{"id":4323620,"date":"2025-05-28T18:11:34","date_gmt":"2025-05-28T16:11:34","guid":{"rendered":"https:\/\/www.deptagency.com\/insight\/how-googles-try-it-on-changes-shopping-yet-again\/"},"modified":"2025-08-08T16:21:21","modified_gmt":"2025-08-08T14:21:21","slug":"how-googles-try-it-on-changes-shopping-yet-again","status":"publish","type":"article","link":"https:\/\/www.deptagency.com\/en-dk\/insight\/how-googles-try-it-on-changes-shopping-yet-again\/","title":{"rendered":"How Google\u2019s \u201ctry it on\u201d changes shopping yet again\u00a0"},"content":{"rendered":"
\n\t\n\t\t\n\t\t\t<\/mask><\/g><\/svg>\t\t\t\n\t\t\t\tInsights\t\t\t<\/span>\n\t\t<\/div>\n\t<\/div>\n\tHow Google\u2019s \u201ctry it on\u201d changes shopping yet again\u00a0<\/h1>
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\n\t\tWesley Valstar\t\t
\n\t\tE-commerce & Transformation Director\n\t\t\t<\/div>\n<\/div>\n\t
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\n\t\t\t\t\n\t\t\t\t\tDate\t\t\t\t<\/span>\n\t\t\t\t\n\t\t\t\t\tMay 28, 2025\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n<\/div>\n\n\n\tAt Google I\/O 2025, search officially became a stylist.<\/h2>\n\tWith AI-powered overviews, personalized product suggestions, and a new \u201cTry It On\u201d feature<\/a> that lets shoppers see clothing on their own body with a simple upload, Google has re-arranged the traditional customer journey once again.\u00a0

Discovery, comparison, and decision-making can now happen in one, AI-generated moment. Less browsing. Less site hopping. Just a styled suggestion and a swipe.

For brands, this isn\u2019t a feature drop. It\u2019s a fundamental shift in how people shop\u2014and how your brand needs to show up.<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tThe era of curated commerce<\/h2>\n\tHistorically, brands fought to rank<\/em>, get found, win the click. But that era is fading fast. In this new AI-mediated experience, the question isn\u2019t \u201cWill they find us?\u201d It\u2019s \u201cWill we be recommended?\u201d

Google\u2019s AI is no longer just indexing product pages. It\u2019s interpreting taste and styling products. It\u2019s surfacing what it believes users will like in the moment. And it\u2019s doing that using your brand\u2019s assets (such as images, copy, and metadata) as fuel.

The battleground has shifted from SEO to GEO<\/a> to suggestions. And the assets you produce today will train the model that decides tomorrow.<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tFrom storefront to endless interface<\/h2>\n\tWhere does your storefront start, and where does it end?

Increasingly, the answer is: wherever the shopper is.\u00a0

With features like Google\u2019s \u201cBuy for Me,\u201d which tracks prices, compares products, and can even complete checkouts on your behalf, the storefront is dissolving into a network of agent-driven moments. What used to be a linear journey (click an ad, land on a product detail page, orPDP, and convert) is now a distributed choreography orchestrated by AI.

At DEPT\u00ae, we refer to this shift as \u201cEndless Interfaces:\u201d a new commerce paradigm where transactions are embedded everywhere, and the brand experience is reconstructed in places you don\u2019t control. TikTok. WhatsApp. Google Search. Your storefront is being transformed, and your product data, content, and user experience (UX) need to keep pace.

In this landscape, the role of the PDP is extended, not erased. It must act like an API for your brand: modular, machine-readable, and expressive enough to fuel recommendations and autonomous agents. Because, increasingly, shoppers won\u2019t browse\u2014they\u2019ll ask an assistant to decide for them.

If you\u2019re only optimising for your own domain, you\u2019re already behind the curve.<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tDesigning for the style, not the scroll<\/h2>\n\tIn this new landscape, brands are being styled into<\/em> someone\u2019s world.

