In fashion, visuals are everything. But behind every product description page is data. From the cut of a hem to the color name in a dropdown, product data dictates how items are discovered, displayed, purchased, and returned. When it’s accurate, it quietly powers the entire system. When it’s not, the consequences hit everything from logistics to customer trust.
A 2024 Forrester Consulting study found that 83% of e-commerce leaders admit their product data is incomplete, inconsistent, inaccurate, unstructured, or outdated. And the effects aren’t just limited to the backend. Poor product data delays launches, limits visibility, frustrates customers, and drives up returns. In fashion, where precision drives sales and margins are tight, that becomes a serious liability.
As brands scale across more retail channels, the problem multiplies. Managing dozens of formatting requirements, image standards, and taxonomies at once adds layers of complexity. But multimodal AI–models that can process both images and text–is emerging as a tool that can finally address these challenges at scale.
When Product Data Undercuts the Sale
Every product page in digital retail is a customer touchpoint, and in fashion, that interaction demands accuracy. Mislabeling a color, omitting a material, or mismatching an image with its description doesn’t just look unprofessional, it disturbs the buying experience.
And it matters to shoppers. According to industry research:
- 42% of shoppers abandon their carts when product information is incomplete.
- 70% exit a product page entirely if the description feels unhelpful or vague.
- 87% say they’re unlikely to buy again after receiving an item that doesn’t match its online listing.
And when products are purchased based on inaccurate product descriptions, brands are being hit hard by returns. In 2024 alone, 42% of returns in the fashion sector were attributed to misrepresented or incomplete product information. For an industry already burdened by return costs and waste, the impact is hard to ignore.
And that’s only if the shopper ever sees the product—error-ridden data can tank visibility, burying items before they even have a chance to convert, leading to lower sales overall.
Why Fashion’s Data Problem Isn’t Going Away
If the issue is this widespread, why hasn’t the industry solved it? Because fashion product data is complicated, inconsistent, and often unstructured. And as more marketplaces emerge, the expectations keep shifting.
Every brand manages catalogs differently. Some rely on manual spreadsheets, others wrestle with rigid in-house systems, and many are tangled up in complex PIMs or ERPs. Meanwhile, retailers impose their own rules: one requires cropped torso shots, another insists on white backgrounds. Even the wrong color name–”orange” instead of “carrot”–can get a listing rejected.
These inconsistencies translate into a tremendous amount of manual work. A single SKU might need several different formatting passes to meet partner requirements. Multiply that by thousands of products and dozens of retail channels, and it’s no surprise that teams spend as much as half of their time just correcting data issues.
And while they’re doing that, priorities like seasonal launches and growth strategy fall behind. Listings go live missing key attributes, or are blocked entirely. Customers scroll past or purchase with incorrect expectations. The process meant to support growth becomes a recurring source of drag.
The Case for Multimodal AI
This is exactly the kind of problem multimodal AI is built to address. Unlike traditional automation tools, which rely on structured inputs, multimodal systems can analyze and make sense of both text and images, similar to how a human merchandiser would.
It can scan a photo and a product title, recognize design features like flutter sleeves or a V-neckline, and assign the correct category and tags required by a retailer. It can standardize inconsistent labels, mapping “navy,” “midnight,” and “indigo” to the same core value, while filling in missing attributes like material or fit.
At the technical level, this is made possible by vision-language models (VLMs) — advanced AI systems that jointly analyze product images and text (titles, descriptions) to understand each item holistically. These transformer-based models are trained on platform requirements, real-world listing performance, and historical catalog data. Over time, they get smarter, learning retailer taxonomies and fine-tuning predictions based on feedback and outcomes.
Tasks that used to take weeks can now be completed in hours, without sacrificing accuracy.
Why Clean Data Speeds Everything Up
When product data is complete, consistent, and well-organized, everything else runs much more smoothly. Items surface in the right searches, launch without delays, and appear in the filters customers actually use. The product shoppers see online is the one that arrives at their door.
That kind of clarity leads to tangible results across the entire retail operation. Retailers can onboard SKUs without lengthy back-and-forths. Marketplaces prioritize listings that meet their standards, improving visibility and placement. When information is clear and consistent, shoppers are more likely to convert and less likely to return what they bought. Even support teams benefit, with fewer complaints to resolve and less confusion to manage.
Scaling Without the Burnout
Brands aren’t just selling through their own sites anymore. They’re going live across Amazon, Nordstrom, Farfetch, Bloomingdale’s, and a long list of marketplaces, each with its own evolving requirements. Keeping up manually is exhausting, and over time, unrealistic and unsustainable.
Multimodal AI changes that by helping brands build adaptive infrastructure. These systems don’t just tag attributes, they learn over time. As new marketplace-specific rules are introduced or product photography evolves, listings can be updated and reformatted quickly, without starting from scratch.
Some tools go further, automatically generating compliant image sets, identifying gaps in attribute coverage, and even tailoring descriptions for specific regional markets. The goal isn’t to replace human teams. It’s to free them up to focus on what makes the brand unique, while letting AI handle the repetitive, rule-based tasks that slow them down.
Let Brands Be Creative and Let AI Handle the Rest
Fashion thrives on originality, not manual data entry. Messy product data can quietly derail even the strongest brands. When the basics aren’t right, everything else–from visibility to conversion to retention–starts to slip.
Multimodal AI offers a realistic, scalable path forward. It helps brands move faster without losing control, and brings order to a part of the business that’s long been defined by chaos.
Fashion moves fast. The brands that succeed will be the ones with systems built to keep up.
