Fashion tagging is the foundation of accurate occasion filters. Learn how better tagging improves search and discovery.
by Jess Erdman, Content Marketing LeadJune 2022
A Day in the Life of a Frustrated Shopper
5:00 PM: Log off from Slack and start opening dozens of tabs to find a dress for a day in the office. Something’s flattering, but hides the arms.
6:00 PM: Begin to question why 20 tabs are open. Close a few and go to the favorite fashion eCommerce store.
6:15 PM: Filter by work occasion. But the search results include dresses that are not office-related. Why are these dresses under the work occasion filter? The shopper closes the tabs and gives up.
The retailer had a robust product catalog and occasion filters, so what went wrong?
In this article, we'll take a closer look at fashion tagging, the foundation of accurate occasion filters, and how to get it right. Specifically, we'll cover:
The gap between expectations and reality in occasion filters
Why fashion tagging for occasions matters to retailers and shoppers
Our research shows that shoppers tend to have an open search mindset, with specific product attributes in mind. For example, “I’m open to a work-appropriate dress, as long as I can hide my arms and the silhouette is flattering.”
How can shoppers quickly find that dress? The current search experience forces them into two frustrating options:
Enter into the search bar different sets of keywords, let’s say, ‘work dress long sleeve,’ ‘office outfit flattering,’ ‘work dress fitted,’ until they find the one they like.
Scroll through long lists of text filter options, select attributes one by one, and reload results each time.
Shoppers want to search within their preferred attributes but still feel inspired to discover. Occasion filters are designed to bridge that gap. They let shoppers explore a full catalog on occasion. But most retailers aren't getting them right.
Shoppers want occasion filters. Wedding, vacation, workout, date night — these reflect how people actually think about getting dressed. And these filters are consistently listed as the best practices in eCommerce. So why are so many of them broken?
Building occasion filters is harder than it looks. With large, complex catalogs, manually grouping products with similar attributes under a theme is nearly impossible.
Besides, there's another challenge: the subjectivity. Think about this question: What attributes make a dress suitable for vacation? Is it the pattern, the fabric, the cut? The answer varies depending on who you ask. And that's exactly why so many occasion filters return irrelevant results.
Below are 3 pain points of fashion retailers when creating occasion filters.
Most occasion filters are too basic. Shoppers want more than broad buckets such as ‘casual’ or ‘formal.’ They're looking for categories like ‘workleisure’ or ‘vacation-chic’ that reflect how they actually dress.
Based on our research, shoppers often start their search from an occasion, let’s say, a dress for my sister's graduation. However, current filters don't match that thinking. In addition, the problem with the language of fashion search also exists. Shoppers are asked to interpret retailers’ language, which leads to frustration.
No matter how smooth the search experience is, shoppers still expect to see accurate search results. When a work occasion filter returns a lace-trim camisole top, it frustrates shoppers and damages brand trust.
Problem 3: No Vibe or Mood FiltersOur user interviews show that occasion filters alone are still too limiting. Shoppers want to go further, filtering by vibe and mood, like minimal workwear or vacation-chic 2-piece outfits. Current filter systems in many online fashion stores aren't built for this level of nuance. But shoppers are ready for it.
We've seen the problem. Now the question is: how do you get occasion filters right? It starts with tagging. When attribute tagging is inaccurate or incomplete, this is exactly why so many occasion filters get it wrong. The result is what we saw earlier: a lace-trim camisole top showing up under the work occasion filter. Accurate fashion tagging is the foundation of good occasion filters.
Fashion tagging is the process of identifying and labeling a fashion item's attributes, such as silhouette, neckline, sleeve length, fabric, and more. Once those attributes are tagged, AI uses them to determine which occasion category each product belongs to.
For example, a dress tagged with ‘long sleeve,’ ‘collared neckline,’ ‘fitted waist,’ and ‘midi length’ gets categorized as work or formal. A dress tagged with ‘floral print,’ ‘lightweight fabric,’ and ‘flowy silhouette’ gets categorized as vacation or casual.
