Visual Search in Ecommerce: Modernizing Online Shopping

Think about taking a photo of a stranger wearing an outfit you adore, and without having to type a word, you can quickly locate identical products on the internet. That is how visual search in ecommerce works its magic! Given that 62% of millennials favor visual shopping over conventional techniques, it is evident that this invention is revolutionizing the retail industry. 

Visual search technology aids faster and more intelligent product discovery, potentially reducing the time between inspiration and purchase. This technology not only makes product discovery easier but also personalizes shopping and increases conversions.

In this blog, we’re going to look into why visual search in ecommerce is becoming a must-have for retailers. 

What is Visual Search in Ecommerce?

Visual search is a technology that enables you to search the e-commerce store by just uploading an image rather than entering it in a search bar.  It’s similar to having a virtual assistant that uses visual signals like color, shape, and pattern to find precisely what you’re looking for. By just taking a picture, this AI-powered tool makes it simple to find related products or obtain additional information. With Visual Search, you can search the internet more effectively and say goodbye to lengthy scrolling. 

Visual Search Examples:

Here are some of the real-world examples of visual search engines;

eBay’s Image Search

eBay users can upload images of the items they want to buy, which helps them locate similar items on the website. For rare or used items, this feature is really helpful.

ASOS Visual Search

Customers can utilize the visual search feature of ASOS, a well-known fashion shop, to find similar items on their website by snapping a photo of an ensemble or piece of apparel. This facilitates easier and more intuitive fashion shopping.

L’Oreal’s Style My Hair

Users of L’Oreal’s Style My Hair app can upload an image and virtually try on various hair colors using visual search. Before making a purchase, consumers can utilize the technology to see how items like hair dyes might seem on them.

The Technology Behind Visual Search: How it Works?

Visual search in ecommerce uses cutting-edge technologies such as computer vision, machine learning (ML), and artificial intelligence (AI) to analyze and understand images. Here’s the process of how it functions; 

1. Image Uploading: 

Users upload an image from their gallery or paste a URL to obtain an image. From here, users can start searching for their desired product.

2. Feature Extraction:

Visual search systems understand and extract important image features, such as shapes, colors, patterns, textures, and many more facets.

3. Image Processing with AI:

Incorporated AI and neural networks help process images to recognize the objects, tags, or components present in them. Pre-trained models are used to classify items and locate them appropriately.

4. Matching with Database: 

The extracted features match those in the eCommerce platform’s product catalog/image database. Advanced algorithms rank the possible candidates who are most similar to the input image against the most matched results.

5. Display of Results: 

The system brings out a catalog of closely resembling input images for products to allow browsing, selecting, and shopping for similar items.

Technologies Involved 

• Deep Learning Models: The algorithm uses convolutional neural networks to learn visual patterns and extract meaningful information. 
• Image Recognition Software: Recognizes objects, props, logos, etc., in an image. 
• Analysis of Metadata: Improves matching by providing tags or categories for the image together with image features.

Popular Ecommerce Platforms Using Visual AI

Major ecommerce platforms increasingly utilize visual AI technologies to enhance product discovery and improve user experience. Here are some notable platforms; 

Amazon:

• Feature: StyleSnap (also known as Shop the Look in 2022).
• Launch Year: 2019 for fashion; home expanded on it in 2020.
• Technology: Deep learning and convolutional neural networks (CNNs)-based approaches for image analysis and product recommendations.
• Performance: Algorithms are trained on insights from hundreds of thousands of images, resulting in a recognition rate of images that exceeds 95%. 
• Market Impact: The total addressable market in U.S. fashion ecommerce is about $200 billion; for furniture, it is around $150 billion.

ASOS:

• Feature: StyleMatch 
• Launch Year: Its international rollout started in early 2018.
• Technology: The user uploads a picture and finds similar items from the ASOS catalog. 
• Database Size: The tool accesses over 85,000 products, with more than 5,000 new items added weekly. 
• User Engagement: Users engaged with the visual search tool discovered 48% more items and were 75% more likely than non-users to return to the site.

Pinterest:

• Feature: Pinterest Lens
• Launch Year: Launched in February 2017.
• Technology: Allows users to identify products through images and find similar items across various retailers using visual AI.
• Impact on Retailers: Retailers have reported increased traffic and sales from users engaging with Pinterest’s visual search features.

