7 Ways AI in Visual Search Streamlines eCommerce User Journeys

AI-powered visual search gives eCommerce platforms a more intuitive and efficient shopping experience. By overcoming challenges and optimizing product images, your business can unlock its full potential and stay competitive.

a woman with glasses sitting in front of a wall
By Arjel Vajvoda
a woman taking a selfie with her cell phone
Edited by Kim Weidemann
Oli Kashti - Writer and Fact-Checker for Fast Simon
Fact-check by Oli Kashti

Updated November 19, 2024.

eCommerce merchant sitting at a table with a laptop holding a magnifying glass surrounded by empty boxes

AI visual site search is increasingly important for eCommerce platforms, enhancing user experience and product discovery. Shoppers can upload images to find exact or similar products, bypassing text-based searches that require specific keywords. This leads to higher engagement and conversion rates.

According to Gartner, early adopters of visual search can increase their digital commerce revenue by 30%. Another study by ViSenze found that 62% of millennials prefer visual search over other technologies.

» Discover how Fast Simon can help you boost conversions with our advanced AI-powered eCommerce tools

Meet the Expert

Arjel Vajvoda, an experienced leader in the tech industry, is currently the Head of Product and Customer Success at Motomtech. In this role, she drives the development of innovative SaaS solutions designed to address the changing needs of clients.

AI-powered visual search enables shoppers to find products online by uploading images instead of typing keywords. Using AI and computer vision, the search algorithm analyzes visual elements like color, shape, and patterns to match or suggest similar items from an eCommerce catalog.

AI visual search makes product discovery more intuitive. As users upload images, the system quickly identifies and matches the items, improving the shopping experience and conversion rates.

» Optimize your eCommerce store design to boost visual search results



7 Ways AI in Visual Search Streamlines eCommerce User Journeys

a diagram of the seven ways to use visual search


1. Simplifies Product Discovery

Visual search allows users to find products by uploading images, making the search process more intuitive. According to industry research, retailers who implement visual search significantly increase their customer engagement and streamline the shopping experience.

For example, photographing a friend's jacket can help you find the same or similar styles online.

» Discover the difference between product search and product discovery

2. Overcomes Language Barriers

Visual search also eliminates the need for precise keywords or product names. This helps users who may not know the exact terminology or who speak different languages, making the shopping experience more accessible and user-friendly across diverse customer bases.

» Learn more about language variances in our natural language search guide

3. Enhances Personalization

AI algorithms analyze visual inputs to understand user preferences in style, color, and design. This enables platforms to provide personalized product recommendations, increasing the likelihood of a purchase. Studies have also shown that personalization will lead to increased sales and customer loyalty.

» Discover the benefits of AI-based personalization

4. Reduces Search Time

Visual search expedites the shopping process by quickly matching user-uploaded images with products in the catalog, reducing the time spent searching. This efficiency can lead to higher conversion rates and improved customer satisfaction, as shoppers find what they're looking for more quickly.

5. Improves Mobile Shopping Experience

On mobile devices, typing can be a chore. Visual search allows users to snap a photo and search instantly, making it easier to shop. Given the significant growth in mobile commerce, this feature is increasingly important for reaching customers who primarily shop on their phones.

» Don't forget to optimize your website for mobile

6. Enables Inspiration-Based Shopping

Shoppers often seek products inspired by things they see in everyday life. Visual search lets users capture inspiration from their surroundings and easily find similar products online, bridging the gap between offline inspiration and online purchasing.

7. Facilitates Cross-Selling and Upselling

By incorporating advanced visual merchandising techniques, eCommerce platforms can suggest complementary or higher-end products based on visual similarities. For example, if a user searches for a specific handbag, the system can recommend matching accessories or premium alternatives, potentially increasing the average order value and overall sales.

» Here are our tips for cross-selling and upselling



Examples of How AI Visual Search Improves Shopping Across Categories

  • Fashion: A customer can upload a picture of a dress from social media. The AI will suggest similar dresses available on the platform, making it easier to find the desired item. It helps if you've invested time into merchandising for fashion eCommerce.
  • Home décor: A user can upload a photo of a stylish living room, and the AI will match the furniture and décor pieces, helping customers recreate the look.
  • Electronics: Customers can upload an image of a gadget, and the AI will either match it to the exact model or suggest similar products with comparable features, reducing the need for extensive research.


