Inaccurate Fashion Data Proving Expensive, AI Offers Potential Solution
The role of multimodal AI in the fashion industry has become increasingly important as it tackles the challenges associated with inconsistent, incomplete, and inaccurate product data. This advanced technology is capable of processing various data types, including text, images, and videos, to generate standardized, enriched, and reliable product information.
Inaccuracies in product data can be detrimental to the entire retail process. A 2024 study by Forrester Consulting found that 83% of e-commerce leaders admit their product data is incomplete, inconsistent, inaccurate, unstructured, or outdated. This issue doesn't just affect the behind-the-scenes aspects but also impacts customer trust, launches, and overall visibility.
In the fashion industry, precision is crucial for driving sales, and with tight margins, inaccuracies can become a significant liability. As brands expand across multiple retail channels, managing numerous formatting requirements, image standards, and taxonomies becomes increasingly complex. However, the emergence of multimodal AI models, which can process both images and text, offers a promising solution.
The implications of poor product data are far-reaching. Inaccuracies on product pages can frustrate customers, leading to cart abandonment and poor conversion rates. For instance, a survey found that 42% of shoppers abandon their carts when product information is incomplete, while 70% exit a product page entirely if the description is unhelpful or vague. Furthermore, 87% of shoppers say they're unlikely to buy again after receiving an item that doesn't match its online listing.
Returns, a significant issue in the fashion industry, are also escalated by inaccurate product descriptions. In 2024 alone, 42% of returns in the fashion sector were attributed to misrepresented or incomplete product information. To some industries, the cost and waste associated with returns may not seem substantial, but in an industry already struggling with these issues, the impact is significant.
Moreover, inaccurate product data can hinder visibility, burying items before they even have a chance to convert, leading to lower sales overall. The industry's Data Problem persists because fashion product data is complex, inconsistent, and often unstructured. Different brands manage their catalogs in unique ways, relying on everything from manual spreadsheets to rigid in-house systems and complex PIMs or ERPs.
Retailers often impose their own rules, requiring cropped torso shots, white backgrounds, or specific color names, which if not met could lead to listing rejection. These inconsistencies lead to a significant amount of manual work, with teams spending as much as half of their time merely correcting data issues.
Multimodal AI is capable of addressing these challenges at scale by leveraging vision-language models (VLMs). These advanced AI systems, trained on platform requirements, real-world listing performance, and historical catalog data, can jointly analyze product images and text to understand each item holistically. This ability enables them to scan a photo and a product title, recognize design features, assign the correct category and tags, standardize inconsistent labels, and fill in missing attributes.
The benefits of clean, consistent data are numerous. Items surface in relevant searches, launches occur without delays, and customers see the accurate product they intend to buy. This clarity positively impacts the entire retail operation, from onboarding SKUs to improving visibility and placement, reducing returns, and streamlining support tasks.
As brands continue to expand their reach across various retail channels, multimodal AI can help in scaling operations without the burnout. By learning over time and adapting to new marketplace-specific rules and product photography trends, listings can be updated and reformatted quickly without repetitive manual work.
Ultimately, fashion thrives on originality, not manual data entry. By leveraging AI to manage repetitive, rule-based tasks, fashion brands can focus on what truly differentiates them while ensuring accurate product information, which translates to better sales, customer trust, and overall success. The brands that excel in the rapidly evolving fashion landscape will be those with systems built to keep up.
In light of the complex and inconsistent nature of fashion product data, the application of multimodal AI in lifestyle sectors like fashion-and-beauty could prove effective in standardizing and enriching product information, addressing inaccuracies that often impact customer trust, launches, and overall visibility. Moreover, as multimodal AI is capable of processing diverse data types such as text, images, and videos, it offers a promising solution to the industry's data problem, thereby reducing returns, improving search relevance, and streamlining support tasks.
