YesPlz.AI, January 2025
In 2024, online fashion sales hit new heights. The global fashion eCommerce market was worth 781.5 billion U.S. dollars. By 2030, experts predict this figure to more than double, surpassing $1.6 trillion.
A recent survey (published in September 2024) highlights some fascinating insights:
Clothing leads the online shopping segment (43%).
Second place goes to footwear (33%).
These findings are based on online responses from over 10,000 people in the United States. They paint a promising future for online fashion retailers. Rather than traditional shops, people are flocking to online stores for their fashion needs.
But with these figures come both challenges and opportunities in 2025. This article walks you through the 2024 landscape backed by key statistics. From there, we’ll find an actionable strategy for fashion eCommerce to stay competitive in 2025.
To begin with, let's take a quick statistical snapshot that reflects the industry in 2024.
The search bar doesn’t always help:
69% of people head directly to the search bar as their first step.
Yet, 80% abandon the process midway.
41% blame irrelevant results for hindering their shopping journey.
More isn’t always better:
74% walk away from an online purchase because of choice paralysis.
Shoppers are open to AI solutions:
79% find it helpful if AI could understand their unique needs and recommend tailored products.
82% want AI to streamline their research and minimize the shopping time.
The 2024 data highlights a clear gap between shopper expectations and eCommerce offerings. Today’s shoppers, especially Millennials and Gen Z, are more digitally savvy than ever. Yet, many online fashion retailers fall short. They pack their sites with products without optimizing the discovery process. Advanced search technologies and tailored suggestions are often missing.
To thrive in 2025, here are three takeaways for fashion retailers:
AI-Powered Search: Upgrade search functionalities for more relevant results.
AI-Driven Product Recommendation: Simplify and personalize the buying process.
AI-Powered Solutions: Leverage AI to enhance speed and satisfaction.
As you can see from the statistics, shoppers are increasingly impatient. Traditional search and generic recommendations no longer please them. As we transition into 2025, smarter, faster, and more personalized shopping experiences are non-negotiable. The difference between leading and lagging in the industry is clear. It depends on how well retailers leverage AI to boost personalization and efficiency.
This shift has pushed some tech-forward, multi-brand retailers to adopt fashion AI tools. From the initial search to the final purchase, these tools help redefine every step of the shopping journey.
For most people, their online shopping journey starts at the search bar. If this initial interaction feels clunky, they’re likely to walk away. So, how can fashion eCommerce optimize this very first touchpoint? What is the key success factor for enabling better product discovery?
From YesPlz's experiences with different retailers, there is no one-size-fits-all formula. Continuous experimentation is the answer. Success comes from testing various search approaches and continuously optimizing them over time.
What do various search approaches mean? Think beyond the search bar and traditional text-based search. You can also let your shoppers search by visuals, occasions, moods, or vibes. In 2025, it’s not just a concept - it’s already happening.
From 2024 backward, retailers had limited eCommerce search tools. Most relied on keyword-based technology built for all types of online stores. This traditional search engine isn’t good enough for fashion eCommerce. Because in fashion, styles, trends, and personal preferences matter deeply. This explains why the search bar doesn’t always help.
As of January 2025, the launch of AI search is set to solve this pain point. This technology integrates text search, fashion vector, and AI to understand shoppers’ intent. Plus, AI search is specifically designed for fashion. Shoppers can ask for style suggestions or search for fashion items in whatever way they used to speak. This makes AI search four times more powerful than traditional technology.
You can read more about the benefits of the hybrid search for online fashion retailers.
Not all shoppers are fluent in fashion jargon. Many don’t keep up with the latest trends. They don’t know the exact terms to type into your search bar. What if finding the perfect outfit was as easy as pointing to a body part? No text is required at all. Meet the virtual mannequin filter for fashion eCommerce.