Recent Advancements in AI Visual Search Technology

Recent advancements in AI and computer vision have significantly enhanced visual capabilities for eCommerce. Technologies like Vision Transformers (ViTs) and Contrastive Language-Image Pretraining (CLIP) improve image recognition and feature extraction, allowing for more accurate matching of user-uploaded images with product catalogs. These improvements in real-time image processing and algorithms lead to faster, more relevant search results.

» Find out why your store needs visual discovery

Core Components of AI-Powered Visual Search Systems in eCommerce

Image Input Interface

A user-friendly feature allowing customers to upload or capture images of products they like. This interface can be integrated into mobile apps or websites, such as those built on Shopify and Magento, enabling shoppers to search with just a photo.

Image Preprocessing Module

This component processes uploaded images to enhance quality and standardize them for analysis. It performs tasks like resizing, noise reduction, and normalization.

Preparing the images ensures that the subsequent feature extraction is accurate and reliable, leading to better search results.

Feature Extraction Engine

This component analyzes images using AI and deep learning algorithms to identify and extract key visual features like color, shape, texture, and patterns. Techniques like convolutional neural networks help create a unique numerical representation of each image, enabling precise comparison with other images in the database.

Product Image Database With Metadata

This is a database of product images from the eCommerce catalog, each tagged with detailed metadata like brand, category, and attributes. This database serves as the reference point for matching. By having rich metadata, the system can provide more relevant search results that align with user preferences.

Similarity Matching Algorithm

This component is an algorithm that compares the feature vector of the uploaded image with those in the product database to find the most visually similar items. They’re used to rank products based on likeness, ensuring the accuracy of search results.

Results Presentation Interface

This is an intuitive display system that showcases the matched products to the user. It includes sorting and filtering options, high-quality images, and detailed product information. A well-designed interface not only makes it easy for users to browse results but also encourages engagement and increases the chances of buying.




Steps for Implementing AI Visual Search in eCommerce

1. Assess Your Business Needs and Set Clear Objectives

Begin by identifying what you aim to achieve, such as enhancing user experience, increasing conversion rates, or understanding consumer behavior.

Understand your target audience and how they interact with your platform. This step will ensure that the implementation aligns with your overall business strategy. For example, a small boutique may focus on improving product discovery, while a large retailer will aim for personalized shopping experiences across extensive inventories.

» Read about ways eCommerce personalization helps your business

2. Choose the Right Technology Solution

Select a visual search technology that fits your business size, budget, and technical capabilities. Some options include developing an in-house solution, partnering with a specialized provider, or using open-source platforms. You should also consider factors like scalability, integration ease, and support services.

Small businesses usually opt for third-party solutions with quick deployment, while larger companies invest in custom development for greater control. Make sure that the technology employs advanced AI algorithms for accurate and efficient search results.

a screenshot of a web page with a number of items on it


3. Prepare High-Quality Product Images and Metadata

Gather high-resolution images of your products from multiple angles to improve the accuracy of search results. Enrich your product catalog with detailed metadata, including product descriptions, tags, and attributes. This enhances the AI's ability to match user-uploaded images with your products effectively.

an image of a man in a green shirt


4. Integrate the Visual Search System Into Your Platform

Implement your chosen visual search technology into your eCommerce platform, and ensure its compatibility with existing systems like inventory management and customer databases. Use APIs or plugins provided by the technology vendor for seamless integration.

Focus on creating an intuitive user interface that allows customers to upload images and receive results easily. Both small and large businesses should prioritize user experience to maximize adoption and customer satisfaction.

5. Test, Optimize, and Launch the Feature

Conduct thorough testing to ensure that the system delivers accurate and relevant results. Gather feedback from a sample of users to identify any issues or areas for improvement. Then, based on this feedback, optimize the system, paying attention to response times and result accuracy.

Once refined, launch the visual search feature to your entire customer base. Continuously monitoring post-launch is essential to address any emerging issues promptly and to make iterative improvements.

a screen shot of a web page with a bunch of items on it


6. Promote and Educate Customers About the New Feature

Through marketing campaigns, tutorials, and on-site notifications, inform your customers about the visual search capability and how to use it to enhance their shopping experience.

