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Why Fashion E-commerce Search Fails (And How to Fix It)
January 17, 2026
30% of fashion ecommerce searches fail on Shopify, even with search and discovery tools and synonym dictionaries—costing stores €108K+ yearly. AI semantic search for e-commerce solves this by understanding products visually, not just keywords
Substanz has found that 30% of all site searches fail for fashion stores, despite these stores having relevant products for the customer to purchase. This is a huge problem for retailers as search users have a much higher intent to purchase. By ignoring search failures, retailers are leaving a significant amount of money on the table.
The Real Cost:
A store with 10,000 searches per month and a 30% failure rate loses 3,000 potential conversions. If just 6% of searches convert, that's 180 lost sales monthly. At a $50 AOV, that's $9,000 per month or $108,000 per year—before accounting for the additional sales a better search solution would generate.
The Problem Isn't Just You - Industry-Wide Search Failure
If you are facing search challenges, know that you are not alone. According to Baymard Institute's 2024 research, 41% of ecommerce sites fail to support common search query types. While it may seem like search is a lower priority, after all only 15% of visitors use search, those searchers generate 45% of all revenue. If customers cannot find what they are looking for, they will assume you do not stock items which you in fact do. Research shows that 72% of shoppers will leave if search does not meet their expectations.
The Five Reasons Fashion E-commerce Search Fails
Stores and Customers Speak Different Languages
This is a massive problem across different English-speaking markets, but it's amplified for EU-based stores. These stores might be primarily in English, but their customers speak French, German, Swedish, or Polish.
A shoe store may stock many products they have called "sneakers", but their customers could be looking for trainers, or runners, or Turnschuhe, or kicks. Without an extensive (and possibly never-ending) synonym dictionary, a store will struggle to capture all the different ways a user could search.
For many stores, non-English queries on an English-based site will fail 100% of the time. This is a big problem as many stores put time and effort into localizing their websites to ensure customers will convert (60% of customers won't purchase from English-only sites), but then still fail on search as all their product data is still English-based and so relevant results are not surfaced. This is an extra issue for merchandising teams as even if they maintain synonym dictionaries, they may not understand the nuances of local dialect for search purposes.
Synonym Dictionaries Are Expensive, Time-Consuming Band-Aids
Stores have tried to overcome the limitations of keyword search by maintaining synonym dictionaries. This is an expensive band-aid on a systemic problem. Across a 1,000-product catalog, a store may need to add 5+ synonyms (not even taking into account different languages), and if the store expands into new territory it will just add more words. When new Pantone colors are introduced, they will need to adjust. When new trends and slang become popular, they will further need to adjust, even if they understand that this is a way people are now searching. This just does not scale for stores, and even with all this time and effort, search still fails frequently.
All of the above also assumes that customers are spelling their searches correctly, but frequently, especially on mobile, customers misspell what they are looking for, leading to further missed searches.
Fashion Has Limitless Ways to Describe Products
Any given garment has an almost limitless way it could be described. Ask 5 people to describe the same dress and they will each give you a different take. Is it a LBD, or a black cocktail dress, or a backless mini dress, or schwarzes Kleid, or a robe noire, or a vestido negro, or an ebony halterneck knee-length frock? Stores have no way to anticipate all possible combinations, even if they take into account historical searches.
Limited or Reused Product Descriptions Create Mismatches
Merchandisers are pressed for time. With 5 different color options for the same item, the description might be copied and pasted across the same item type to save time. Or to drive users to the other colorways, they will reference the other colors in the description ("This sweater is also available in red"). What had a good intent inadvertently makes their search irrelevant. They may include the most high-level attributes, like color and fabric, but may miss critical descriptors a customer is looking for—e.g., bias cut skirt—leading once again to wrong results, missing results, or zero results.
Traditional Keyword Search Doesn't Understand Intent
Traditional keyword search takes users' searches at face value, not taking into account nor understanding their intent. When new trends pop up like "Y2K aesthetic" or "quiet luxury," traditional search has no idea what to do with this. Unless items have been tagged as such, they will return zero results, instead of taking into account what this might mean or what vibe that is. Similarly, "wedding guest dress" will often return bridal dresses as the guest part gets lost. These high-intent searches are goldmines for stores—they reveal both the customer's true intent and provide valuable data about what their customers actually care about.