Powered by AI, this one-of-a-kind fashion model takes the guesswork out of the search process. Instead of relying on words, shoppers can explore through visuals. In detail, it lets shoppers use multiple-faceted filters along with visual cues. It simplifies the search process into two steps:
Step 1: Simply tap a body part.
Step 2: Tap to select a preferred silhouette.
Then, all done! In just seconds, shoppers can customize search queries exactly as they want.
By offering a visual-first search approach, retailers make the initial search more intuitive and enjoyable. Curious about the impact? Check out the case study on how it increased the average cart size by 1.7x. Better yet, see it in action. Why not try the virtual mannequin filter yourself?
Fashion eCommerce platforms often classify products into broad categories like type, gender, age, etc. While practical, this rigid structure doesn’t reflect how shoppers actually think. At YesPlz, we found that shoppers normally begin their search with an occasion in mind.
So, what do you think if your shoppers can navigate your site based on moods, vibes, or occasions? For example, imagine your navigation pane featuring these categories:
Shop by Occasion: Work, night out, home, vacation, off duty, and more.
Discover Trending Vibes: Modern Romance, hype streetwear, and beyond.
By integrating vibe/occasion navigation, retailers can:
Improve product discovery through intuitive pathways.
Help shoppers quickly pinpoint styles that suit their needs.
Foster stronger engagement by offering a user-friendly experience.
If AI search engines optimize the first touchpoint, AI fashion recommendations take a step further by tailoring every interaction. Together, they form a powerful duo for seamless product discovery.
There are many different kinds of recommendations. Without testing, it’s hard to tell which works best for your fashion eCommerce. That’s why we’re here to guide you through each option. Once you understand their features and benefits, you can easily pick and confidently experiment to find the perfect fit for your site.
YesPlz uncovered an intriguing insight during the shopper interview. Once shoppers land on a product detail page, they crave more options in the same style. “I’m hooked on this look, so show me similar products. Don’t make me waste time hunting them down,” one shopper revealed. This insight sparked the creation of similar recommendations.
This feature suggests fashion items that look similar to the one a shopper is viewing. They might share attributes like color, neckline, pattern, etc. From our case study, we found that well-curated similar recommendations can boost sales by up to 15%.
The traditional recommendations rely heavily on keyword matching. A shopper query and product info (titles and descriptions) should contain at least 1-2 similar words. The engine doesn’t actually understand the fashion context. For this reason, shoppers might get poor recommendations.
Meanwhile, AI-powered technology like YesPlz AI does it differently. Leveraged computer vision, it scans and analyzes product images. So, it detects attributes accurately even if product info is missing. The result? Visually similar recommendations. Simple, smart, and shopper-friendly shopping experience.
Why promote just one piece when you can offer an entire outfit? That’s the core idea behind Complete the Look (CTL) recommendations. Many fashion eCommerce have already integrated CTL into their platforms. It is often labeled as ‘How to Wear it’ or ‘Style with.’ Yet, the technology driving their recommendations is quite different.
Some stores rely on merchandisers to handpick outfits. These experts deeply understand their brands and shoppers. So, they can create cohesive and on-brand styles. But here is the downside. Thousands of new fashion items are added daily. Manually creating CTL for every item is almost impossible.
That’s where AI-powered CTL shines. It leverages large language models, lookbooks, and product images. Hence, this AI tech stays updated with fashion trends and styling rules. The result? It can create thousands of fashionable outfits in just minutes. Your shoppers get fresh, trend-driven outfit recommendations for any occasion.
Those who are not fashion-savvy will especially benefit from this feature. It helps them streamline their research and minimize their shopping time.
Shoppers often stick to big-name brands they already know and trust. This tendency can cause them to overlook hidden gems. Many smaller brands offer unique styles and great quality. Yet, they remain under the radar.
AI-powered brand recommendations help uncover these amazing finds. It suggests lesser-known brands that match each shopper’s taste by analyzing:
Shopper preferences
Browsing history
Current trends
With brand recommendations, retailers can:
Bridge the gap between popular brands and lesser-known names.
