In fashion, visual effects are everything. But behind each product description page is data. From the cut of the hem to the color name of the drop-down list, product data determines how to discover, display, purchase and return items. When it is accurate, it quietly powers the entire system. If this is not the case, the consequences will touch everything from logistics to customer trust.
A 2024 Forrester Consulting study found that 83% of e-commerce leaders acknowledge that their product data is incomplete, inconsistent, inaccurate, unstructured, or outdated. The effect is not limited to the backend. Poor product data delays startup, limits visibility, foils customers and drives returns. In fashion, precision drives sales and profits are tight, which will be a serious responsibility.
As brands expand across more retail channels, the problem is multiplied. Immediately manage dozens of format requirements, image standards and taxonomy can add complexity. But a multimodal AI model that can process images and text simultaneously is a tool that can ultimately solve these challenges at a large scale.
When product data decreases sales
Every product page in digital retail is a customer touchpoint, and in terms of fashion, this interaction requires accuracy. Label colors, omitting materials or mismatching images that do not match their descriptions not only look unprofessional, but also disturbs the buying experience.
It’s important to shoppers. According to industry research:
- When product information is incomplete, 42% of shoppers give up their cart.
- If the description feels helpless or blurry, 70% will exit the product page completely.
- 87% say they are unlikely to buy again after receiving items that do not match their online listings.
And, when purchasing a product based on an inaccurate product description, brands take a huge hit with the rewards. In 2024 alone, 42% of the fashion sector’s returns were attributed to statements or incomplete product information. It is difficult to ignore the impact for industries that are already burdened by returns costs and waste.
This is only when shoppers see the product, the entangled data can attract visibility, and even bury the item before they have the opportunity to convert, resulting in a decrease in overall sales.
Why does fashionable data problems not disappear
If the problem is this, why is the industry not addressing it? Because fashion product data are complex, inconsistent, and often unstructured. As more and more markets emerge, expectations continue to shift.
The management catalog of each brand is different. Some rely on manual spreadsheets, others rely on stiff internal systems to fight, and many are struggling in complex PIM or ERP. Meanwhile, retailers impose their own rules: one needs to cut the torso, the other sticks to a white background. Even the wrong color name – “orange” instead of “carrot” – the list can be rejected.
These inconsistencies translate into a lot of manual work. A SKU may require several different format passes to meet the requirements of a partner. It’s no surprise that multiplied it by thousands of products and dozens of retail channels, and the team only spent half of their time correcting the data problem.
While doing so, priorities such as seasonal rollouts and growth strategies lag behind. The list will lose key attributes or be blocked completely. Customers scroll or purchase with incorrect expectations. The process designed to support growth becomes a recurring source of procrastination.
The situation of multimodal AI
This is exactly the problem that builds the problem used to solve. Unlike traditional automation tools that rely on structured input, multimodal systems can analyze and understand text and images in a way similar to human goods.
It can scan photos and product titles, identify design features such as Flutter casing or V-neck wire, and assign the correct categories and labels that retailers need. It can normalize inconsistent labels, map “Navy Night” and “Indigo” to the same core values while filling in missing attributes such as material or fit.
On a technical level, this is through Visual Language Models (VLMS) (co-analyzing product images and text (title, description)) to have a comprehensive understanding of each project’s advanced AI system. These transformer-based models are trained on platform requirements, real-world market performance and historical catalog data. Over time, they became smarter, learning retailer taxonomy and fine-tuning predictions based on feedback and results.
Now, tasks that used to take weeks can be completed in a few hours without sacrificing accuracy.
Why clean data speeds up everything
Everything else goes smoother when product data is completed, consistent and well organized. The project surfaces in the correct search, does not delay startup, and appears in the filters that the customer actually uses. What shoppers see online is the products that arrive at their doorstep.
This clarity can lead to a tangible effect of the entire retail business. Retailers can board Skus on a short round trip. The market prioritizes lists that meet their standards, thereby improving visibility and location. When the information is clear and consistent, shoppers are more likely to convert and are less likely to return what they purchased. Even the support team has benefited a lot, with fewer complaints on solutions and fewer confusions about management.
Scale without burnout
Brands are no longer just selling through their own websites. They will be played on Amazon, Nordstrom, Farfetch, Bloomingdale and a long list of markets, each with its own evolving requirements. Keeping tired manually, over time, is unrealistic and unsustainable.
Multimodal AI will change by helping brands build adaptive infrastructure. These systems are not only marking attributes, but learn over time. With new market-specific rules or product photography, lists can be updated and reformatted quickly without starting from scratch.
Some tools have further evolved to automatically generate compatible image sets, identify gaps in attribute coverage, and even tailor descriptions for specific regional markets. The goal is not to replace human teams. This is to free them up to focus on what makes brands unique while allowing AI to handle rule-based repetition, rule-based tasks, thus slowing them down.
Let brands use creativity and let AI handle the rest
Fashion thrives in creativity, not manual data entry. Messy product data may even be the most powerful brand. When the basics are incorrect, everything else (from visibility to conversion to retention) will start to slip.
Multimodal AI provides a realistic, scalable path. It can help brands move faster without losing control and bring orders to a part of the business that has long been defined.
Fashion moves very quickly. A successful brand will be the system that builds the system.
