Sizethat

Sizethat is a term used in fashion technology to describe a sizing guidance system that suggests appropriate product sizes to consumers based on measurements and brand-specific size charts. The concept encompasses methods, data practices, and interfaces that help users navigate cross-brand size variation and reduce uncertainty in online apparel shopping.

In practice, sizethat systems collect user measurements such as height, chest or bust, waist, hips, inseam, and sometimes shoulder width or sleeve length, along with fit preferences (tight, regular, relaxed). They map these inputs to size options using brand size tables, fit models, or machine-learning mappings that account for fabric stretch, garment cut, and typical wear. Some implementations permit users to create a personal fit profile that improves consistency across purchases.

Sizethat functionality is commonly integrated into e-commerce sites, mobile shopping apps, or in-store kiosks and may be offered as a standalone service or as part of a retailer's broader fit strategy. By standardizing an approach to size recommendations, sizethat aims to improve user confidence, personalize shopping experiences, and reduce return rates due to poor fit.

Challenges include variation among brands, inconsistent measurement accuracy, and privacy concerns around collecting body measurements. Because size definitions differ widely between brands and product lines, no universal sizethat standard exists. The term is used more as a description of a capability than as a formal standard.

See also: size chart, virtual fitting room, size recommendation algorithm.