Data, research and field notes on hyper-personalised storefronts, virtual try-on, fit, and where fashion e-commerce is heading.

Agentic commerce, intent-based discovery, me-commerce, fit data as infrastructure, AEO — the shifts already visible in 2026, and what to do about each one now.
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The January returns bill is decided in November. Solving the gift-size problem, personal gifting, speed under peak load, and turning December gifts into February customers.
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The definition, the Five Levels of Personalisation ladder, and the practical path from segmented emails to a storefront that becomes the shopper.
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An honest map of the landscape — category specialists, apparel widgets, merchandising engines, and where whole-store hyper-personalisation fits. Competitors included.
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Cross-sell has always asked shoppers to imagine the outfit. What happens when every suggestion is already styled on them — one tap to try, one tap to cart.
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Chatbots tell. This one shows — recommendations, restyles, scene changes and "add it all to cart", every answer rendered on the customer's own try-on.
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Personalisation usually stops at recommendations. What happens when the imagery itself becomes the shopper — across the whole store, automatically on every new drop.
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Fit drives up to ~70% of apparel returns. How reading size from photos — and rendering at the shopper's real size — cuts them.
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Believed to be the first hyper-personalised storefront for fashion brands — what it does, why it works, and the data behind returns and fit.
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