My Size, Inc. (MYSZ)
Unlike traditional garment-size charts that force bodies into fixed categories, My Size, Inc. (MYSZ) operates at the intersection of computer vision and fashion logistics—capturing precise human dimensions and translating them into fit predictions across thousands of SKUs. Where conventional retailers treat sizing as a static problem (offering S/M/L/XL), My Size competes by treating it as a data and measurement problem solvable through algorithmic matching.
The Return Problem the Company Solves
Apparel returns driven by poor fit cost online retailers anywhere from 15% to 40% of order value. A customer buys three sizes of a shirt hoping one fits; two get returned. My Size addresses this by providing apparel retailers and e-commerce platforms with body-measurement APIs that feed into their checkout and recommendation flows. A shopper can use the company’s smartphone app or web-based scanning tool to capture their body dimensions; the system then maps those measurements against that specific retailer’s garment data and predicts which size will fit best. The economic logic is straightforward: fewer wrong-size returns mean higher margins for the retailer, faster inventory turns, and reduced logistics overhead.
The company’s competitive position rests on two technical capabilities. First, 3D measurement from single images or video, which requires computer vision and body-model training to infer dimension from ordinary smartphone cameras. Second, garment-database construction and matching—building and maintaining SKU-specific fit data that translates human measurements into predicted fits. Neither is trivial at scale, and both improve with data. My Size’s differentiation against direct competitors lies in its focus on the end-to-end retailer integration challenge: not just providing measurements, but embedding the solution into existing checkout workflows and recommendation engines.
How Retailers Adopt the Platform
My Size’s revenue model centers on per-transaction fees or subscription licensing to apparel retailers and marketplaces. The company must convince often-conservative fashion and mass-market retailers to embed a new technology into their user journey. Adoption requires API integration, training on how to use the system, and internal buy-in from merchandising teams. This makes My Size’s sales process enterprise-oriented despite its consumer-facing product: deals move slowly, implementation takes months, and volume scales gradually as partners roll out the feature to more SKUs or user segments.
The company’s geographic exposure is global—US e-commerce giants represent the largest opportunity, but European and Asian apparel and logistics companies also license the technology. This geographic breadth provides revenue diversification but also exposes My Size to currency fluctuation and varying regulatory approaches to biometric data (body measurements, though not facial or fingerprint data, sit in regulatory gray zones in some markets).
Data Moat and Competitive Friction
Each garment fit prediction My Size makes feeds back into its models. Over time, actual customer purchase and return behavior refines the algorithm’s accuracy. Retailers that use My Size intensively generate rich feedback loops—millions of body scans paired with purchases and returns—that competitors must laboriously replicate. This flywheel effect is My Size’s primary moat: a competitor starting today would need to rebuild the training data, recruit retailers willing to switch, and prove equivalent accuracy before gaining traction.
However, the moat is constructible, not impenetrable. Large apparel manufacturers could build fit-matching in-house. Retailers dissatisfied with My Size could exit and use a competitor. And the underlying 3D computer vision and body-model technology is not proprietary to My Size—it is grounded in open-source and academic research that any competent team can access. The company’s defensibility depends on its execution staying ahead of its costs and on continued retailer adoption.
The Operating Reality
Most of My Size’s revenue derives from retailers who use the platform actively—those that integrate it into their core checkout or recommendation flows. A fraction of the user base consists of one-off users or casual adopters who scan themselves out of curiosity. The company must manage a freemium or tiered model to capture as much retail API traffic as possible while allowing individual consumers to explore the service. This splits the business into two distinct challenges: acquiring and retaining paying corporate partners, and managing the cost of consumer-facing scanning infrastructure without cannibalizing retailer margins.
My Size’s gross margins depend on the cost of hosting 3D scanning servers, maintaining the computer vision models, and funding customer support and integration work. Scaling the business profitably requires achieving high retailer utilization—many transactions per retailer per month—before fixed costs overwhelm the unit economics.
Capital and Runway
As an earlier-stage public company, My Size relies on stock offerings and debt to fund ongoing R&D and sales operations. The company operates in a competitive space where technology and network effects matter, but profitability typically lags revenue growth. Investors must evaluate whether the company can reach scale—hundreds of major retailers using the platform, billions of scans per year—before cash runs out or capital markets close. The presence of well-capitalized competitors and the possibility of retailers building in-house alternatives add pressure.