AI Fashion Tagging: How We Got 22% More Search Results & 11% More Clicks

Site search is one of the highest-converting channels in eCommerce. Only about 15% of visitors use it, yet those users generate nearly 45% of total eCommerce revenue. Also, many retailers already know their site search isn't great. Shoppers type a query, then irrelevant products appear. Or worse, nothing shows up at all. Either way, they leave. 

But, here's what many fashion retailers don’t realize: Poor tagging is the main reason site search fails. So, we decided to run a test with 13,374 search queries to measure what happens when you use appropriate tagging and when you skip tagging—same product catalog, but two different setups. One used AI fashion tagging. The other didn’t. 

According to our experiment, the AI-tagged setup returned 22% more products per search and generated 11% more clicks. At this point, you might wonder why AI tagging performs better. To answer that, we need to first look at what we measured. 

Table of Contents:

• What We Measured

• AI Fashion Tagging Isn’t a Nice-to-Have—It Drives 11% of Clicks

• Why Category Searches Break on Fashion Sites

• How AI Fashion Tagging Increases Product Discovery by 22%

• Why Niche Fashion Queries Depend Entirely on Tags

• Click-through Rate (CTR) Increases When Tagged Products Dominate Results

• Why AI Fashion Tagging Still Needs Fine-Tuning

• The Practical Takeaway: Where to Start With Fashion Tagging

• Get Started with AI Fashion Tagging Without Heavy Commitment

What We Measured

Surface-level metrics aren’t helpful. Counting how many tags were applied or how fast tagging was completed doesn’t tell you anything about revenue. You need metrics that directly link tagging to customer behavior and business outcomes. 

So, we want to go beyond vanity metrics. It’s not enough to know that tagging helps. We need to know how much it helps, where it helps most, and even when it actually hurts. So we tracked two critical metrics:

• Product Discovery: How many relevant products become searchable with tags?

• Click Engagement: Which products actually got clicked by shoppers?

These two metrics answer a simple but critical question: Does tagging help shoppers find and choose products they would otherwise miss? 

AI Fashion Tagging Isn’t a Nice-to-Have—It Drives 11% of Clicks

And the data told us a clear story. Here’s what we found:

12.5% unique pairs of search queries and clicked products only happened because of tagging. Without tags, these connections between shoppers and products would not happen at all. And that translates to 11% of total clicks powered purely by fashion tags.

Here’s a snapshot of how tagging contributed to clicks for common fashion queries:

For the search query ‘jacket,’ removing tagging would eliminate 64% of shopper clicks. That means more than half of shoppers searching for this keyword would fail to find a product worth clicking.

Why Category Searches Break on Fashion Sites

According to our statistics and from the table above, you can see that category names like ‘jacket,’ ‘coat,’ and ‘bag’ are massively popular search keywords. But why do many site search engines still return irrelevant products or no results at all for those queries? 

Our data points to two main reasons.

The Category Name is Missing from Product Data

Many product titles and descriptions focus on brand, fabric, or style details. But they never explicitly state the category. For example, a product might be clearly a jacket. Yet, the word ‘jacket’ never appears in the title or description. As a result, when shoppers search for ‘jacket,’ that product is excluded from results.

Shoppers Use Different Words Than Fashion Retailers

You use industry terminology instead of shopper language. Let’s say your product title is ‘women’s outerwear in black.’ However, your shoppers don’t use the keyword ‘outerwear’ to search. They use ‘coat.’ Even though the intent matches, your site search engine fails to connect the two. It causes your black outerwear not be displayed in shopper search results.

In both cases, the gap between how products are described and how shoppers search leads to missed results, missed clicks, and lost conversions. Manual tagging struggles to keep up with this mismatch. And skipping tagging entirely makes the problem worse.

