Visual Search in Ecommerce: How Shoppers Are Finding Products Without Keywords

You see a jacket on someone walking down the street. You like the shape, the color, maybe even the way it fits into the outfit. But there is one problem: you have no idea what the jacket is called.

So what do you search for?

“Brown oversized jacket” might bring thousands of results. “Casual men’s jacket” might be even less useful. The words you choose may not match the words used by the retailer at all.

Now imagine simply taking a picture of the jacket and using that image to start your search.

That is the basic idea behind visual search in ecommerce, and it is changing an important part of the shopping journey. Instead of asking shoppers to translate what they see into keywords, visual search lets the image carry part of the meaning.

The shift matters for ecommerce brands because product discovery is becoming less dependent on a single search box. A shopper can discover a product through a photograph, screenshot, social post, video frame, camera image, or another product they already own.

For merchants, that creates a practical question: if a shopper finds your product through an image instead of a keyword, is your catalog ready for that journey?

What Is Visual Search in Ecommerce?

Visual search is a way of finding information or products by using an image as the starting point. Instead of typing a description into a search engine, a shopper can upload a photograph, use a camera, select an image from their device, or interact with an image they are already viewing.

The search system then attempts to understand what is visible. Depending on the technology, that can include recognizing objects, comparing shapes, identifying colors and patterns, understanding the broader scene, or connecting visual information with product and shopping data.

For ecommerce, the interesting part is what happens after the image is understood. The shopper may be shown an exact product, similar products, different versions of the same product, retailers selling the item, or additional information that helps with the purchase decision.

Google has been investing heavily in this type of discovery through Google Lens. Google reported that Lens was handling nearly 20 billion visual searches every month, with 20% of those searches related to shopping. It has also described shopping experiences where Lens can identify products and surface information such as prices, reviews, deals, and retailers. Google’s Lens shopping announcement explains several of these use cases.

That may sound like a small change in search behavior, but for ecommerce it affects the entire path from inspiration to product page.

Why Keyword Search Does Not Always Match the Way People Shop

Traditional ecommerce search assumes that the shopper can describe the product they want. Sometimes that is true. Sometimes it is not.

Consider furniture. A shopper may see a sofa with curved arms, a low profile, textured upholstery, and a warm neutral color. They understand what they like visually, but they may not know whether the retailer calls it “modern,” “contemporary,” “boucle,” “curved,” or something else entirely.

The same thing happens in fashion. A shopper might recognize the silhouette of a dress but have no idea whether the product category is “slip dress,” “bias-cut dress,” “midi dress,” or simply “summer dress.”

Visual search removes some of that friction because the shopper can begin with the thing they already understand: its appearance.

This does not make keyword search obsolete. In fact, the two methods work better together. A shopper may upload a picture and then add “show me something under $150” or “find this in black.” The image establishes the visual direction, while text supplies the remaining constraints.

Visual Search, Image Search, and Multimodal Search Are Not the Same Thing

These terms are increasingly used together, but they describe different parts of the modern discovery experience.

For ecommerce teams, this distinction leads to a useful conclusion. You should not build one optimization strategy for text and another completely separate strategy for images. The product catalog has to support both.

How Visual Search Actually Connects a Shopper to a Product

The technology behind visual search can involve computer vision, machine learning, image embeddings, product catalogs, ranking systems, and other components. Merchants do not need to understand every technical detail to understand the ecommerce implication.

Think of the process as a chain.

Notice where the merchant’s responsibility appears in this process. It is not just the image. The product catalog sits underneath the entire experience.

Why Google Lens Matters to Ecommerce Brands

Google Lens is probably the clearest example of visual search becoming part of everyday search behavior.

A shopper can see a product in a video, on a website, in a store, or while browsing social content and use Lens to investigate it. Google has also described combining images with words so that shoppers can refine what they are looking for. A photograph can establish the visual direction, while text can add something the camera cannot know, such as budget or preferred color.

That number should not be treated as a promise of traffic for every ecommerce site. It is better understood as evidence that visual discovery is already a substantial behavior.

The practical question for merchants is therefore not whether every shopper will use visual search. It is whether the business is prepared when a shopper does.

Google’s New Multimodal Search Reporting Makes This More Measurable

One of the most useful recent developments for SEO teams is better visibility into multimodal search behavior.

In September 2026, Google announced web multimodal search performance reporting in Search Console. The reporting covers search experiences involving Lens, Circle to Search on Android, image uploads to Google Search, and Chrome’s “Search this image” feature. Google says the data can be accessed through a new multimodal search type filter in Performance reporting.

This changes how ecommerce teams can approach optimization. Instead of assuming that a particular image should perform well, marketers can look at actual search behavior, identify the product categories receiving visual discovery, and then investigate what makes those pages useful.

The First Optimization Priority: Make the Product Image Easy to Understand

This sounds obvious, yet it is one of the easiest things to get wrong in a large catalog.

A product image should show the product clearly enough that a shopper can recognize it without having to decode the photograph. The same principle is useful for visual discovery systems.

Lifestyle photography still has an important role. It helps shoppers imagine how a product fits into their lives. The problem starts when lifestyle photography becomes the only meaningful representation of the product.

For example, a bedroom photograph may look beautiful, but if the product page sells only the nightstand and the image contains a bed, lamp, rug, artwork, and several other objects, the visual relationship becomes ambiguous.

Use lifestyle images to add context. Use clear product photography to establish identity.

Product Data Is Just as Important as Product Photography

Imagine visual search identifies the right sneaker. That solves only the first part of the shopping problem.

