Discover 4 proven fashion recommendation strategies that boost conversions. Learn how About You, J.Crew, W Concept, and Cider drive sales.
by YesPlz.AIDecember 2025
It's surprising how many fashion eCommerce sites overlook PDP (product detail page) recommendations, even though our data from hundreds of user interviews proves they're a key conversion driver.
Shoppers land on a PDP already interested in that product—curating complementary or style-matched items next to it naturally turns their curiosity into cart additions.
Generic "you might like" suggestions, on the other hand, fall flat; thoughtful pairings create that seamless "I need this too" moment. Brands ignoring this miss out on effortless upsells.
So, here are our top 4 picks for PDP recommendations done right, from outfit-inspired complements to personalized style boosters. We've analyzed what works and why, so you can borrow ideas to lift your own conversions. Hope this sparks some fresh inspiration for your fashion site.
Table of Contents:
About You stands out because it doesn’t treat discovery as a single step. Instead, it views every PDP as an opportunity to guide shoppers through a thoughtful, personalized journey. Their approach layers multiple fashion recommendation types to meet shoppers where they are. It doesn't matter if shoppers are exploring alternatives, styling ideas, or familiar brands; there is always a relevant path forward.
When you land on an About You PDP, you’re not just viewing a single item. You’re stepping into a carefully curated discovery ecosystem that guides you through your own personalized shopping journey.
Take their Adidas Performance Jersey PDP as an example. About You doesn’t rely on one recommendation type. Instead, they’ve built a multi-layered eCommerce discovery strategy that adapts to different shopper mindsets and intents.
Their approach goes far beyond basic product matching. This is a great example of personalized recommendations that other fashion retailers can learn from.
The Similar Products module serves shoppers who like the style but want alternatives. Maybe their size isn’t available. Or they want to compare prices across similar products. About You match items based on category, visual similarity, and key attributes.
Outfit Inspiration (Wear it with)Outfit Inspiration adds lifestyle context. It shows how the items work in real outfits and suggests styling ideas. This isn’t just about selling a jersey. It’s about selling the complete, aspirational look. In the image above, you can see that About You suggests three additional items to wear with the jersey, including:
Bralette sports bra in black
Wide leg workout pants 'DFB' in red, burgundy
Sneakers 'SL 72' in brown, caramel
More from this Brand capitalizes on brand loyalty. If someone loves Adidas, it makes sense to show them more products from this brand. The section builds brand affinity and increases the chances of multiple-item purchases from the same label.
About You understands that shoppers arrive with different needs. Some shoppers want close alternatives. Others need styling inspiration. And many don’t know what they’re looking for until they see it. With multiple eCommerce discovery pathways, About You reduces decision fatigue, increases engagement, and keeps shoppers exploring longer.
For online fashion retailers, adopting a similar strategy can lead to:
More page views per session
Higher average order values
Significantly lower bounce rates
Don't limit to one recommendation type. Build a comprehensive discovery ecosystem that serves multiple shopping intents at once.
J.Crew's approach highlights a growing shift in fashion eCommerce: Shoppers don’t just want options; they also need guidance. Today, shoppers face an overwhelming number of choices online. This is where the real value of smart recommendation engines emerges:
Help shoppers cut through the noise
Discover pieces that fit their style
Feel confident in their decisions
J.Crew has mastered the art of making algorithmic recommendations feel human. Their PDPs don’t just show shoppers similar items. They educate, inspire, and guide shoppers the way a personal stylist would. This is a great fashion eCommerce discovery that blends AI technology with fashion taste.
Visit their Chiara Topcoat in Double Face PDP, and you’ll immediately notice the elevated design. Every recommendation module is beautifully crafted, visually compelling, and purposefully placed.
This is where J.Crew truly shines. The “How to Wear it” section doesn’t just list matching products. It creates complete, styled looks that show shoppers exactly how to incorporate the item into their wardrobe.
The design is editorial-quality, featuring lifestyle imagery and curated outfits that are aspirational yet attainable. It's the digital equivalent of having a stylist pull together looks for you in a fitting room.
This approach achieves two goals:
Increase basket size through cross-selling
Reduce the cognitive load of styling decisions
The “Some Similar Styles" section also helps shoppers explore alternatives. Clear, clickable filters appear at the top of the section, guiding discovery. Instead of scrolling endlessly, shoppers can quickly refine what “similar” actually means to them. For example:
Product type, like topcoats
Materials, including wool and wool blends
Color families, such as brown tones
Quality signals, like top-rated items
This approach keeps discovery focused and efficient. Shoppers stay in control, find relevant options faster, and move forward with confidence.
