Confident Size Recommendations, Powered by Body Data
Size recommendations match an individual’s body measurements to a brand or organization’s size chart, generating an accurate, automated fit decision. This approach replaces self-reported sizing and manual estimation with objective, repeatable data that applies consistently across retail, uniform programs, and workforce apparel programs.
Accurate size recommendations depend on precise body data, and Size Stream provides the foundation that makes measurements reliable at scale. Individual measurements are matched automatically to brand or organization-specific size charts, replacing guesswork with consistent, data-driven fit decisions. Both consumer-facing experiences and internal sizing workflows are supported. Sizing logic is fully configurable by brand, product category, or organizational policy.
From Body Measurements to Automated Size Recommendations
Ready-to-wear sizing with Size Stream begins with objective body data. Individual body measurements are mapped directly to predefined size charts, producing automated size recommendations that are consistent and repeatable across every user and session.
- 240+ body measurements mapped to standard or custom size charts
- Numeric, alpha, and hybrid sizing systems all supported
- Size logic configurable by brand, product category, or organization
- Consistent recommendations across every user, session, and channel
This approach bridges the gap between how bodies are measured and how products are sized, with no manual estimation required.
Clothing Fit Solutions Built for Organizational Consistency
Unlike consumer-only sizing tools, Size Stream is built to support organizational standards and policies. The same sizing logic applies across every location, channel, and operator; no drift between what one store recommends and what another recommends, and no reliance on individual staff judgment.
- Consistent sizing rules applied across all locations and channels
- Size recommendations aligned with approved organizational size charts
- Repeatable, auditable sizing decisions independent of operator skill
- Reduces returns in-store and online
This makes Size Stream well-suited for any environment where consistency and accountability in sizing decisions matter, from fashion retail to large-scale uniform programs.
Size Recommendations Across Every Sector
Size Stream’s ready-to-wear platform is deployed across a wide range of organizations, with the same underlying approach in every case: body measurements feed directly into a size recommendation engine, producing a data-driven result without manual estimation.
- Retail and e-commerce apparel brands
- Uniform and outfitting programs for public and private sector employers
- Educational institutions and government organizations managing apparel distribution
- Enterprise businesses managing large-scale uniform or workwear programs
Deployment: Consumer, Internal, and SDK
Size Stream’s size recommendation platform deploys in multiple configurations, with no changes required to how products are manufactured or labeled. It operates as a data layer that enriches existing workflows rather than replacing them.
Consumer-Facing
Size recommendations surfaced at the point of purchase. Reduces returns by replacing self-selection with data-driven fit decisions.
Internal Tool
Staff or administrator-facing sizing for bulk assignment. Supports uniform programs, outfitting workflows, and enterprise apparel distribution.
SDK & APIs
Embeds size recommendation logic directly into existing platforms, product catalogs, and digital workflows.
A Practical Foundation for Better Fit
Ready-to-wear sizing is often the first step toward improved fit confidence and reduced returns. Size Stream gives organizations the measurement foundation to make that step reliable and repeatable: body data replaces guesswork, and size chart mapping replaces manual estimation, at any scale.
Frequently Asked Questions
How do size recommendations work?
Size recommendations work by matching an individual’s body measurements against a predefined size chart. The individual is measured through a body scan or other measurement capture method. Those measurements are compared against the size ranges defined in a brand or organization’s size chart, and the closest match is returned as the recommended size. Size Stream automates this process end to end, removing manual estimation and producing consistent results at any scale.
How does body measurement reduce clothing returns?
Body measurement reduces returns by replacing size self-selection with data-driven recommendations. Most size-related returns happen because shoppers estimate their size from past experience or generic guides, both of which produce inconsistent results across brands and fits. When a recommendation is generated from actual body measurements matched to a specific brand’s size chart, the fit is more likely to be correct on the first order. For published data on apparel ecommerce return rates, see Shopify’s ecommerce returns research.
What are best practices to reduce ecommerce returns?
The most effective approach is providing accurate size recommendations based on actual body measurements, not generic size guides or self-reported sizing. Additional best practices include: brand-specific size chart mapping instead of universal standards; capturing measurement data before purchase to reduce the chance of a mismatch; and tracking return rates by size and product to identify fit gaps in existing size charts. Size Stream’s platform supports all of these practices through a single, integrated size recommendation engine.
Talk to our team about 3D body scanning, fit, and body composition.