How Poor Product Data Is Harming Fashion and the Role of AI in Fixing It

The Critical Role of Accurate Product Data in Fashion

In the fashion industry, visuals play a vital role, but product data is the backbone behind every product description page. Details from the cut of a hem to the exact color name in a dropdown menu influence how products are discovered, displayed, purchased, and even returned. When this data is accurate, it seamlessly supports the entire system; when flawed, it causes wide-reaching issues affecting logistics and customer trust.

The Impact of Bad Product Data

According to a 2024 Forrester Consulting study, 83% of e-commerce leaders admit their product data is incomplete, inconsistent, inaccurate, unstructured, or outdated. Poor product data leads to delayed product launches, limited visibility, frustrated customers, and increased return rates. In fashion, where precision is essential and margins are tight, these problems can severely impact business performance.

Challenges Multiply with Scale

As fashion brands expand across multiple retail channels, managing diverse formatting requirements, image standards, and taxonomies becomes increasingly complex. Multimodal AI, capable of processing both images and text, is emerging as a promising solution to tackle these challenges at scale.

How Product Data Affects Sales

Each product page serves as a critical customer touchpoint. Errors such as mislabeling colors, omitting materials, or mismatching images with descriptions disrupt the buying experience. Research shows:

42% of shoppers abandon carts when product information is incomplete.
70% leave product pages if descriptions are vague or unhelpful.
87% are unlikely to repurchase after receiving items that don't match online listings.

Inaccurate descriptions also lead to high return rates—42% of fashion returns in 2024 were due to misleading or incomplete data. Furthermore, poor data can reduce product visibility, lowering overall sales.

Why the Data Problem Persists

Fashion product data is inherently complex and inconsistent. Brands use varied systems ranging from manual spreadsheets to complex PIMs and ERPs. Retailers impose differing requirements, such as specific image styles or background colors, leading to significant manual efforts to comply. This manual work consumes up to half of teams' time, delaying launches and impairing growth strategies.

The Promise of Multimodal AI

Multimodal AI systems, particularly vision-language models (VLMs), analyze both images and text to understand products comprehensively. They can identify design features, standardize inconsistent labels, and fill in missing attributes automatically. These models improve over time by learning from marketplace rules and catalog data, drastically reducing the time needed for data preparation without sacrificing accuracy.

Benefits of Clean, Consistent Data

Accurate and well-structured product data ensures products appear in relevant searches, launch on time, and match customer expectations. This clarity improves retailer onboarding, marketplace visibility, customer conversion, and reduces returns and support issues.

Scaling Fashion Retail with AI

With brands selling across multiple marketplaces each having unique requirements, managing product data manually is unsustainable. Multimodal AI enables adaptive infrastructures that evolve with new rules and content formats. Some AI tools even generate compliant image sets and tailor descriptions for regional markets, freeing human teams to focus on creative and strategic work.

Enabling Creativity Through AI Support

Fashion’s success depends on creativity rather than manual data entry. Multimodal AI offers a scalable, practical solution to bring order to product data chaos, allowing brands to move faster and maintain control. The future belongs to those who can keep pace with the industry's rapid changes through intelligent systems.