The fashion industry faces a fundamental and pervasive challenge: inconsistent sizing. This issue leads to ill-fitting garments and a surge in product returns, eroding consumer trust, impacting brands' financial health, and contributing to environmental waste. Recent research indicates that poor fit and varied sizing are significant deterrents for consumers, with ill-fitting items being the primary cause of returns. Addressing this widespread problem, particularly prevalent in womenswear, presents a considerable opportunity for innovation and improvement within the sector.
Phoebe Gormley, an entrepreneur with a decade of experience in bespoke tailoring, observed firsthand the persistent frustration among women regarding clothing sizes. Her observations revealed a universal complaint: regardless of age, price point, or shopping venue, women consistently expressed dissatisfaction with sizing inconsistencies. This led her to believe that the fashion industry had fundamentally lost its way in establishing reliable garment dimensions, prompting her to seek a technological solution to this enduring issue.
Driven by her insights, Gormley established Fit Collective, an AI-powered operating system designed specifically for the fashion sector. This platform aims to equip brands with crucial data for enhancing garment production. By analyzing customer return data, fabric properties, and sizing nuances, Fit Collective provides actionable intelligence to rectify fit problems. The startup recently secured \u00a33 million in pre-seed funding, complemented by a \u00a3324,000 UK government grant. This capital will primarily be allocated to recruiting engineers specializing in machine learning to further develop Fit Collective's AI capabilities. Since its launch in late 2023, the company has attracted ten clients, including notable brands like Rixo and Boden, accumulating a rich dataset to pinpoint the root causes of sizing discrepancies.
Gormley highlights that sizing issues disproportionately affect women. While menswear typically sees return rates around 15%, womenswear can experience rates as high as 40-50%, and for luxury womenswear, this can escalate to approximately 60%. This disparity stems from women's clothing designs often deviating more significantly from initial sizing templates, known as "blocks," compared to men's apparel. The use of more intricate designs and a broader array of fabrics, from ultra-lightweight to highly stretchy materials, further complicates consistent sizing. As brands base new garment sizes on previous ones, these deviations from original sizing blocks tend to amplify over time, exacerbating the problem. For luxury brands, the challenge is intensified by lower sales volumes, which results in less return data compared to mass-market retailers, making it harder to identify and correct fit issues.
Gormley acknowledges that securing funding for Fit Collective's pre-seed round was challenging, partly because investors view fit technology with skepticism due to past failures of numerous tools in significantly reducing return rates. Existing solutions often involve AI-driven body scans requiring customers to submit photos for measurement estimates, or "find my size" buttons on product pages that ask for height, weight, and typical size. Both approaches introduce friction and demand considerable customer input, leading to low adoption rates; Gormley notes that only 3% of consumers use these tools among Fit Collective's current clients.
Beyond customer input, a major hurdle for brands is the substantial variation in sizing within their own product lines. Gormley's analysis of one high street brand revealed a 66cm difference between the smallest and largest size 12 shirts among 179 women's shirts, underscoring the lack of internal consistency. She argues that instead of focusing solely on post-purchase solutions like "find my size" tools, the industry should prioritize consistent product fit from the outset. Gormley believes AI can help brands standardize their sizing by leveraging existing transaction and return data, which is often underutilized. While transaction data informs production teams about popular items, the detailed reasons for returns are frequently overlooked, representing a missed opportunity for improvement.
Fit Collective's software is designed to harness this overlooked data. Its AI capabilities analyze returns, fabric characteristics, and sizing inconsistencies, consolidating them into a comprehensive platform. This functions as a "co-pilot" for brands during production, guiding them toward smarter sizing decisions and a subsequent reduction in returns. Eighty percent of Fit Collective's product is a backend software tool that allows brands to monitor garment performance. The dashboard provides insights for each SKU, rating its commercial and fit success (red, amber, or green) based on sell-through rates, return rates, lost value from returns, and the proportion of overall returns. It gathers feedback on product fit and fabric quality from customer return information and suggests actions for data reporting, often integrating with other sources like manufacturer logs. Crucially, it predicts future return rates and potential revenue changes based on recommended improvements.
For brands operating on Shopify, Fit Collective offers a one-click installation app that integrates with their existing data tools via API keys. This connection to transaction and return data is vital, and additional data points like customer reviews and product lifecycle management (PLM) systems can be integrated to enhance insights. For brands not using Shopify, Fit Collective can be implemented via an API connection to their data warehouse. Operating on a subscription model, Fit Collective's costs are based on a brand's revenue and return rates; for instance, a womenswear brand with $10 million in revenue might pay \u00a31,000 per month. This investment is designed to yield returns over time, typically within six to twelve months, as sizing improvements lead to reduced returns and increased profitability.
Fit Collective also provides consumer-facing features that embed fit data directly into product descriptions on websites, ensuring that all customers receive accurate sizing advice before purchasing. Gormley notes that unlike separate "find my size" tools, this integrated approach captures 100% of consumers, significantly reducing return rates and quickly recouping the annual contract value for brand clients. While the production-side improvements take longer to manifest, the immediate impact of consumer-facing recommendations helps to reduce returns in the interim. As large language models like ChatGPT and Google advance in shopping integrations, Fit Collective's Shopify integration will enable its sizing recommendations to be pulled by these AI platforms, offering consumers accurate fit advice across various digital touchpoints. Gormley envisions a future where consumers' body measurements, possibly stored on their phones via technologies like Apple's in-camera measurement apps, can be leveraged by AI shopping platforms to filter vast product selections, ensuring that only items with a high probability of fitting are presented.
In the short term, Gormley emphasizes the substantial financial and sustainability opportunity for the fashion industry in helping brands create better-fitting products. By reducing return rates, brands can free up capital to invest in higher quality production, thereby breaking a "negative spiral" where quick, cheap manufacturing leads to high returns and shrinking budgets. Fit Collective aims to reverse this trend, empowering brands with data-driven confidence to invest in product quality, ensuring garments fit customers better and significantly reducing return rates from figures as high as 60%.