What Is AI-Powered Visual Search? Key Components and Benefits
Effective AI in eCommerce visual search can boost sales and enhance the customer experience by streamlining user journeys. From more effective upselling and cross-selling to improved accessibility, here are all the benefits of this eCommerce search technology.
Updated January 14, 2025.
Imagine you're shopping for a specific style of lamp but don't know its name. With visual search, you can upload a photo, and the system will show you similar lamps available for purchase, streamlining your shopping journey. This is a growing trend. In fact, 36% of U.S. consumers have used visual search for shopping, and 62% of millennials prefer it over traditional text search.
» Looking for an eCommerce visual search tool? Schedule a demo with Fast Simon
Traditional Search vs. Visual Search
Visual discovery and search technology lets you use images to find products online. Instead of typing keywords, users can upload a photo or use their camera to search for similar items—extremely useful when you're unsure how to describe something or want to find products that look alike.
| Feature | Traditional Search | Visual Search |
|---|---|---|
| Search Input Method | Traditional eCommerce site search relies on text-based queries, requiring users to describe what they're looking for | Visual search allows users to upload an image or use their camera to find similar products, making it easier when exact terms are unknown |
| Search Accuracy | Text searches depend on the user's ability to describe items accurately, which can be challenging | Visual search analyzes the image's content, such as color, shape, and patterns, often leading to more precise results |
| User Experience | Standard search experience that can vary from effective with the use of synonyms and natural language processing to ineffective | Visual search offers a more intuitive and engaging experience, especially for visually-driven products like fashion and home décor |
» Learn how visual search is enhancing product discovery
Key Components of Visual Search
Image Recognition
Image recognition is the backbone of visual search, enabling systems to analyze and identify objects within images. AI-powered algorithms detect features like colors, shapes, patterns, and textures to understand what the image represents.
For example, if you upload a picture of a handbag, the system recognizes its design elements, brand style, and even materials. This process is made possible by advanced AI models like convolutional neural networks, which specialize in processing visual data.
» Interested in AI? Here's how to optimize your eCommerce site with AI
Computer Vision
Computer vision allows machines to interpret and make sense of visual inputs, mimicking human sight. In visual search, it processes uploaded images to identify details like edges, symmetry, or depth. AI enhances this by enabling systems to "learn" from millions of examples.
For instance, if a shopper uploads a picture of shoes, computer vision compares the image to a database, finding visually similar products. Tools like OpenCV and TensorFlow are often used in this field.
Natural Language Processing (NLP)
While visual search focuses on images, NLP bridges the gap when users add text inputs like tags or queries alongside images. It helps the system interpret user intentions, refine search results, and combine text and visual data for better accuracy.
Uploading a dress photo with the keyword "red" ensures the results prioritize red dresses. This fusion of vision and language understanding makes AI-driven systems smarter and more adaptable to user preferences.
» Need more help? Here's our guide to natural language search
Recommendation Engines
AI-powered product recommendation engines enhance visual search by offering personalized suggestions based on the user's history, preferences, and search patterns. These systems analyze data like past purchases or uploaded images to predict what a user might like.
For example, uploading a photo of sneakers could trigger suggestions for matching accessories. Machine learning ensures the engine continually improves, making recommendations more relevant over time.
» See our top strategies for personalized product recommendations
AI-Powered Traditional and Visual Search
Fast Simon's advanced eCommerce technology leverages the power of AI and machine learning to integrate visual and traditional search into your eCommerce store.
Personalized product recommendations
Effective natural language processing
6 Benefits of AI in Visual Search
1. More Effective Upselling and Cross-Selling
AI-powered visual search enhances cross-selling and upselling in eCommerce by analyzing product images to identify visually similar or complementary items. When a customer searches for a specific product, the system can suggest related accessories or higher-end alternatives, increasing the average order value.
Searching for a particular style of shoes may prompt recommendations for matching accessories or outfits, encouraging additional purchases through merchandising at different stages of the purchasing journey:
- Discovery stage: AI-powered visual search is especially useful when users can't accurately describe what they're looking for. By simplifying product discovery, visual search increases the time users spend exploring, leading to a significant uptick in product views—up to 48%.
- Consideration stage: During the consideration phase, visual search enhances user engagement by making product comparison seamless. It presents users with visually similar or complementary items, broadening their options.
- Purchase stage: At the purchase stage, AI-powered visual search reduces friction by quickly connecting users with the exact products they desire. By optimizing the shopping experience and minimizing obstacles to finding products, visual search not only enhances user satisfaction but also significantly improves sales figures.