Bridging the Gap with AI Fashion Tagging

So how do you close the gap between product data and shopper language? By applying comprehensive, consistent fashion tags across the entire catalog. AI fashion tagging allows a single product to carry multiple relevant categories and attribute terms. With this automated approach, your products can be discovered through different keywords—whether a shopper searches for ‘jacket,’ ‘coat,’ or ‘outerwear.’ This ensures the product appears regardless of the exact term the shopper uses.

In our experiment, the AI-tagged catalog showed a clear net gain in performance. Even after accounting for minor mismatches, tagging delivered a strong positive impact:

The result you can expect:

• More products discovered

• More clicks generated

• Fewer shoppers are leaving empty-handed

How AI Fashion Tagging Increases Product Discovery by 22%

We also looked at how AI fashion tagging affects product discovery.  So, we analyzed 1,000 keywords and counted how many products each search query could surface:

• Without tagging: 2,058 products per keyword on average

• With tagging: 2,520 products per keyword on average

That’s 462 additional products per keyword. A 22.5% increase in product discovery. Some keywords saw extreme jumps:

Look at ‘collar.’ We went from 8,343 products to nearly 34,000 overnight. More products in search results means more options for your shoppers. More options mean a higher chance they find exactly what they want. And that leads directly to more sales.

Why Niche Fashion Queries Depend Entirely on Tags

Not all shoppers search by category names. Some know exactly what they want, so they use long-tail, specific queries like ‘wedding dress,’ ‘wool pleated skirt,’ ‘ivory long coat,’ or ‘leather woven bag.’ These searches are deliberate and high-intent. For these queries, AI fashion tagging becomes critical.  

In our dataset, tags were the only reason these products were discovered. The table below shows keywords where 100% clicks came from tagged products:

Every single click in the table came from tags. Remove tagging, and these searches return zero meaningful results.

The Wedding Dress Example: When Product Names Don't Match Search Intent

Take ‘wedding dress’ as an example. When shoppers typed in the search bar ‘wedding dress,’ not a single matching product actually contains those exact words in the title or description. Some products were labeled ‘bridal.’ Others had titles like ‘long dress’ or ‘halter-neck dress’ with no wedding reference at all. Yet, they still appeared in search results and generated 30 total clicks.

Why? Because AI fashion tagging identified the occasion and tagged them as appropriate for weddings. The system recognized visual cues—white or ivory fabric, formal silhouettes, delicate details, floor-length cuts—and connected them to wedding occasions even when the product data never mentioned weddings explicitly.

So, when a shopper types ‘wedding dress’ into your search bar, dresses that are suitable for a wedding occasion will show up even though their titles or descriptions don’t contain those exact words.

This is the gap that manual tagging can't close at scale. A person reviewing products might tag obvious items labeled ‘bridal,’ but they'll miss the elegant white dresses that could work perfectly for weddings, but aren't marketed that way.

Why Manual Tagging Breaks Down with Multi-Attribute Searches

The challenge multiplies when shoppers combine multiple attributes in one query. Take ‘wool pleated skirt’ as an example. To match this query, a product must be tagged with:

• Material: wool

• Style: pleated

• Category: skirt

Now consider that a single clothing item can have 20-60 relevant attributes, the possible combinations become enormous. A shopper might search for:

• black leather jacket for winter

• casual striped cotton shirt

• vintage high-waisted wide-leg jeans

• minimalist beige trench coat

• etc.

Each query requires multiple tags to align perfectly. Manual tagging can't maintain this level of coverage across thousands of products. They might tag the obvious attributes but miss secondary or tertiary combinations that shoppers actually search for.

How AI Fashion Tagging Solves the Combination Problem

AI fashion tagging handles this complexity automatically. It analyzes each product image and description, then generates comprehensive tags across all relevant dimensions:

• Materials (cotton, wool, leather, silk)

• Colors (including specific shades like ivory, burgundy, navy)

• Patterns (striped, floral, geometric, solid)

• Style attributes (pleated, belted, cropped, oversized)

• Categories (dress, jacket, skirt, pants)

• Occasions (wedding, casual, work, party)

• Seasonal fit (winter coat, summer dress)

• Design vibes (minimalist, vintage, bohemian, elegant)

This allows specific, multi-attribute searches to surface exactly the right products—even when shoppers combine attributes in ways you never anticipated. The system doesn't need a human to predict every possible search combination. It simply tags comprehensively, and the search engine does the matching.

