Personalized email recommendations
Tarte Cosmetics sends product recommendation emails based on a customer’s recent browsing history.
The email reminds shoppers of a product they viewed and adds a “you may also like” section below.
Why it works: Email recommendations have a massive advantage: they reach customers who left without making a purchase. The personalization (based on actual browsing, not random picks) makes the suggestions feel relevant rather than spammy.
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4. Soi: “Complete the look” outfit builder
After adding a dress to their cart, customers see a “complete the look” section suggesting a matching belt and shoes.
The recommendations create a full outfit around the original item.
Why it works: Fashion shoppers often want the full look but don’t want to search for each piece individually. This approach increases average order value by 20-30% for brands that implement it well, according to multiple case studies. It turns a single-item purchase into a multi-item order.
5. Colourpop: Cart specials with limited availability
Colourpop shows “cart specials” offering discounted add-ons when items are already in the cart.
The products include a liquid liner and cheek palette at reduced prices, with a “limit 1 per order” restriction.
Why it works: The discount makes the add-on feel like a deal. The quantity limit creates scarcity and urgency. Together, they trigger impulse purchases that feel like smart shopping rather than overspending.
6. Target
Target is a major US retailer that sells clothing, home goods, electronics, beauty products, groceries, toys, furniture, and other everyday products through its stores and ecommerce site.
Target places multiple recommendation carousels below the main product. In this screenshot, “Discover more options” shows similar dresses that give the shopper alternatives to the item they are viewing.
The “Guests also viewed” section uses shopper behavior to surface other products that people browsing the same item also looked at.
Why it works: A shopper may like the style of a product but dislike its color, price, fit, or design. “Discover more options” keeps that shopper browsing by showing close alternatives instead of making them restart their search.
7. Chewy
Chewy is a US online retailer focused on pet products and services. It sells pet food, treats, toys, beds, supplies, pharmacy products, and other pet care items for dogs, cats, birds, fish, reptiles, and other pets.
Chewy uses two recommendation sections on the product page.
- The first, “Related to This Item,” recommends closely related products in the same category. In this screenshot, someone viewing a dog bed gets several other dog bed options with different styles, prices, ratings, and offers.
- The second, “Compare Similar Items,” goes a step further. It places competing products side by side so shoppers can quickly compare alternatives without starting a new search.
Why it works: This approach helps shoppers who like the product category but are not fully convinced by the exact item they opened. Instead of losing that shopper, Chewy immediately gives them other relevant choices.
8. Frank Body: Visual browsing with benefit icons
Frank Body uses product recommendation carousels with high-quality images and icons highlighting key benefits (vegan, cruelty-free, natural ingredients) for each suggested product.
Why it works: The benefit icons help shoppers quickly filter recommendations without clicking into each product page. Visual scanning is faster than reading descriptions, and faster decisions mean higher conversion rates.
Also see: I Analyzed 50 Product Listing Pages (11 Best in 2026)
9. Gymshark: “People also bought” collaborative filtering
Gymshark places a “People Also Bought” section on product pages, showing items frequently purchased alongside the product being viewed.
Why it works: This is collaborative filtering in action. Real purchase data from thousands of customers powers these suggestions, making them far more accurate than manually curated recommendations. Customers trust what other shoppers actually bought over what a brand suggests they should buy.
Also check: 11 Best Upselling and Cross-Selling Strategies
10. Warby Parker
Warby Parker is an eyewear company that sells prescription glasses, sunglasses, contact lenses, and related vision services. It also offers eye exams and online tools that help shoppers choose frames.
On the Millie sunglasses product page, Warby Parker shows a section called “Similar to Millie.” It recommends other frames that have a similar look, shape, and style to the product the shopper is already considering.
Why it works: Eyewear is highly style-based. A shopper may like the overall shape of one frame but prefer a different width, color, detail, or fit. Instead of sending the shopper back to a large product catalog, Warby Parker shows close alternatives right on the product page.
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11. ASOS
ASOS is an online fashion retailer that sells clothing, shoes, accessories, beauty products, and fashion items from its own labels and hundreds of partner brands.