Visual Search vs. Traditional Search: Why Is Visual Search Ground-Breaking?

Here’s the difference between visual search vs. traditional search: how visual search is changing the game for retailers; 

Visual Search vs Image Search:

Image search functions differently and has distinct goals than visual search. While visual search employs AI to examine an uploaded image’s real content, such as color, shape, and patterns, to locate visually related things, image search uses text-based metadata to find matching photos.

Unlike image search, which is better suited for reference or study, visual search is excellent for product discovery. The fact that 36% of consumers use visual search to locate things online is not surprising, as it demonstrates the growing popularity of visual search in ecommerce industry. 

Why should you optimize for visual search?

Following are the reasons that you should take into account when optimizing for visual search in ecommerce;

1. Increasing Demand from Consumers:

62% of millennials prefer visual search over conventional search methods – are you meeting their needs?

2. Improved Experience: 

Does your platform make it simple for users to locate particular products without using precise keywords?

3. Increased Conversion Rates: 

Are you giving users accurate and pertinent matches that encourage them to make decisions about purchases more quickly?

4. An Edge over Competitors:

Does your company use visual search to differentiate itself in visually-driven markets?

Impact on Customer Journey: From Discovery to Purchase 

Customers’ shopping habits are being revolutionized by visual search. They may find similar things by simply uploading a photo rather than typing out lengthy descriptions, which speeds up and simplifies the discovery process. Customers are kept interested and find it easier to compare options when this gives a sense of excitement to the buying experience. 

Decisions may be made more quickly with more accurate search results, which increases conversions. Visual search in ecommerce ultimately simplifies the purchasing process, resulting in happier customers, higher sales, and improved customer loyalty. 

Visual Search and Social Media: The Perfect Pairing

Visual product search and social networking platforms are a match made in digital heaven. Platforms such as Instagram and Pinterest enable consumers to find things straight from photographs they love by incorporating visual search features. Visual product search transforms inspiration into action, whether it’s recognizing a pair of shoes in a post or locating comparable home décor from a pin.  

By instantly connecting users with products, this smooth experience not only increases user engagement but also boosts sales by making it simpler than ever to shop straight from social media.

What are the Challenges and Solutions in Visual Search Adoption?

Despite the enormous promise of visual search technology, organizations encounter various obstacles when implementing it. The main obstacles and their solutions are as follows:

Challenge: Limited Access to Data

• Problem: Visual search depends on large databases of annotated images, which might not be available or comprehensive.
• Solution: To create extensive datasets, collaborate with AI suppliers that specialize in visual product search, and use synthetic data production.

Challenge: Exorbitant Implementation Expenses

• Problem: It can be costly to develop and implement visual search engines.
• Solution: Choose cloud-based AI tools and services that provide affordable, scalable visual search options.

Challenge: Reliability and Pertinence of Findings

• Problem: Users may become irritated by incorrect identifications or unrelated matches.
• Solution: It’s better to spend money on sophisticated AI models that use deep learning for frequent model changes and more accurate visual identification.

Challenge: Integration with Existing Systems

• Problem: It can be difficult to integrate visual search with older e-commerce apps or platforms.
• Solution: For a smooth integration with no interruption, use pre-built modules and APIs.

Challenge: Adoption and Consumer Awareness

• Problem: A lot of users don’t know how to use visual search efficiently.
• Solution: Inform customers about the advantages of visual product search through marketing campaigns, demonstrations, and tutorials.

How VisionX Enhances Visual Search in Ecommerce

VisionX’s customized Computer Vision Development Service can help grow your online store to new heights. By simply uploading an image, you can enable your clients to browse your products with ease.

Our sophisticated generative artificial intelligence system sorts through product images, classifying and labeling them to make it simple for customers to find related products they want. Instant picture matching removes the need for extra steps by quickly directing customers to visually similar items in your inventory.

With the use of cutting-edge tools like TensorFlow and PyTorch, our team develops custom AI algorithms to guarantee flawless functioning that meets your unique specifications. This improves your website’s aesthetic appeal while also increasing consumer happiness and loyalty, which eventually leads to increased sales!