Collaborative Filtering: "Customers Also Love"Alongside editorial curation, J.Crew uses shared shopper behavior to guide discovery. The “Customers Also Love” module is powered by what shoppers with similar tastes have viewed, liked, or purchased.
The logic is intuitive. Other shoppers who bought this topcoat also explored these styles. For this reason, they are likely to be highly relevant to shoppers currently viewing it. Badges like “Top Rated” add social proof and reduce hesitation. These recommendations feel natural and trustworthy. The data works silently in the background, while the shopping experience remains human.
Why This WorksJ.Crew understands that fashion is emotional, aspirational, and deeply personal. By investing in beautiful design and thoughtful curation, they elevate the eCommerce discovery experience beyond transactional browsing.
Their personalized fashion recommendations don’t feel like pushy sales tactics. They feel like helpful styling advice. The visual investment also strengthens brand equity, positioning J.Crew as a taste-making, premium fashion retailer.
Design matters. Invest in recommendation modules that are visually compelling and editorially curated to build trust, inspire shoppers, and elevate your brand.
W Concept data tells a compelling story: Personalized recommendations deliver 3x higher add-to-cart rates than generic ones. But the impact goes even deeper. Their strategic implementation of “More by the Brand” achieved a 1.28x higher add-to-cart rate compared to the industry benchmark. An average 10% of sales attribution directly comes from implementing these well-designed recommendations. This is a significant revenue boost that requires no additional traffic acquisition costs.
On their PDPs, W Concept deploys multiple high-quality recommendation types. Each serves a distinct purpose.
More by the Brand fosters brand loyalty and encourages deeper exploration of their favorite brand catalog.
You Might Also Like uses collaborative filtering and visual similarity to showcase items that match shoppers’ demonstrated preferences.
Style With removes the guesswork by suggesting complementary pieces that work together cohesively.
Frequently Bought Together leverages purchase data to show proven product combinations.
W Concept understands that personalization isn’t about showing more products. It’s about showing the right products at the right time. Instead of wading through thousands of products, shoppers are immediately presented with items that match their taste, size, and style preferences.
Quality over quantity. The real impact comes from having good, fashion-specific recommendations that understand styling, visual similarity, and shopper intent—not just showing more products.
Cider has built a shopping experience that feels less like browsing a catalog and more like scrolling through a highly curated social feed. Their approach is designed to be endless, engaging, and fun. This is a fresh personalized fashion recommendation example that resonates with younger audiences.
Visit Cider’s website, and you’ll notice something immediately: Their recommendations never stop. Scroll through their PDPs, and you’re met with a continuously flowing stream of “You May Also Like” suggestions that feel handpicked for your taste.
This isn’t accidental. Cider understands its Gen-Z core audience, a demographic that grew up with infinite scroll on TikTok and Instagram. They’ve translated that addictive browsing behavior into an eCommerce experience.
The “You May Also Like” module doesn’t just display a static grid of 10-15 items. It keeps going, creating a browsing loop that encourages exploration and extended session times. The more shoppers scroll, the more they discover, and the more likely they are to find something irresistible.
The uniqueness of Cider lies in its emotional experience. Their curation feels playful, unexpected, and aligned with youth culture. Products are organized around trend-focused themes ("Cottage Core," "Y2K Revival," "Coastal Grandmother") that resonate with their audience.
This discovery experience is both functional and entertaining. Shoppers come for one item and stay to explore because the experience itself is enjoyable.
Cider has recognized that for their audience, shopping is an entertainment activity. By creating an endless, engaging eCommerce discovery experience, they've transformed browsing from a means to an end into an enjoyable activity in its own right.
This strategic placement of recommendations throughout the journey ensures maximum exposure and opportunity for conversion.
Match your discovery experience to your audience's behavior patterns. For younger demographics, endless scroll and trend-focused curation drive engagement.
While each of these fashion retailers has a distinct approach, they all share one thing in common: None of them relies on a single algorithm. They deploy various recommendation strategies to serve different shopper intents simultaneously.
Creating recommendation experiences like About You, J.Crew, W Concept, and Cider requires sophisticated AI technology that is purpose-built for fashion. Effective fashion recommendations require several key AI capabilities, including:
Visual similarity matching
Attribute-based recommendations
Collaborative filtering
Personalization engines
Style understanding
Book a demo with us to see how YesPlz AI can help you create one-of-a-kind recommendations that keep your shoppers engaged.
Written by YesPlz.AI
We build the next gen visual search & recommendation for online fashion retailers
Stop losing sales to poor product filtering. Discover how AI simplifies creating Shopify filters, saving you 25-50 hours per 100 products.
by YesPlz.AI
Automate Shopify product pages and cut 50–100 hours of manual work. AI generates product titles, descriptions, and metadata instantly from product images.
by YesPlz.AI
