6 Critical Flaws AI Search Engines Can Solve: Revolutionizing Online Fashion Search

Every year, retailers lose $2 trillion - not to competitors [1]. But to something far more preventable: Bad online SEARCH experiences.

Outdated search engines fail to keep up with how modern consumers shop. For years, it has worked under an old assumption. Shoppers are willing to search, filter, and refine repeatedly to find their desired items. Yet, today’s shoppers don’t have that patience. They expect to locate what they need easily and quickly. 98% say that their favorite websites are ones where they can quickly find the best match [2]. Failing to understand shoppers' intent comes at a cost.

What can online fashion retailers like you expect from this paper?

A look at:

Six technical flaws of traditional search engines that cost you sales and loyalty.
AI search engines for fashion retailers that enhance search accuracy by 4x performance [3]:

Hybrid of image and text search
Automatic fashion product tagging
Style-aware fashion embedding
Contextual language processing for search intent detection
Automated trend keyword updates

6 Flaws AI Search Engines are Solving for Fashion Retailers

This section uncovers six technical flaws that frustrate online shoppers and hurt retailers’ revenue. More importantly, you’ll discover how fashion AI search engines can fix them. With these insights, you can transform your search bar from a sales blocker into your most powerful eCommerce sales assistant.

#1 Incomplete Product Data

Poor product data contributes to bad online search experiences. But why does this problem persist in fashion eCommerce?

It all comes down to manual product data management, which is slow, costly, and error-prone. Typos, missing key attributes, duplicate information, and wrong tagging are common. Plus, siloed departments and disconnected systems make it difficult to maintain data consistency.

For example, incorrect tagging and missing attributes can derail search results. Consider a midi skirt mistakenly tagged as a miniskirt. When someone searches for a miniskirt, the wrong product pops up. Or, what if retailers miss key details like neckline type and sleeve length in a long-sleeve turtleneck sweater? Shoppers are forced to scroll endlessly or refine their search multiple times to find the best match.

When a search fails, so do sales. But AI search engines for fashion can help retailers avoid these issues.

AI Solution: Smarter Tagging for Precision Search Results

AI can automate tagging across a retailer’s entire product catalog, reducing manual errors. From a single product image, it extracts important information with precision, including:

Key fashion attributes: neckline, sleeve length, cut style, material, color, pattern, etc.

Occasion-based categories: work, night out, music festival, workout, etc.

This automatic process enriches retailers’ product data without manual taxonomy management. It ensures precise and consistent product information, leading to accurate search results. Shoppers get what they want instantly. No more endless scrolling, just seamless discovery.

#2 The Keyword Mismatch

Variations in wording can impact search accuracy. Traditional search engines rely on exact text matching. It fails to recognize slight variations in words, especially in natural fashion language. This leads to three key challenges: keyword mismatches, synonym handling issues, and lack of stemming. For this reason, many relevant products stay hidden.

Imagine this: A shopper searching for ‘vinyl skirts.’ Ideally, the shopper should see all relevant options in stock. But in reality, only a few appear in search results. The reason? The buried items are labeled with a different names like ‘slim coated skirt’ or ‘glossy leather skirts.’

A keyword-dependent search engine won’t recognize them as perfect matches simply because the wording differs. It fails to connect the dots. This issue hides relevant products, frustrates shoppers, and costs you sales.

An AI search engine with fashion-embedding models can solve these wording variation issues. It ensures every item in retailers’ product catalogs gets found. But how?

AI Solution: Style-Aware Fashion Embedding Model Unlocks Hidden Matches

Retailers can make every item discoverable by incorporating a fashion-embedding model. This fashion-trained AI leverages visual technology to eliminate keyword dependency. It identifies fashion styles and attributes by scanning product images.

Thanks to this capability, the model doesn’t require an exact keyword match in product data. Instead, it intelligently connects search queries with information extracted from product images. This approach lets the model improve search results in both quantity and quality.

When analyzing a client’s catalog, we found 72 relevant products for vinyl skirts. A traditional search engine returned only one result because it could only match exact keywords. Meanwhile, the fashion embedding model surfaced 71 matching products that would have otherwise remained hidden.

What does this mean for retailers? No need to update keywords or variations manually. And stop losing sales to outdated search engines.

#3 The Intent Misinterpretation

The exact text matching mechanism forces traditional search engines to break down a search query word by word. This approach overlooks the shopper’s intent. It consequently leads to frustrating misinterpretations.

Take a search for ‘glossy vinyl hype jacket,’ for instance. A traditional search engine treats each word separately. It then returns a random mix of:

Glossy accessories
Vinyl dresses
Hype sneakers
Various jackets

Yet, none of these items precisely match what the shopper wants.

How can AI search engines for fashion better understand shopper intent?

AI Solution: Contextual Language Processing to Understand Searcher Intent

LLM-powered AI search engines can interpret queries based on context rather than individual words. They understand shoppers' queries and their context, especially for fashion items.

Instead of processing each word in isolation, the search engine analyzes the query as a whole. It recognizes that the shopper is looking for a jacket made of glossy vinyl and matching the hype vibe.