Occasion filters reflect how shoppers naturally think about clothing. Nobody wakes up thinking, "I need a midi dress with a collared neckline." They think "I need something for my sister's wedding" or "I need a look for my first day at a new job." Occasion filters bridge the gap between how retailers organize products and how shoppers actually search for them.
When shoppers can filter by occasion, they find relevant products faster and are more likely to convert. For retailers, getting occasion tagging right also means:
More product visibility: Items that were previously buried in generic categories get discovered through themed filters
Better personalization: Occasion-based results feel curated
Stronger brand trust: When filters work as expected, shoppers trust your store and come back
Thematic and occasion filters are inherently subjective. One shopper's romantic blouse is another's feminine workwear. Vintage can mean timeless elegance or secondhand charm, depending on who you ask.
Many retailers are still manually mapping products to occasion filters, relying on one or two merchandisers' interpretations. The result is the exact problem we described above: inaccurate filters, frustrated shoppers, lost sales.
YesPlz does it differently — with a hybrid approach that combines crowd training and fashion-trained AI.
To eliminate subjectivity, YesPlz conducts real shopper interviews to define what occasion terms actually mean to shoppers. Definitions like ‘vacation,’ ‘workwear,’ or ‘date night’ are validated across a large enough group that we can establish consistent, accurate criteria.
YesPlz trains AI to understand what a product looks like and how it feels. The model learns to identify vibe, mood, and occasion directly from product images and continues to improve with every iteration.
Take this example: a grey wool-blend midi dress with a notched lapel, long sleeves, and a slim belt. YesPlz's Fashion Tagger identifies it across multiple attributes simultaneously:
The result of combining crowd training and fashion AI is occasion filters that are accurate, scalable, and genuinely reflective of how shoppers think. Products appear where they belong. Shoppers find what they're looking for. And retailers stop losing shoppers to bad search results.
Fashion tagging for an occasion doesn't need to be difficult to implement. With an AI-powered solution like YesPlz, both shoppers and retailers can finally enjoy the full benefits of occasion filters.
These three filter types operate on different levels of shopper intent. Occasion filters answer the most practical question: "Where am I going?" They cover contexts like a job interview, a beach holiday, or a wedding.
Vibe filters go one layer deeper. They capture the overall aesthetic a shopper wants to project, such as old money, coastal grandmother, or quiet luxury.
Mood filters are the most personal of the three. They reflect how a shopper wants to feel while wearing the item, whether that's confident, relaxed, playful, or powerful.
The most sophisticated discovery experiences layer all three. Shoppers can find something perfect for a rooftop dinner that feels effortlessly chic and makes them feel unstoppable.
Rather than forcing each product into a single occasion box, YesPlz's Fashion Tagger evaluates every item across multiple attribute dimensions simultaneously. This means an item can be placed on different occasions. These multi-occasion assignments reflect how people actually think, not just how a merchandiser might categorize it.
Many retailers start with in-house tagging. A small team of merchandisers manually categorizes products based on their own judgment. It feels controllable, but it doesn't scale.
The core problem is scalability. Two merchandisers can look at the same floral wrap dress and disagree on whether it belongs under vacation, date night, or garden party. Multiply that inconsistency across thousands of SKUs, and the result is an occasion filter system that shoppers simply cannot rely on.
As new products are added daily, manual tagging becomes a bottleneck. It slows down product discovery and leaves new arrivals buried in generic categories.
YesPlz solves both problems. By grounding occasion definitions in real shopper research rather than individual opinion, subjectivity is removed from the equation. And because the AI processes and tags products automatically at scale, there is no bottleneck. New arrivals are tagged consistently and immediately.
The difference in outcome is significant. In-house tagging reflects how a handful of merchandisers think. YesPlz reflects how thousands of real shoppers think.
Written by Jess Erdman
Content Marketing Lead
I'm passionate about creating cool content. The best part? I get to learn new things about fashion tech and ecommerce everyday. Have an idea or opinion about this article? Reach out at jess@yesplz.ai
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