The shopper still wants to know whether it is available in their size, what it costs, which colors exist, what it is made from, when it can arrive, and what happens if they need to return it.

That information lives in the product catalog.

This is one reason visual search should be considered part of a broader product-data strategy rather than a standalone SEO project. The same clean data can support site search, filters, recommendations, shopping feeds, marketplaces, structured data, AI shopping experiences, and merchandising.

For businesses dealing with large or fragmented catalogs, product digitalization for ecommerce can help bring product information and digital commerce operations into a more consistent structure.

Do Not Forget Image Alt Text, But Do Not Overestimate It

Alt text has an important job, particularly for accessibility. It also provides context about an image when the image itself cannot be perceived.

What it should not become is a dumping ground for keywords.

The second version describes what a person can actually see. That is the standard to aim for.

Structured Data: Useful Foundation, Not a Shortcut

Structured data gives search engines machine-readable information about a page. For ecommerce, Product structured data can communicate details such as product identity, offers, price, availability, reviews, and other supported information.

Google’s current documentation says Product markup can make eligible pages appear in product-related search experiences, including merchant listings and product snippets.

For visual ecommerce, the key word is consistency. If structured data says one thing while the visible page, image, price, or variant says something else, the implementation is not doing its job properly.

Product Variants Deserve Special Attention

Apparel, footwear, furniture, electronics, luggage, and many other products are sold in variations. Google specifically recommends ProductGroup and related properties for describing relationships between variants such as size, color, material, and pattern.

Visual Search on Shopify, Magento, WooCommerce, and Headless Commerce

Shopify

Shopify merchants should start with catalog quality before looking at advanced visual search functionality. Product photography, product attributes, variants, collections, descriptions, structured data, and internal links need to be reliable first.

Once the foundation is sound, visual search can be introduced through appropriate applications, integrations, or custom storefront development. Shopify development services can support custom ecommerce functionality when standard capabilities are not enough.

Magento and Adobe Commerce

Magento and Adobe Commerce stores often have larger catalogs, more complex attributes, configurable products, multiple storefronts, and deeper integrations. In those environments, visual search needs to be connected to the underlying product architecture.

If the visual-search index says a product is available but the inventory system says it is out of stock, the shopper experience becomes unreliable. The search layer should therefore consume dependable catalog and inventory information rather than becoming another isolated source of truth.

Magento development services can support this type of custom product discovery architecture.

WooCommerce

WooCommerce stores can take the same foundational route: improve product images, organize attributes properly, maintain structured product information, and make product pages technically accessible. Custom visual discovery can then be added where there is a clear customer benefit.

WooCommerce development services can support custom search and product-discovery integrations.

Headless Commerce

Headless architecture becomes interesting when visual discovery is a major part of the customer experience. A custom frontend can combine image upload, visual results, filters, recommendations, product comparison, and other discovery features while APIs connect the experience to commerce systems.

But headless does not automatically mean better visual search. It simply gives a business more control. The additional complexity should be justified by an actual customer or operational requirement.

Where Visual Search Can Make the Biggest Difference

Not every ecommerce category has the same visual-search opportunity. The strongest use cases tend to be products where appearance is an important part of the decision.

Visual Search Should Connect With Product Recommendations

There is a useful distinction between “find this” and “help me find something like this.”

The first is an identification problem. The second is a discovery problem.

A strong ecommerce experience should be able to handle both. If the exact product is unavailable, the shopper should not necessarily reach a dead end. The system can offer alternatives based on visual similarity, price, color, category, or other relevant attributes.

This is where visual search starts becoming more than a search feature. It becomes part of merchandising.

Common Visual Search Mistakes That Can Hurt the Experience

How to Measure Visual Search Performance

A visual search strategy should eventually answer a simple business question: does this help shoppers find useful products?

The answer should not be based only on how many people use the feature. A shopper can upload an image, receive poor results, and leave. That is activity, but it is not necessarily success.

A Practical 90-Day Roadmap for Visual Ecommerce Search

The temptation with emerging ecommerce technology is to start by buying or building the technology. For visual search, that is often the wrong first step.

Start by fixing the information that the technology would depend on.

That last step is important. A visual search strategy should become a feedback loop. If shoppers repeatedly search visually for a particular category but the results are weak, the answer may be missing attributes, poor images, incorrect variants, weak product relationships, or a problem in the search implementation.

Visual Ecommerce Search Checklist

Key Takeaways

Visual search is changing one of the oldest assumptions in ecommerce: that shoppers have to know what a product is called before they can find it.

They increasingly do not.

A photograph can now be the first step. A screenshot can be a product query. Something seen in a video can become a shopping opportunity. And an image can be combined with text to create a much more specific request.

For ecommerce brands, the answer is not to abandon keyword SEO or create a collection of visual-search hacks. The more durable approach is to make the product catalog genuinely useful to both people and machines.

That means better images. Better product attributes. Better variant relationships. Better structured data. Better technical accessibility. Better consistency between what the customer sees and what the commerce system says is available.

Google’s latest multimodal Search Console reporting is another sign that visual discovery is becoming a measurable part of the search ecosystem rather than a niche experiment.

The brands that prepare well are not necessarily the ones with the most complicated visual-search technology. They are the ones whose products are easy to understand, easy to compare, and accurately represented wherever shoppers encounter them.

For ecommerce businesses that want to build a stronger foundation for modern product discovery, Ecommerce Development Services can help connect product data, search, integrations, merchandising, and customer experience into a scalable commerce environment.