The Solution:
AI Semantic Search Powered by Visual Understanding
How It Works
Your customers do not have to change any of their behavior. You don't have to change any of your product data. Substanz is an AI e-commerce search app for Shopify stores that works out of the box.
By training specifically on fashion data and analyzing your product images, we understand your products the same way you and your customers do—visually. When a customer types a search, we match their intent to what products actually look like, not just the text in your descriptions. This overcomes the vocabulary gap without requiring photo uploads or complex integrations. We combine search with filters to ensure even large catalogs can be searched effectively.
Why Image-Backed Semantic Search Solves All Five Problems
Solves the Language Problem
By relying heavily on product image data and AI, search can cross the language divide and customers can search in their native language without you needing to internationalize your product data.
Solves the Never-Ending Synonym Dictionary Problem
By leveraging AI, search can stay current with vocabulary trends and doesn't need constant updates to synonym dictionaries to keep current with both trends and newly uploaded items.
Solves the Infinite Variation Problem
Our implementation handles any variation or combination of words for search, enabling you to focus on your core business without worrying about unusual search patterns.
Solves the Bad Copy Problem
By considering the product images primarily, bad copy won't have such an outsized impact on search results.
Solves the Intent Problem
By understanding the intent behind words, we can understand what a customer might mean by "office casual" even if it's not a phrase you use to describe any of your products.
Key Takeaways
Why does Shopify search fail to find products?
Shopify's default search fails because it relies on exact keyword matching. When customers search for "trainers" but you've labeled products "sneakers," or when they search in German on your English site, traditional keyword search returns zero results even though you have the exact products they want. Research shows 30% of fashion ecommerce searches fail this way. The system can't understand synonyms, multiple languages, or search intent—it only matches the exact words in your product descriptions.
What is semantic search in e-commerce?
Semantic search in e-commerce uses AI and natural language processing to understand the meaning and intent behind customer searches, not just match keywords. Instead of only finding products with exact word matches, it understands that "trainers," "sneakers," and "running shoes" all refer to similar products. This delivers relevant results even when customers use different terminology, misspell words, or search in different languages.
Why do customers get no results when searching my store?
Customers get no results because traditional search can't handle:
Synonyms and regional variations (sneakers vs trainers)
Non-English queries on English sites
Misspellings, especially on mobile
Descriptive searches like "office casual" or "Y2K aesthetic"
Poor or missing product descriptions.
Industry research shows 30% of fashion e-commerce searches fail this way, even when relevant products exist in stock.
How much revenue do stores lose from failed searches?
A store with 10,000 monthly searches and a 30% failure rate loses approximately €108,000 annually (assuming 6% conversion rate and €50 average order value). This calculation assumes conservative conversion rates—actual losses may be higher when accounting for customers who leave after seeing irrelevant results.
Do I need to translate my products for multilingual search?
Not with AI semantic search. AI-powered search understands products through images, so customers can search in German, French, Spanish, or any language without requiring product description translation. The AI matches search intent to product visuals, eliminating the need for expensive translation and maintenance of multilingual product catalogs.
Why don't synonym dictionaries fix search problems?
Synonym dictionaries don't scale. A 1,000-product catalog needs 5+ synonyms per product across multiple languages—that's 5,000+ manual mappings. When new slang emerges, new colors launch (Pantone names), or you expand to new regions, you need constant updates. Even with this effort, synonym dictionaries can't handle misspellings, understand intent, or cover all the ways customers describe products.
What's Search Costing Your Store?
Based on our analysis of fashion e-commerce stores, 30% of searches fail even when relevant products exist. For a store with 10,000 monthly searches, that's €108K in lost revenue annually.
See how your store performs:
Book a free 20-minute diagnostic where we'll:
Test live searches on your store to identify gaps
Show you how customers actually search vs. what you think they search
Calculate your estimated revenue loss
Demo AI semantic search on your actual products
No analytics required. No commitment.