What Is Visual Search? Examples, Applications and Case Studies

Have you ever noticed a declining trend in your landing page visits? Or an increase in product impressions but no clicks?

If your customers can't make out what a certain component of your image is, their trust goes for a toss and they trace their way back from your website. The inability to define an image results in a poor user experience. high-cost acquisition per lead and disturbed marketing ROI. 

Consumers tend to judge products based on whether they visually like them or not. And sometimes they just fall for a perfect table they saw online but don't know its name. This is where visual search comes in - a technology that meets human nature.

Labeling the categories of your product images internally with image recognition software can build better visual experiences and enable website visitors to circle and search for any term or product they like. Let's learn visual search in detail. 

The visual search follows the logic of optical character recognition (OCR) and query, key, and value technique of search engine to extract features from the search query and display image results. Visual search relies on either metadata or data samples of the input image.

These engines are useful to help customers search for uncategorized components in an image and find quick answers. 

How does visual search technology work?

Visual search is a breakthrough solution that fills the gap when users struggle to describe what they see. Instead of relying on keywords, it uses images as queries — analyzing visual cues to return relevant, often shoppable, results. Whether it's a stylish table seen on Instagram or a rare sneaker spotted in the wild, visual search helps users find what words often can’t.

At its core, visual search technology is built on a combination of artificial intelligence, machine learning, and image recognition software. These systems use deep learning models, such as convolutional neural networks (CNNs). They identify objects within an image, such as shape, color, texture, and spatial patterns. The engine then compares those features against a database of indexed visuals to identify matches or near-matches.

The more images these systems process, the smarter they become. This is known as training data optimization — the more visual inputs a model sees, the more precise and context-aware it gets. Platforms like Google Lens, Pinterest Lens, and Amazon’s StyleSnap continuously retrain their models on billions of images to improve accuracy.

But it's not just about raw visuals. These engines also evaluate image metadata — alt text, file names, captions, and even schema markup to add semantic context. For example, an image of a table isn't just matched by its look; the engine also considers tags like "mid-century", "walnut finish", or "coffee table" pulled from the image’s data or page content.

This is where structured optimization makes a difference. E-commerce teams using image recognition software and AI marketing tools can label product images, embed metadata, and generate structured data to make their visuals more searchable, especially in AI-driven platforms.

So, when a user uploads a photo or takes a snap, the visual search engine processes it like this:

1. Extract visual features using deep learning
2. Cross-reference metadata to add meaning
3. Match results based on both pixel-level similarity and contextual relevance

Ultimately, visual search turns every image into a new opportunity for discovery, helping brands surface products, guide customer journeys, and increase engagement in a way that words alone can’t.

What are the best visual search engines in 2025?

It was back in 2017 when Ben Silbermann, CEO of Pinterest, said, “The future of search will be all about pictures, not keywords.” Fast-forward to 2025, and that vision is playing out across the tech landscape. Platforms like Google Lens, Pinterest Lens, and Amazon StyleSnap are leading the charge, helping users skip the guesswork of keywords and search directly with images.

These AI-powered visual search engines are no longer limited to object recognition; they now support multimodal queries, real-time personalization, and AR-enhanced product discovery. From fashion and furniture to travel and education, visual search is changing how consumers find and interact with products online.

Below is a side-by-side comparison of the top platforms using visual search technology in 2025, what they offer, who they serve, and the latest confirmed features driving innovation this year.

How do you optimize content for visual search?

Content is king. You’ve probably heard that a million times. But actually, visual content is the true ruler. According to a recent report, 56.2% of marketers said that visual content plays an important part in their marketing strategy. Visual search is about structuring your visual assets so that AI systems, search engines, and discovery platforms can understand, rank, and serve them at the right time to the right audience.

While creating your content strategy, you shouldn’t forget about its less amusing but crucial part, optimizing your visuals for better search results. Here’s your visual search SEO playbook for 2025.

1. Name your images like keywords matter (because they do)

The image title plays an SEO role similar to blog post titles, and as in the case of blog post titles, it should contain a relevant keyword. It’s best to use either long-tail or location-based keywords. Long-tail keywords help your website’s ranking in Google. The file name is one of the first signals a visual search engine sees. A generic filename like IMG_7234.jpg says nothing. A clear, keyword-rich filename tells Google and Pinterest what the image is about before they even crawl the image data.

Why it matters: File names are used in image indexing, Google Lens analysis, and mobile-first search previews. Especially important for product images.