Enrich the shopping experience, making it more dynamic and exploratory.
Drive sales for diverse brands on your fashion eCommerce.
Collaborative filtering is an AI-driven data approach. It collects and analyzes the behaviors of shoppers with similar tastes. Based on that, it predicts what a shopper might like and makes relevant recommendations.
Let’s take an example to understand how it works.
Shopper 1 bought items A, B, and C.
Shopper 2 viewed or liked items A, B, and D.
The system notices that their preferences overlap. So, it suggests item D to shopper 1 and item C to shopper 2. This is the core tech behind ‘Frequently Viewed Together’ or ‘Frequently Bought Together.’
As time goes on, the system collects more data from shopper interactions - what they click, view, like, or buy. With this growing data set, collaborative filtering gets smarter over time. As a result, it becomes better at personalizing product recommendations.
Combined with similar recommendations, it can solve the cold start problem for fashion eCommerce. For new shoppers or products without much data, the system can rely on patterns from similar shoppers to make suggestions.
Traditional personalized recommendation works like a black box. We don’t know how the curation works for individuals. We can’t also control what ingredients go to the recommendations.
AI-era personalized recommendations, on the other hand, can control what elements go into the recommendations. In this way, we can measure and iterate, thereby tailoring the personalized curation more precisely. As a result, retailers can deliver a shopping experience that’s truly unique for every shopper.
When shoppers log into their accounts, the system reviews their history activities. It gathers and processes anonymized data from the items they clicked, viewed, favorited, added to their cart, and purchased.
Using these data points, the system generates tailored recommendations that feel uniquely crafted for each shopper. These might include:
Similar items: Products matching their preferences in style or function.
CLT items: Pieces to create cohesive, stylish outfits.
Favorite brands: Items from brands they purchased in the past.
Many other fashion data that’s relevant.
Every shopper’s history is unique. So are their recommendations. With AI-driven personalized recommendations, you show shoppers you are not just claiming to be personalized. You’re actually delivering it.
And beyond personalization, YesPlz AI adds another layer of insight. Retailers can access the Product Recommendation Management Console. It allows retailers to analyze shopper behavior throughout their journey. Then, make smarter, data-driven decisions to optimize the discovery experience. You can schedule a demo to learn more details.
As you can see, data is the backbone of fashion AI. It would not function effectively without high-quality, accurate, and comprehensive data. In fashion eCommerce, manually handling data for thousands of items can be overwhelming. Manual tasks like tagging product attributes, writing descriptions, and translating content are time-consuming and prone to error. This is where AI-driven data solutions step in to simplify the operation.
Automated product tagging is powered by computer vision. It is capable of automatically detecting product attributes. YesPlz AI tagging technology can tag 20-60 attributes for every fashion item. The best part? Thousands of products can be processed in just minutes.
Read more about how image tagging optimizes online fashion stores.
Struggling to create rich and engaging product descriptions? The auto-product description has it covered. It generates unique and detailed descriptions that capture a product's essence.
Expanding globally has never been easier with auto-translation. It seamlessly translates product descriptions into multiple languages. Fashion eCommerce can thus break down language barriers and reach worldwide shoppers effortlessly. Our clients have cut manual translation costs by 75% and saved countless operational hours.
The 2024 statistics make one thing clear. The future belongs to retailers who can meet shoppers' expectations. And AI solutions are the key to bridging that gap.
Ready to lead in 2025? YesPlz AI has got you covered. From smart AI search and AI-powered personalized recommendations to AI-driven data, we offer a comprehensive package to keep you ahead.
Our AI models are fully customizable to your fashion eCommerce needs. There are no limitations here. Just share your specific requirements. We’ll tailor the solutions to fit. So, you can enjoy the flexibility to experiment and optimize for your store.
What’s your next move for 2025? Why not schedule a demo with us and let’s get started?