For example, provide step-by-step guides or demo videos demonstrating how uploading a photo can help find desired products quickly. Increasing awareness leads to higher adoption rates, maximizing the return on your investment.

an image of a woman's jeans on a blue background


Challenges & Solutions of Implementing AI Visual Search

Data Quality and Image Consistency

  • Challenge: Many eCommerce businesses face issues with inconsistent or low-quality product images, which can lead to poor search results. The AI struggles to accurately match user-uploaded images with the available products when images lack standardization or clarity.
  • Solution: To overcome this, businesses should invest in high-resolution, standardized images taken from multiple angles. Using consistent backgrounds and adding detailed metadata, such as color, size, and style, will further enhance the AI's ability to match and recommend products effectively.

Scalability and Cost

  • Challenge: Implementing AI-powered visual search can be costly, especially for small businesses with limited budgets. Scalability also becomes a challenge as the product catalog grows. Large companies may struggle to efficiently manage vast inventories without straining their resources.
  • Solution: Businesses can start with flexible, scalable AI solutions that fit their size and budget. Small companies can opt for third-party solutions with pay-per-use models, while larger companies may invest in custom-built AI systems to handle extensive catalogs and traffic volumes. Selecting the right technology ensures the system grows alongside the business.

User Adoption and Education

  • Challenge: Even with advanced features, some customers may be unfamiliar with visual search technology or unaware of its benefits. Without clear guidance, users are less likely to adopt the feature, limiting its effectiveness.
  • Solution: To drive adoption, businesses should actively promote the visual search feature through tutorials, demo videos, and on-site notifications. Highlighting how visual search simplifies and enhances the shopping experience can spark interest.


» How to overcome challenges in visual merchandising like an expert

1. Use High-Resolution Images

Use clear, high-resolution images to ensure the AI can analyze fine details such as color, texture, and patterns for more accurate search results.

a pair of black and blue shirts on a purple background


2. Provide Multiple Angles

Provide images from multiple angles (front, side, back) to give the AI a full view of the product, improving the accuracy of visual matches.

a pair of black shoes on a white background


3. Maintain Consistent Backgrounds

Use plain, consistent backgrounds across all product images to reduce distractions and make it easier for the AI to focus on the product itself.

three different types of water filters in boxes


4. Standardize Image Sizes

Use uniform image sizes and aspect ratios to streamline the AI's processing of visual data across different products.

a row of different kind of electronic devices


5. Detailed Metadata

Add descriptive metadata and tags to images, including attributes like color, size, and material, to improve search relevance.

a man standing behind a metadata screen


6. Update Images Regularly

Keep product images and metadata updated to reflect changes in inventory, ensuring the AI system remains accurate and up-to-date.



» Achieve success with a personalized search and recommendation engine

Case Studies: AI Visual Search in Action

ASOS, a global fashion retailer, implemented visual search to let users upload images of clothing they liked. The system then suggests similar items from the ASOS catalog. This innovation led to a significant increase in user engagement and conversions, with customers favoring visual search over traditional text-based methods.

a collage of photos of men and women


Similarly, eBay introduced AI visual search, allowing users to upload photos of items they wanted to buy. This feature proved especially useful for rare or unique items, improving customer satisfaction and boosting sales.

a pair of shoes is shown on two iphone screens


The Future of AI Visual Search in eCommerce

Looking ahead, AI visual search will likely evolve with more accurate image recognition, faster real-time processing, and deeper integration with augmented reality (AR). In the future, shoppers may be able to use AR to visualize products in their own environments before purchasing. Additionally, emerging trends like multimodal AI, which combines text, voice, and visual inputs, will further enhance personalization and accuracy in product searches.

AI-powered visual search is a game-changer for eCommerce, significantly improving the user journey by making product discovery faster, more intuitive, and personalized. With its ability to enhance engagement, reduce search times, and support mobile shopping, AI visual search is quickly becoming an essential tool for eCommerce platforms looking to stay competitive in the digital age.

» Explore how technologies like AI-powered visual search can enhance your customer experience