Demystifying Fashion Taxonomies: What Do Customers Really Want?

"Fashion is subjective, and product descriptions are open to interpretation." _Vogue Business_

Quick question: What would you call the fabric accessory in this photo?

Some people see a scarf. Others call it a bandana. Or a kerchief.

Depending on where you live and what you know about fashion, you'll use different words for the same thing. But this diversity in language creates an inconsistent fashion taxonomy. Your shoppers can't find what they're looking for. And you, as an online fashion retailer, can't keep up with evolving terms. 

The foundation of solving this problem is accurate product tagging. When your products are correctly tagged with the right and diverse attributes, your site search functionalities (search, filters, recommendations) will work better.

In this guide, we'll break down fashion taxonomy, shopper search behavior, and how AI product tagging solves these problems.

Table of Contents:

• The Fashion Taxonomy Problem

• The 4 Customer Search Personas You Need to Know

• How Computer Vision and AI Tagging Unlock Search Success

• The Solution: AI-Powered Product Tagging

The Fashion Taxonomy Problem

A shopper searches for ‘bandana’ on your site and gets zero results. Why does this situation happen? It’s probably because you tagged it as ‘scarf.’ That's the fashion taxonomy problem in action. 

Fashion taxonomy is how you name and categorize products—silhouettes, necklines, fabrics, styles. Read our 101 guide about what fashion taxonomy is here. 

The real issue? Fashion terminology changes faster than retailers can update their systems. New trends emerge weekly. Regional language differences are everywhere. One person's ‘tennis skirt’ is another's ‘pleated mini.’ By the time you update your product tags with the latest trend, shoppers have moved on.

For decades, traditional taxonomy has been the only way to categorize thousands of product attributes. But modern AI nowadays offers alternatives. AI product tagging can power better product discovery experiences.

The 4 Shopper Search Personas You Need to Know

Every shopper fits into a search persona. Some are combinations of multiple personas. In fashion, YesPlz AI found 4 key search intentions your site needs to support:

1, Exact Search

2, Product Category Search

3, Attribute Search

4, Occasion & Mood Search

Can your search system handle all four search intents?

Persona #1: "On a Mission" (Exact Search)

This shopper knows exactly what she wants. She hesitates for no time when typing in a search term. She knows the exact brand name, dress style, and season. And she's probably short on time.

Where Traditional Taxonomy Falls Short? Due to the specificity and speed of the search, the shopper may make a typo during the search. Unless your system recognizes typos and search variations, she gets zero results. As a customer on a mission, she’ll likely bounce to a different website to continue the search.

Persona #2: "Willing to Explore" (Product Category Search)

This shopper has a product category she’s interested in  (like ‘dress pants’). She is looking to explore the whole category. She wants to see relevant results (and perhaps product suggestions) that complete the search.

Where Traditional Fashion Taxonomy Falls Short? Your “willing to explore” shopper is likely to fall victim to terminology confusion. For instance, ‘dress pants’ has multiple interpretations and regional variations. What if she doesn't know you tag these items ‘trousers’? She might get zero results or a combination of dresses and pants.

Persona #3: "Fashionista Features" (Attribute Search)

This shopper cares about details. She wants a cropped vegan leather jacket, under $200. Her expectations are high. She's seen major fashion brands highlight features. So, she expects the same experience from you.

Where Traditional Fashion Taxonomy Falls Short? Fashion taxonomy requires constant feature updates and an understanding of relevant terminologies. As your fashionista shoppers add more filter options to their search, the chances increase that an attribute won't be tagged correctly or comprehensively. Your shoppers get frustrated and leave.

Persona #4: "The Themed-Searcher" (Occasion & Mood Search)

This shopper searches for ‘wedding guest dress’ or ‘cozy fall jacket.’ These are examples of thematic searches based on occasion, season, or vibe. They're how we, as humans, naturally think. We know a casual denim dress isn't wedding-appropriate. We've mapped those relationships in our minds. But most eCommerce search systems haven't.

Where Traditional Fashion Taxonomy Falls Short? Fashion taxonomy is, at its core, a system created by humans. Unless those humans map every possible relationship between occasions, seasons, moods, and clothing (which varies by demographics), it's impossible to cover everything. Therefore, it’s likely that the thematic searcher gets few results. Shoppers might assume that your fashion eCommerce site doesn’t have the appropriate products for their needs.

How Computer Vision and AI Tagging Unlock Search Success?

AI tagging is the foundation of successful search. When a shopper searches, she expects accurate results. Whether she gets them depends entirely on your product tagging.

Here's a common example. A product tagged ‘fit and flare skirt’ won't appear if someone searches ‘A-line skirt.’ The fashion language is mismatched between the searcher and the retailer. This happens all the time, even when you tag products in-house or rely on third-party taggers.

How do you prevent this issue on your site? AI and computer vision to automatically tag product images. This eliminates human tagging error, increases tagging accuracy, and improves the relevance of site search results. Modern AI fashion tagging systems can recognize product attributes in all types of images, even low-quality photos. 

Here's the 4-step AI tagging process:

• Computer vision analyzes product images

• AI identifies attributes (color, silhouette, pattern, fit, occasion, mood)

• Fashion experts validate accuracy

• Tags apply automatically to your entire catalog

The Solution: AI-Powered Product Tagging

Product tagging is the foundation for solving fashion taxonomy problems. When your tagging is accurate, everything works better—search, filters, recommendations. But manual taggingcan't keep up since language varies by region, trend, and personal vocabulary.

AI-powered product tagging solves this. It automatically identifies product attributes from images. It eliminates human error and inconsistency. Computer vision, combined with fashion expertise, tags thousands of products in the time it takes a human to tag one.

Modern fashion search combines this technology to deliver what shoppers expect:

• Visual search that understands style  

• Natural language that interprets how people talk  

• Automatic tagging that stays current  

• Smart filters that work how humans think

Interested in AI product tagging for your store? Schedule a demo with us to see it in action.