How could AI fix fashion’s fit problem? | BBC News

Key Concepts

• High Street Sizing Inconsistency: The significant variation in clothing sizes offered by different retailers, leading to customer confusion and dissatisfaction.
• Fit Collective: A tech startup utilizing AI to address clothing sizing issues for fashion brands.
• PLM Data (Product Lifecycle Management): Data related to the manufacturing process of garments, including design specifications, materials, and production details.
• Commercial Data: Sales and customer interaction data, including transactions, returns, reasons for returns, customer reviews, and customer service feedback.
• AI-Powered Analysis: The use of artificial intelligence to process and interpret large datasets of PLM and commercial data to identify patterns and actionable insights.
• Return Rate Reduction: The primary goal of Fit Collective, aiming to decrease the percentage of clothing returned by customers due to poor fit.
• Financial Impact: The quantifiable savings and increased profitability for brands resulting from reduced returns.
• Global Sizing Differences: Variations in body shapes and preferences across different geographical regions, necessitating tailored sizing strategies.

The Problem with Clothing Sizing

The transcript highlights a pervasive issue in the fashion industry: the unreliability of high street sizing. The speaker, who began tailoring at 14 and interned in Savile Row, notes that for 200 years, Savile Row exclusively catered to menswear. The emergence of women's wear tailors on Savile Row, like Phoebe's establishment, marked a shift. A common sentiment expressed by these tailors is the inadequacy of high street sizing, with customers often stating, "High street sizing is so bad. I don't think we can trust high street sizing at the moment." This inconsistency leads to a preference for judging clothing by its appearance rather than relying on numerical sizes, which can vary drastically. For instance, a size 8 in one garment might be a size 10 in another, causing frustration and a disheartening experience for consumers.

Empirical Evidence of Sizing Discrepancy

To illustrate the extent of this problem, a test was conducted with six pairs of jeans, all labeled with the same supposed size. Measurements revealed a significant variation: one pair measured 40 cm, while the smallest measured 33 cm. This represents a 7 cm difference in half the circumference, translating to a 14 cm difference in the full circumference. In fashion terms, a 4 cm difference is equivalent to a full size. Therefore, a 14 cm difference indicates a discrepancy of three and a half sizes between the largest and smallest size 28 jeans tested. This empirical data underscores the lack of standardization and the challenges consumers face in finding well-fitting garments.

Fit Collective: An AI-Powered Solution

Phoebe founded Fit Collective, a tech startup aiming to resolve the clothing sizing problem for brands through artificial intelligence. Fit Collective's unique approach involves analyzing a combination of PLM data (Product Lifecycle Management) and commercial data. This is described as the "first solution that is really comparing all of their manufacturing data... and all of their commercials." By examining how specific design elements (e.g., neckline depth, sleeve length, garment length, hem width) correlate with customer feedback, Fit Collective can identify the root causes of poor fit.

Data Integration and Analysis

The core methodology of Fit Collective involves integrating and analyzing vast datasets. This includes:

• Transactions: Identifying instances where customers purchased multiple sizes of the same item, and how they ultimately decided which to keep.
• Returns: Analyzing the reasons provided for returns, including any written commentary from customers.
• Reviews: Processing customer reviews to gauge satisfaction and identify recurring fit issues.
• Customer Service Emails: Extracting valuable insights from direct customer communication.

Artificial intelligence is employed to "read, summarize, and analyze all the feedback from customers." This allows for the distillation of complex data into concise, actionable messages for production teams.

Actionable Insights and Financial Impact

The power of Fit Collective lies in its ability to translate data analysis into concrete recommendations with significant financial implications. An example provided is: "take 5 cm off the length of this jumpsuit to reduce the returns by 4%, which is going to save you £312,000." This demonstrates the capability to distill extensive data into a single, actionable sentence that can be understood and implemented by production teams within seconds.

Growth and Adoption of Fit Collective

Fit Collective is currently collaborating with approximately 10 brands, with expectations of significant contract growth. The speaker attributes this anticipated expansion to the interconnected nature of the fashion industry, where successful solutions tend to be widely adopted. The prediction is to be working with "a 100 brands in a year's time." Several high street retailers, including Me and M Bowden and Rexo, are already testing the technology.

Case Studies and Brand Insights

The transcript provides examples of how brands are leveraging Fit Collective's insights:

• Halters: One brand identified halter necks as their "highest returning neckline type." This insight allowed them to direct their garment tech team to investigate patterns related to halter necks, exploring potential issues with specific suppliers, fabrications, or the cut of the garment.
• Bridal Wear: A bridal brand significantly altered its strategy based on Fit Collective's data. They found that their online bridal offering was "really unprofitable" and consequently shifted their focus to an "in-store experience only."
• US vs. UK Customers: The data revealed that US customers require "more fit guidance than our UK customer," prompting a strategic adjustment in how fit information is presented to different customer bases.

Perspectives on Sizing Complexity

While Fit Collective offers a data-driven solution, some perspectives within the fashion industry acknowledge the inherent complexity of sizing. It is argued that "Each designer, each retailer has its own interpretation of what the shape, the fit, the size should be." This includes preferences for different fits, such as a "more comfortable fit" versus a "tighter, more body hugging fit," which are influenced by the designer's interpretation of fabric, trends, and the season.

Global Body Shape Variations

Furthermore, the transcript touches upon the significant differences in body shapes across geographical regions. For example, UK designers who achieve success in Japan often need to "redesign their collections completely" because "the body shape in different parts of the world is different." This highlights the need for localized sizing strategies beyond a universal standard.

Conclusion and Future Outlook

Despite the complexities, Phoebe and Fit Collective remain committed to expanding their reach and impact. The ultimate "biggest um bonus" is the "really big financial impact" for their partner brands. Some brands that have been working with Fit Collective the longest have experienced a "10% reduction in their return rate since this time last year," which is considered "really fantastic," especially given that returns are generally increasing year-on-year for most brands. This significant impact underscores the power and potential of Fit Collective's AI-driven approach to revolutionize clothing sizing and improve profitability within the fashion industry.