» Here are some more ways to improve the customer experience with AI
Example: Fashion Nova
Fashion Nova’s visual search feature revolutionizes how customers shop. By allowing users to upload photos via a simple camera icon, the platform helps shoppers quickly find exactly what they’re looking for, even if they don’t know how to describe it.
This leads to a smoother, more personalized shopping experience. Plus, it doesn’t stop there. By showing visually similar items, Fashion Nova opens up opportunities for upselling and cross-selling.
» Need more help? See our tips for upselling and cross-selling
2. Enhanced Personalization
AI in visual search enhances eCommerce personalization by analyzing the visual features of uploaded images, such as colors, patterns, and styles, to understand a shopper's preferences. This allows eCommerce platforms to recommend products tailored to individual tastes. Uploading a photo of a dress might generate suggestions for similar styles or complementary items like shoes or bags.
This tailored personalized search approach boosts user satisfaction by simplifying the search process and making shoppers feel understood. It facilitates personalized recommendations that drive higher conversion rates.
» Discover why AI-based personalization is the future of eCommerce
Example: Princess Polly
Princess Polly’s "Shop the Look" feature makes it easy for shoppers to recreate styles they love. Instead of searching for individual pieces, customers can view entire outfits and buy everything in one click. This seamless approach of personalization in online shopping simplifies decision-making and ensures shoppers feel confident in their purchases.
By recommending complementary products, Princess Polly also increases average order value, encouraging customers to add matching items to their carts.
» Not sure how it works? Learn more about eCommerce personalization technology
Fast Simon's eCommerce Personalization and Search
AI-powered visual search and discovery
Tailored upsell and cross-sell product recommendations
Personalize the entire eCommerce journey
3. Improved Accessibility
AI-powered visual search makes eCommerce more accessible by letting users search with images instead of relying on text. This is especially helpful for people with language barriers who may struggle to describe products in the platform’s language. It also benefits users with limited ability to type (such as those with disabilities or less familiarity with technology) by allowing them to simply upload a photo.
» Learn more: How AI in visual search streamlines user journeys
4. Mobile Optimization
AI in visual search makes shopping on mobile devices effortless by allowing users to snap photos or upload images directly from their phones to find similar products. This eliminates the need for typing lengthy descriptions on small screens, making the process faster and more intuitive.
With mcommerce projected to reach 59% of total eCommerce sales in 2025, this feature is a game-changer. It streamlines discovery, speeds up the decision-making process, and ensures a seamless user journey. Shoppers can browse, compare, and purchase on the go, enhancing convenience and satisfaction while driving higher engagement and conversions for mobile-first eCommerce platforms.
Shoppers can capture images of items they see in stores, on social media, or in everyday life and instantly find similar products online. This creates a seamless connection between physical inspiration and digital convenience.
» See our top mobile eCommerce strategies
Example: Rockstar Original
Rockstar Original’s shoppable Instagram feed brings social media inspiration directly into the shopping experience. Customers can browse stylish, curated looks from Instagram and buy the exact items with ease. This not only shortens the path from inspiration to purchase but also creates a more interactive and enjoyable journey.
» Need more help? Here's how to optimize your website for mobile
6. Better Inventory Management
It also supports better inventory management for retailers, helping match customers with in-stock alternatives. This technology builds loyalty by simplifying the shopping experience, keeping users engaged, and encouraging repeat purchases.
» Want to start implementing visual search? Here's how
7. Brand Recognition and Storytelling
When considering the impact of AI-powered visual search, one often overlooked aspect is its potential to reshape how brands approach storytelling. Beyond simplifying search and boosting conversions, visual search can create deeper emotional connections with your audience.
Think of it as an opportunity to enhance your branding through storytelling and imagery. For example, when users upload images to search, the results can subtly highlight your brand's unique aesthetic or values, whether that’s sustainability, craftsmanship, or bold innovation. This adds an emotional layer to the functional utility of the search.
» Don't believe us? Here are some eCommerce brands with the best online shopping experiences
Boost Visual Search With AI Today
With the online shopping world moving towards convenience, implementing visual search in your eCommerce store is quickly becoming a necessity. Thankfully, it doesn't have to be difficult.
Fast Simon leverages AI-powered visual search to streamline eCommerce user journeys by enabling shoppers to find products using images instead of text. Users can upload photos or use their mobile cameras to search for visually similar items, simplifying product discovery and overcoming language barriers.
» Ready to get started? Book a demo today to learn more about our eCommerce search technologies