How to implement:

• Use hyphen-separated, descriptive names
• Include long-tail keywords or location indicators
• Match naming conventions across product variants

Example:

2. Write alt text with both AI and accessibility in mind

In simple words, alt text is what will replace your image in search results if, for example, a user’s internet connection is too weak to upload images. But alt text is more than that. Search engines use it to understand what your image is and how to rank it via SEO meta tags. Alt text ("alternative text") describes the image content to screen readers and search engines alike. It's mandatory for accessibility — but it's also critical for visual search engines parsing image meaning. So, apart from your target keywords, try to use these 125 characters to describe the image as accurately as possible. 

Why it matters: Alt text feeds directly into how platforms like Google Images, Pinterest, and Amazon associate your image with intent-based queries. It's also used in AI Overviews and generative previews.

How to implement:

• Write concise, specific descriptions (under 125 characters)
• Include relevant keywords naturally
• Avoid keyword stuffing or vague phrases

Example:
Do's: “Mid-century velvet blue accent chair with gold legs”
Don'ts: “Chair product modern furniture buy now cheap”

3. Descriptions

If the content management platform you use allows image descriptions, make sure to fill it in. Descriptions give you space to provide context for the image and further insights into what you’re offering. It should be compelling and consistent with your brand image. And include your primary and secondary keyword(s) for better crawlability and targeting. 

Why it matters: Structured data helps search engines understand, index, and feature your images in rich snippets and AI-generated results.

How to implement:

• Use JSON-LD markup (Google’s preferred format)
• Wrap product pages in Product schema with image fields
• Use ImageObject schema for standalone visuals
• Include details like caption, license, and contentUrl

Example: 

Pro Tip:
After adding schema markup, run your URL through Google’s Rich Results Test to make sure it's eligible for visual-rich search results and AI Overview inclusion.

4. Formatting

Both size and format are crucial factors that affect website speed and can make your content load very slowly or not at all. It’s usually best to go for JPEGs; this format is small in size and does not compromise image quality. Use PNGs for images with transparent backgrounds. Keep in mind that image dimensions should not exceed the average desktop screen resolution, which is up to 2560 pixels in width in most cases.

These are the general, universal guidelines, but it is also important to optimize your content for specific media outlets. Before you even start to create your content, make sure it meets the guidelines of your target medium.

For example, on Pinterest, the ideal pin size is 600 x 900 pixels. When posting to social media, always look up recommended image sizes and remember to fit the style and contents of your visuals to every individual platform's audience.

Why it matters:
Google penalizes slow-loading pages. Visual search platforms also deprioritize heavy or unresponsive visuals in SERPs and AI modules.

How to implement:

• Use compressed WebP or AVIF formats over JPEG/PNG
• Serve images through a CDN
• Implement lazy loading (loading="lazy")
• Predefine width and height to prevent layout shift
• Keep the image file size ideally below 150 KB without quality loss

Example:
Do's: furniture-product.webp (compressed to 88KB)
Don'ts: furniture-product.png (uncompressed at 1.2MB)

Why are marketers interested in visual search?

Apart from the obvious benefit of optimizing for image search and better organic visibility, marketers have many reasons to be interested in the technology.

1. Providing experiences through content

Following the example of recent buzz around OpenVerse and its creative common search to diversify knowledge and build ethical standards, visual search gives omni-sensory experiences for people to make the most of virtual worlds. You can also go for a long-term collaboration and incorporate AI tools into your offer. For example, if you’re in the fashion industry, you may think about partnering with tools such as Canto that generate outfit recommendations based on what your customers are interested in.

Finding your niche

Look for apps and websites dedicated to your niche. For example, if you’re in the furniture industry, look into adding your product content to the virtual design program of Living Spaces, a visual designer tool that allows people to use their product base in visual projects.

Content creation

Once you research trends using visual search, use your insights to reverse engineer the content creation process. Research-driven content creation is key to getting your products in front of valuable audiences – trends and competitive analysis will help you pin down the best practices.

E-commerce case studies: ROI from visual search adoption

As visual search becomes more embedded into shopping journeys, forward-thinking e-commerce brands aren’t just experimenting with it; they’re reporting real ROI. From higher conversion rates to lower bounce rates, these companies are proving that image-driven discovery isn’t a novelty; it’s a revenue driver.

Here are three real-world case studies showing how leading e-commerce brands have implemented visual search technology and what they’ve gained from it.