That's what enables a shopper searching for ‘ivory long coat’ to find a product titled ‘cream wool overcoat’—different words, same intent, perfect match.

CTR Increases When Tagged Products Dominate Results

We analyzed the top 100 search keywords with the highest share of tagged products. The impact on engagement was immediate and measurable. The results speak for themselves:

• Average CTR: 205.76%

• Average Session: 53.34%

• 53.7% of valid clicks went to products that were only discoverable through tagging

In short, more than half of shopper engagement came from products that would not appear without tags. 

Here’s a snapshot of representative queries:

The pattern is clear. When tagged products dominate search results, shoppers engage more. 

Why AI Fashion Tagging Still Needs Fine-Tuning

Adding more tags isn’t always better. Poor tagging creates noise. It surfaces irrelevant products and confuses shoppers instead of helping them. While evaluating whether the additional products brought in by tagging were actually relevant to search queries, we initially found some issues :

The ‘Belt’ Problem

Dresses with attached belts got tagged for details as ‘belt’. But shoppers searching for ‘belt’ seem to look for standalone belt accessories.

The Fix: We refined the AI to tag standalone belt products as ‘belt.’ Dresses with belts are now tagged as ‘belted dress’ or ‘belted skirt’ to avoid confusion.

The ‘White Bag’ Problem

Multi-colored bags with small white accents were tagged as ‘white bag.' However, shoppers searching for ‘white bag’ expect ones that are primarily white, not bags with minor white details.

The Fix: We trained our AI fashion tagging system to apply color tags only when the color is dominant, not when it appears as an accent.

The ‘Flats’ Problem

The AI tagged any footwear with flat heels as ‘flats’—including knee-high boots and ankle boots. But shoppers searching for this keyword expect ballet flats and slip-on shoes. While technically accurate, these results didn't match shopper intent.

The Fix: We updated the tagging to distinguish between ‘flats’ (ballet flats, loafers) and ‘flat heel' (boots and other footwear with flat heels). Now, when shoppers search for ‘flats,’ ballet flats appear first. If they search for ‘flat heel boots,’ those boots match correctly.

The Lesson

Your tagging needs to match search intent, not just technically describe the product. You need to understand what shoppers mean when they search. Then align your tags with those expectations. That requires testing, reviewing real search behavior, and continuously refining tags. 

The Practical Takeaway: Where to Start With Fashion Tagging

The takeaway for retailers is simple: start with core tags for your highest-traffic queries. Focus first on the searches that drive the most revenue. These queries create the biggest impact when tagging improves discovery and relevance.

Once the core is in place, test and iterate on edge cases. Look for queries where shoppers are highly specific, where intent is strong, or where results feel noisy. That’s how you unlock the additional 11% CTR we saw in our study.

Tagging works best as an iterative process, not a one-time setup. Measure how search results perform, review mismatches, and refine tags based on real shopper behavior. Over time, this approach compounds: more relevant results, higher engagement, and fewer missed clicks. Or you can always use pre-process fashion tagging that doesn’t require updates, but is ready to integrate into your search and improve conversion.

This is also where the difference between manual tagging and AI fashion tagging becomes clear.

Get Started with AI Fashion Tagging Without Heavy Commitment

You don’t need a long implementation cycle or a large upfront investment to get value from AI fashion tagging. You can start small. Tag a subset of your catalog. Test performance on high-impact queries. Then, scale as results compound. Our tagging pricing is designed for this exact approach. You can:

• Upload a CSV and get products tagged in minutes

• Plug into a tagging API to automate workflows

• Pay only for what you use, with no long-term lock-in

This makes it easy to experiment, measure results, and expand when the data proves it works.