On this product page, ASOS shows a “YOU MIGHT ALSO LIKE” section below the main dress. The recommendations feature other dresses with a similar overall style, color range, length, and occasion.
Why it works: Fashion shoppers often like the general idea of a product but may want another color, cut, price, pattern, or brand. ASOS keeps those shoppers moving through relevant alternatives instead of making them return to search results.
12. Sephora
Sephora is a major beauty retailer that sells makeup, skincare, fragrance, hair care, bath and body products, and beauty tools from a large range of brands.
The “Pick Up Where You Left Off” section shows a product the shopper recently viewed. This is a recently viewed recommendation, which helps someone return to a product without searching for it again.
Below that, “Chosen For You” shows products that Sephora describes as “Personalized based on your preferences.” This is a clear example of personalized product recommendations.
Why it works: Beauty shopping depends heavily on personal preferences such as product type, skin needs, shade, finish, brand, and past interests. A generic list of popular products may not match every shopper.
13. Amazon
Amazon is a large ecommerce marketplace that sells products across electronics, fashion, home, beauty, books, groceries, and many other categories. It also allows third-party sellers and brands to sell directly through its marketplace.
The main section uses Amazon’s “Frequently bought together” recommendation. It shows products that Amazon’s system has found are often purchased along with the item a shopper is viewing.
Why it works: “Frequently bought together” removes extra search work. Instead of making the shopper find related items separately, Amazon presents them together and provides one “Add all 3 to Cart” button.
14. Stitch Fix
Stitch Fix is an online personal styling service for women, men, and kids. It recommends clothing and accessories based on each customer’s style, size, fit, and budget. Customers can receive a curated “Fix” from a human stylist or shop personalized recommendations directly.
The screenshot promotes Stitch Fix’s Style Quiz, which collects information about the shopper’s style, size, fit preferences, and budget.
Why it works: Fashion recommendations can be difficult because two shoppers looking for jeans may have completely different needs. Stitch Fix asks for those preferences before recommending products. It can consider things such as: Size and fit, Personal style, Budget, Lifestyle, Previous feedback.
This reduces the number of irrelevant products shoppers need to browse.
15. Revolve
Revolve is an online fashion retailer focused mainly on premium clothing, shoes, accessories, and beauty products. It carries more than 1,600 emerging, established, and owned brands and mainly serves Millennial and Gen Z shoppers.
The first is “Recommended For You.” It presents a mix of dresses, jeans, and other fashion items selected for the shopper.
Below that, “Style It With” recommends accessories and other products that can be paired with a main clothing item to create a complete outfit.
Why it works: Fashion shoppers often need help deciding what goes with an item, not just which item to buy. “Recommended For You” gives shoppers more products to discover based on their shopping experience, while “Style It With” helps them picture a complete outfit.
Where to Place Product Recommendations on Your Store
The best product recommendations fail if they’re in the wrong spot. Here’s where to place them for maximum impact.
| Placement | What to show | Why it works there |
|---|---|---|
| Product page | Complements first, alternatives second | The shopper has told you the category they want |
| Cart page | One cheap add-on or a threshold nudge | Intent is highest, and the decision is already made |
| Checkout | One item, or nothing | Anything more risks the order you already have |
| Homepage | Best sellers for new visitors, recently viewed for returning ones | You know almost nothing about a first-time visitor |
| Post-purchase email | Refills, accessories, the next size up | The product is in their hands, and the brand is fresh |
| Search and 404 pages | Popular items in the closest category | A dead end is a guaranteed exit otherwise |
Product Recommendation Strategies That Actually Work
Pair recommendations with social proof
Product recommendations convert better when they include trust signals. Star ratings, review counts, “bestseller” badges, and social proof statistics like “bought 500+ times” all reduce purchase hesitation.
Tools like WiserNotify let you add real-time social proof notifications (“Someone in New York just bought this”) directly on product pages where recommendations appear.
The combination of personalized suggestions and live purchase activity creates both relevance and urgency.
Don’t overwhelm with choices
Showing 20 recommended products is worse than showing 4. Choice overload leads to decision paralysis.
