Add Sizebay size recommendations and virtual try-on to Shopify and Shopify Plus PDPs, using product-level sizing data to reduce return risk and help shoppers pick the right size faster.
• Shopify product, variant, and option data is mapped to Sizebay’s sizing model, with SKUs, size labels, and attribute values aligned per product type.
• Product-level sizing inputs (for example, measurements, fit notes, and size chart references) are synced to Sizebay so recommendations resolve at PDP level, not category level.
• Sizebay widgets are rendered on Shopify PDPs via theme integration, pulling the relevant product identifier and variant context for each shopper session.
• Locale-specific and store-view differences (language, units, and sizing standards) are handled through separate mappings so the same SKU can resolve to different display rules.
• Events such as widget views, recommendation results, and virtual try-on interactions are tracked and forwarded to Sizebay for analytics and model tuning where supported.
• Sync jobs use delta logic where possible, sending only changed product and sizing records, with validation and error logging for mismatched attributes and missing identifiers.
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We install the Sizebay widget on Shopify PDPs and map it to your products, variants, and size attributes so recommendations match what you actually sell.
Typically you’ll need clean size attributes per variant plus key fit inputs per product group; we help normalize this so Sizebay can calculate consistent suggestions.
Yes, we align Sizebay rules with your variant setup (sizes, colors, regional sizing) so shoppers don’t get recommendations for out-of-stock or non-existent options.
It shouldn’t, but third-party scripts can add weight; we implement, load-test, and tune the setup to keep PDP performance stable.
Yes, we can track Sizebay interactions as events in GA4 via GTM so you can measure adoption, PDP behavior, and changes in size-related drop-offs.