1. IKEA: Empowering DIY interior design with visual discovery

Challenge: IKEA needed a way to bridge the gap between showroom inspiration and real-world room planning.

Solution: IKEA launched IKEA Kreativ, an AI- and AR-powered visual search and room planning tool. Shoppers can scan their actual rooms using their phones, erase existing furniture, and drop in IKEA products in real scale. The app then offers personalized product suggestions based on visual cues.

ROI Impact:

• 2x longer session durations in app
• Higher add-to-cart rates on visually recommended products
• Improved engagement from mobile-first Gen Z users

IKEA combines visual search + augmented reality to create a highly personalized, conversion-focused mobile shopping experience.

2. Sephora: Visual AI meets personalized beauty

Challenge: Makeup customers often struggled to find the right products online without trying them in person.

Solution: Sephora implemented the visual artist — a selfie-based virtual try-on feature powered by image recognition and AI. Shoppers can scan their faces, receive shade matches, and explore product recommendations through a visual interface.

ROI Impact:

• 80% increase in conversion rates for try-on users
• Over 200 million shades matched since launch
• Reduced product returns due to better shade accuracy

Sephora’s success shows how AI-powered visual personalization can increase both user satisfaction and revenue.

3. ASOS: Streamlining fashion search for mobile shoppers

Challenge: ASOS wanted to help fashion shoppers quickly find similar styles from uploaded images or social screenshots.

Solution: The brand introduced style match in its mobile app, letting users upload any outfit photo to instantly find visually similar items from its catalog. The tool uses deep learning-based feature matching across apparel categories.

ROI Impact:

• 35% faster product discovery time
• Increased conversion rates among Gen Z shoppers
• Visual search queries now account for over 10% of app-based purchases

ASOS uses visual search to reduce friction in mobile fashion discovery, tapping into social-first behaviors.

Frequently asked questions about visual search

Got more questions? Get your answers here

1. What are examples of visual search engines?

Leading visual search engines include Google Lens, Pinterest Lens, Amazon StyleSnap, eBay Image Search, IKEA Kreativ, Sephora’s Virtual Artist, and ASOS Style Match. These tools let users search using images to find similar products, identify objects, or explore styles — without needing keywords.

2. How do you optimize images for visual search?

Optimize images by using keyword-rich filenames, writing clear alt text, and applying structured data with schema markup. Use fast-loading formats like WebP, compress file sizes, and submit image sitemaps. These steps help AI systems index and rank your visuals effectively.

3. Why is visual search important for e-commerce?

Visual search helps shoppers find products instantly using photos, not keywords. It improves product discovery, increases conversions, and aligns with mobile-first behavior. For retailers, it creates faster, more intuitive buying journeys and boosts engagement.

4. What companies are using visual search successfully?

Brands like IKEA, Sephora, and ASOS use visual search to personalize shopping. IKEA’s Kreativ app offers AR room scans, Sephora enables virtual makeup try-ons, and ASOS helps users match outfits from images — all driving engagement and ROI.

5. What are the benefits of visual search for marketers?

Visual search attracts high-intent traffic, enhances mobile UX, and shortens the path to purchase. It supports product discovery, fuels personalization, and unlocks new visibility in visual SERPs when images are properly optimized.

6. What are the challenges or risks of visual search?

Key challenges include AI bias, missed SEO opportunities due to poor metadata, and legal risks from copyright misuse. There are also privacy concerns around user-uploaded content. Ethical design and strong tagging help mitigate these issues.

7. How does visual search impact SEO strategy?

Visual search expands SEO to include image optimization, structured data, and page speed. Alt text, filenames, schema, and image sitemaps all become ranking factors — especially as AI Overview and multimodal search gain traction.

8. What are the top visual search trends in 2025?

Top trends include multimodal search (image + text + voice), AR-powered shopping, mobile camera-based discovery, and eco-conscious search features. Platforms are surfacing visual content more often via AI Overviews and personalized feeds.

Ok Google, show me sling bags like this one

Visual search is empowering B2C and B2B brands to deploy computer vision capabilities to replicate human sentiments and suggest personalized products to their end audiences. The vision is being refined, and customer-centric content is turning out to be a prioritized focus for brands that want to genuinely invest in brand and consumer growth. 

Become more adept at using VR visualization software tools to bring visual search to the forefront of your marketing strategy today.

This article was originally published in 2020 and has been updated with new information.