I’ve seen this with dozens of stores.
The ones that curate 3-6 highly relevant recommendations outperform those that dump every related product onto the page. Quality over quantity, always.
Match recommendations to the customer journey
Someone browsing casually needs different recommendations than someone with items in their cart.
Early in the journey: show more options, broader categories, trending items.
Close to purchase: show complementary products, upgrades, and bundles that pair with what’s already selected.
A/B test your recommendation placements
Don’t assume the first placement works best. Test different positions, different numbers of products shown, different recommendation types (collaborative vs. content-based), and different visual layouts.
The data will tell you what converts. I’ve seen stores double their recommendation click-through rate just by moving the section from below the fold to above the “add to cart” button.
3 Product Recommendation Mistakes That Kill Conversions
1. Irrelevant suggestions
A customer browsing winter coats sees swimsuit recommendations.
It sounds absurd, but it happens more often than you’d think when recommendation engines aren’t configured properly.
Every suggestion must be contextually relevant to the product the customer is viewing or the items in their cart.
If your recommendations feel random, customers lose trust in your store’s ability to help them.
2. Showing too many products
I’ve audited stores with 30+ recommended products on a single page.
The result? Customers scroll past all of them.
Stick to 4-8 recommendations maximum.
Prioritize the most relevant ones, and display them in a clean grid or carousel format with high-quality images.
3. Not tracking performance
Implementing product recommendations without measuring results means you’re flying blind.
Track click-through rates on recommended products, conversion rates from those clicks, and how recommendations affect average order value.
Review the data monthly and optimize based on what’s working.
3 Tools to Power Your Product Recommendations
1. Recommendify (Shopify)
A Shopify app that uses collaborative filtering to analyze purchase data and generate personalized product suggestions.
It’s straightforward to set up and integrates directly with your store’s theme.
Best for: Shopify stores that want plug-and-play recommendations without heavy customization.
2. Nosto
Nosto is a full personalization platform covering product recommendations, personalized content, and email campaigns.
It uses AI and machine learning to continuously improve the accuracy of recommendations.
Best for: Mid-size to large ecommerce stores with enough traffic data to power machine learning models.
3. Barilliance
Barilliance offers product recommendations, triggered emails, and personalized search on a single platform.
Their recommendation engine is built for high-traffic stores and includes advanced behavioral targeting.
Best for: Enterprise ecommerce stores that need multi-channel personalization with deep analytics.
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Wrapping Up
Product recommendations aren’t optional for ecommerce stores that want to grow.
The data is clear: they drive up to 31% of revenue, increase average order value, and keep customers engaged longer.
The 15 examples above show that recommendations work across all placements (homepage, product pages, cart, email) and all strategies (cross-sell, upsell, personalized, social proof-driven).
Start with the highest-impact placement for your store. For most brands, that’s the product detail page and the cart page. Add relevant, curated suggestions.
Pair them with social proof. Then measure and optimize.
Small changes to your recommendation strategy compound over time. A 10% improvement in recommendation click-through rate across thousands of daily visitors adds up fast.
Frequently Asked Questions
What is a product recommendation with an example?
A product recommendation is when a store suggests something you might like based on what you’ve looked at or bought before.
Example: If you’re shopping online for a phone, the website might suggest a phone case or screen protector that goes with it.
What are product recommendations?
Product recommendations are suggestions made to help you find items you might want or need. They’re usually based on your preferences, past purchases, or what’s popular among other customers.
How would you recommend a product?
To recommend a product, you should understand what someone is looking for and suggest something that fits their needs or interests. For example, if someone wants a gift for a coffee lover, you might recommend a coffee maker or a unique blend of coffee beans.
What are recommended products?
Recommended products are items that a store or website suggests for you. These are often personalized based on what you’ve looked at, bought before, or what other customers are buying.
Krunal Vaghasiya is a marketing tech expert who boosts e-commerce conversion rates with automated social proof and FOMO strategies. He loves to keep posting insightful posts on online marketing software, marketing automations, and improving conversion rates.
Capterra