A well-shot campaign photo is both a creative win and a strategic input. A lookbook becomes machine-readable proof of how your product fits into a lifestyle. Flat lays, 360s, UGC, and model diversity suddenly matter more, not because they impress a shopper directly, but because they feed the model a richer understanding of who your product is for.

It\u2019s no longer enough for your content to be beautiful. It has to be describable<\/em> by a machine.<\/p><\/div>\n\t\t\t\t\t\n\t\t\t\n\t<\/div>\n\n\n\tWhat does this mean for brands?<\/h2>\n\tWhen Google sets a new standard for shopping UX, it can win user trust and also establish a benchmark for others to follow.\u00a0

Soon, shoppers won\u2019t just appreciate immersive try-on or personalised suggestions, they\u2019ll begin to expect it. And if Google can deliver that upstream, they\u2019ll wonder why your site can\u2019t deliver it downstream. That raises important questions: Should you partner with try-on technology providers? Can Google\u2019s tech be extended into your owned experiences? Are your current content and data infrastructures ready?

There\u2019s still hesitation in the industry, and rightly so. Factors including fit, fabric nuance, and brand identity matter. Some e-comm leaders have questioned whether virtual try-on is just a gimmick.

But the conversation has changed, because this isn\u2019t about jumping on a trend. We’re entering a time when styling, fitting, and buying are no longer distinct phases. They\u2019re simultaneous, and often mediated by machines.

So yes, fidelity matters. But so does being present in the experience at all. The brands that win won\u2019t be the ones with the most perfect simulation. They\u2019ll be the ones who show up early, learn fast, and make their content work across both human and AI touchpoints.<\/p><\/div>\n\t\n\t<\/div>\n\n\n\tHow to leverage Google\u2019s \u201cTry it on\u201d feature\u00a0<\/p>\n\tInvest in accurate, detailed 3D product data.<\/strong> The foundation of any virtual try-on experience is a high-fidelity 3D model of your products. Creating a pretty digital copy isn’t enough. You need to capture:<\/p>\n\n\t
  • Precise dimensions (lengths, widths, circumference)<\/li>
  • Fabric properties such as stretch, weight, and drape<\/li>
  • Construction details that affect fit (seams, cuts, closures)<\/li><\/ul>\n\nStandardize & enhance your size and fit metadata.<\/strong> Every brand\u2019s sizing is unique. A medium on one label might feel like a small or large on another. AI \u201cTry It On\u201d features rely heavily on metadata to understand these nuances. You should:<\/p>\n\n\n\t
  • Maintain detailed, accurate size charts and garment measurements<\/li>
  • Aggregate and expose \u201ctrue-to-size\u201d ratings and customer fit feedback<\/li>
  • Use standardized terminology and tagging so AI can interpret data consistently<\/li><\/ul>\n\nLeverage customer fit feedback as AI training data<\/strong>. User reviews and returns data hold a treasure trove of insights into fit accuracy. Feeding this data back into AI models helps improve size recommendations and virtual try-on accuracy.<\/p>\n\n\n\t
  • Collect structured feedback about fit and comfort (e.g., \u201cRuns small,\u201d \u201cPerfect fit\u201d)<\/li>
  • Monitor return reasons related to sizing and fit<\/li>
  • Partner with AI providers to anonymize and utilize this data for training algorithms<\/li><\/ul>\n\nPrepare for new creative workflows<\/strong>. Producing assets for AI styling and virtual try-on requires new workflows and collaboration between product design, marketing, and technology teams. This includes:<\/p>\n\n\n\t
  • Creating modular, machine-readable content assets<\/li>
  • Regularly updating 3D product scans as inventory or designs evolve<\/li>
  • Training teams on metadata standards and AI capabilities<\/li><\/ul>\n\n\n<\/div>\n\t\n\t<\/div>\n\n\n\tAI is no longer simply powering search, it\u2019s powering style. The brands that adapt their commerce experiences<\/a> won\u2019t just be found. They\u2019ll be favoured.<\/p>\n\t\n\t<\/div>\n\n
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