Enhance your E-commerce Product Search with Visual API

Product discovery is one of the most important drivers for the success of e-commerce businesses. This is especially true with younger audiences making a major chunk of e-commerce sales. Furthermore, shoppers who use search functionality on e-commerce websites have a high intent for purchase. 

Despite this, over 61% of websites perform unsatisfactorily when it comes to search performance. What's more, 72% of websites completely fail in aligning search expectations with results. 

While this is a bitter realization for e-commerce businesses, it is not exactly a mystery. While traditional text-based search engines are powerful tools, they have limitations when it comes to e-commerce search. Many online shoppers know what they want, but don't know how to describe it with the right words.

In other cases, shoppers simply don't know what a product or a brand is called, but have the intent to purchase it. This is where visual search can be a transformational solution. This blog will guide you through the relevance of visual search for e-commerce, and how you can implement it for your business. 

What is visual search in e-commerce? 

Visual search is powered by AI and image recognition technology. It analyzes an image to identify objects within it and then compares those objects against a database of known images to find matches. For example, if you were looking for a pair of black shoes on an online store, you could simply upload a photo of the pair of shoes you want and the visual search engine would scour the internet for retailers that sell them.

Thanks to advances in artificial intelligence and machine learning, image recognition algorithms are now much more sophisticated than they were even just a few years ago. 

Visual search can be used to search for products by their appearance, such as color, shape, or style. It can also be used to find products similar to ones that have been previously viewed or purchased. Consequently, visual search is fast becoming one of the most popular ways for online stores to help consumers find the right product. Google Images and Pinterest Lens are some of the early pioneers in this area, with ecommerce giants such as Amazon following suit. 

Further, visual search can help e-commerce businesses appeal to a wider range of customers. For example, some customers may be more interested in searching for products by color or pattern rather than by type or brand. By incorporating visual search into their product search functionality, e-commerce businesses can make it easier for all types of customers to find the products they’re looking for.

Why e-commerce businesses need to integrate visual search on their store’s internal search

Incorporating visual search into your e-commerce search functionality is a future-forward way of elevating customer experience. This is evident when you consider the younger audiences, especially millennials and generation Z. In fact, 62% of the millennials prefer visual search over searching on Google, Pinterest, or Amazon to fine tune their search results. Let's take a look at all the ways visual search is beneficial to e-commerce businesses

Improved customer experience 

Visual search can help make your site more user-friendly and easier to navigate, which can lead to a better overall customer experience. This is accomplished by offering quick, accurate, and relevant image results when the user inputs a query (in this case, an image from the internet or from their camera). This is important, because when compared to text-based search, visual search can guide the user to similar products, if not the exact same product that they initially intended to purchase. In short, visual search offers a better online shopping experience. 

Better product discovery

Customers can easily discover products on your site by simply searching for an image rather than a text-based description. Visual search can also help users discover new products that they may not have found otherwise. For example, if a user is looking for a dress but doesn’t know what the style is called (a common scenario in the age of social media), they can use visual search to find the same style of dresses along with multiple similar options. This is especially helpful for users who are unfamiliar with a particular product or category.

Higher cart and order values

Fashion e-commerce giants such as ASOS and HM leverage visual search on their mobile apps to nudge users to 'shop the look' instead of a single product, which may have been their initial intent. This encourages users to spend more time on the store, and find complementary products that fit their own sense of style. 

Better search accuracy and relevancy 

This goes without saying. Visual search allows users to find relevant products quickly, a contrast to keyword based searches. Instead of spending their time and energy finding the right description for their queries, shoppers can now find delightfully accurate product suggestions using e-commerce image search.  

How does visual product search work? 

An image search engine analyzes an image to extract deep features and identify matching products in a catalog. This search engine uses computer vision algorithms to identify key points, objects, and colors in the image. It then compares these features against a database of images to find matches. The results of the search are displayed to the user as a set of product thumbnails accompanied by metadata such as labels, tags, or descriptions.

Visual product search is powered by machine learning, which means it gets smarter over time. The more images it processes, the better it becomes at understanding how those images relate to each other. 

Some e-commerce platforms have their own visual product search feature built-in. For example, Amazon's "Fire TV Stick with Alexa Voice Remote" has a "camera search" feature that lets you take a photo of an item and find similar products available on Amazon.

How to build a visual search functionality using visual API 

Using an API is usually the simplest and most effective way to add visual search to an existing e-commerce product search system. In most cases, all you need to do is send the image data to the API endpoint and receive the results back in JSON format. 

A visual search API is a web-based interface that enables developers to access and integrate the visual search functionality into their applications. Once you have found an image processing API, the next step is to integrate it into your e-commerce product search. This can be done by adding code to your existing search functionality or by creating a new search endpoint specifically for visual searches.

Once you have integrated the visual search API into your e-commerce product search, you will need to index all of your product images so that they can be searched against. 

Once all of your product images have been indexed, you can start performing visual searches against them. Simply submit an image to the API and it will return a list of visually similar products. Try different images and experiment with the settings to get the most accurate results possible.

Conclusion - Visual search is a key ingredient for building powerful search experiences 

Up until a few years ago, keyword-based search was the only way shoppers could access products online. But with the advent of visual search, e-commerce businesses are now able to elevate their search performance through deep learning and computer vision.

Today, all a shopper needs to do is point their camera to their desired product and find highly relevant product suggestions through visual search. Platforms such as Zevi are leading this transformation with AI-powered search and product discovery for businesses. 

To explore more, book your demo today.