Type: Jacket
Material: Glossy vinyl
Vibe: Hype

By recognizing shoppers’ intent, a fashion-trained LLM delivers highly relevant results on the first try. This enhances the shopping experience, reduces frustration, and minimizes the need for multiple searches.

#4 Noisy Product Descriptions

A product description does more than just tell shoppers what an item is. It often comes with styling tips and marketing jargons as well to help shoppers. A well-crafted description typically includes:

Product attributes: Type, style, color, waist, size, material, etc.

Styling tips: Suggestions on how to pair the item with other products.

Marketing language: Band storytelling or emotional appeals to influence purchasing decisions.

While helpful for shoppers, styling tips and marketing language can confuse search engines and corrupt search results.

For example, imagine a retailer selling a white shirt. The product description might read:

‘A classic form-fitted white shirt that pairs perfectly with black sneakers for a casual look.’

A query for ‘white shirts’ could mistakenly display black sneakers in the search results. This happens because the phrase ‘black sneakers’ appears in the description. It creates wrong connections in search indexes, leading to irrelevant products. A traditional search engine falsely associates it with the primary product.

This issue highlights the need for AI search engines for fashion. Retailers need one that can filter out misleading associations and improve search accuracy.

AI Solution: Noise Removal for Cleaner, More Accurate Search

AI search engine can pre-process noise, intelligently classifying a product description into 3 distinct categories. For instance, it would break down the white shirt description as follows:

Product attributes:

Type: Shirt
Style: Classic
Color: White
Waist: Form-fitted

Styling tips:

Pair with black sneakers for a casual look
Perfect for layering with a soft, textured cardigan.

An AI search engine for fashion detects additional details in styling tips and marketing language as noisy information. It then intelligently separates them from core product attributes. This prevents irrelevant words from influencing search accuracy. Shoppers can enjoy a cleaner, more accurate search experience. They see only the most relevant products without misleading distractions.

#5 Manual Trend Keyword Update

Fashion trends move fast. Keeping up with the latest trends and popular search terms is crucial for fashion retailers. These trendy keywords are exactly what shoppers will use to find what they need. Updating product information with these terms helps shoppers find their desired items more easily.

For this reason, retailers can collect trending keywords from social media platforms or key fashion publications. Then, it maps the trending fashion keywords to the product catalog. However, manually tracking and updating keywords is both time-consuming and expensive. This is a challenge that not all fashion retailers can afford to take on.

AI Solution: Self-Optimizing Trend Keyword Mapping for Real-Time Updates on Popular Search Terms

An AI search engine for fashion acts as a real-time trend radar. It can continuously monitor social media and identify what people are discussing regarding fashion trends.

Identify trending keywords
Automatically update search libraries with these keywords
Always keep the search keywords fresh

Consider when Y2K becomes a trend. The engine instantly detects and gathers related keywords. It links them to relevant products, such as a ruched glitter tank crop top. This ensures shoppers find relevant fashion items using the latest fashion trend keywords. This automation eliminates the need for constant manual updates from the eCommerce team.

#6 Primitive Filtering System

A search doesn’t end with a query, it continues with filters. Shoppers rank search filters as the second most important website feature (73%) [1]. Yet, 30% get frustrated when they can't narrow down results because of limited filter options [4].

Traditional search engines only offer basic filters like price and color. That’s not enough, especially for fashion shoppers. Imagine a shopper looking for ‘skirts with night out vibe.’ They shouldn’t have to scroll endlessly through hundreds of dresses. But they do because there are no specific filters designed for vibe options.

AI Solution: AI-powered Fashion-specific Filters for Better Product Discoverability

Well fashion-trained AI provides fashion-specific filters by automatically tagging fashion taxonomy [5]. Unlike traditional search systems, retailers don’t have to manually tag product attributes or set up filters themselves. An AI search engine for fashion would handle this automatically.

For example, a search for ‘skirts with night out vibe’ can instantly trigger relevant filters for:

Style vibe
Occasions
Skirt Length
Skirt Style

These taxonomies are automatically created and added to a filter system. This AI solution simplifies the shopping process for shoppers, as they don't waste time scrolling through irrelevant items.

Conclusion

A bad search isn't just frustrating. It’s a $2 trillion mistake. Online consumers have evolved. However, traditional search engines still work on an old assumption and outdated mechanism that still requires a lot of work from fashion retailers. The result? Frustrated shoppers, abandoned carts, and missed opportunities.

But there is good news. AI search engines can help. They offer a range of solutions, including automated image tagging, fashion embeddings, LLMs for intent detection, and trend keyword management. These AI tools can help alleviate the workload for retailers, allowing them to focus on other important tasks.

Imagine if all these AI innovations were combined to create the most powerful search engine. That's precisely what YesPlz AI offers. Our AI search engine integrates these advanced solutions seamlessly, transforming the way fashion eCommerce search works.It helps fashion retailers like you eliminate the technical flaws that cause lost sales. It ensures shoppers find what they need quickly, accurately, and effortlessly.

In an industry driven by speed and personalization, retailers who upgrade their eCommerce search engine today will outpace competitors tomorrow.

Ready to stop losing sales to bad searches? By adopting a fashion AI search solution, retailers can turn the search bar into a revenue-driving powerhouse.

Curious to see how it works? Schedule a free consultation today.