Published on
April 14, 2022
Visual AI is disrupting the world of retail.
Research shows that customers are really eager to utilize visual search as part of their shopping experience. Additionally, a report by Sparktoro showed that Google Images is the second leading participant in the search engine industry, with 21% of searches originating there.
With the rise of online shopping, it’s no wonder retailers are looking to incorporate visual artificial intelligence in their stores.
The combination of computer vision and artificial intelligence has brought us technologies like automatic tagging and visual search that benefit both customers and retailers.
Modern technologies have enabled:
In this guide, we combine market research and our own experience to bring you everything you need to know about visual search.
To get to the beginnings of visual search, we have to go back to 2001.
This was when Google launched its image search option for the first time. The search engine giant had a database of 250 million images users could search from.
The reason was one of the most popular dresses of all times—the dress Jennifer Lopez wore at the Grammy Awards ceremony in 2000. As everyone was searching for this dress, Google decided to introduce the image search feature to handle it.
Since then, many events have contributed to the evolution of visual search.
The technology has progressed a lot during this decade. With Google Lens and Pinterest Lens as important innovators, we're expecting to see a lot of developments in the following period.
We are visual beings.
When you're looking for your coat, you already see it in your mind and that’s how you’re able to recognize it. If you have seen this coat only once, you'll probably need more time to find it. But, the more you see it, the shorter it will take for you to recognize it.
Computers perform the same task. The visual search feature allows users to search by images instead of typing in keywords.
A shopper can download an image from the Internet, take screenshots, or take photos of whatever they want to search for.
Then they can upload it to a search bar that supports visual search and voila - they get a list of images that look similar.
Brands have recognized the technology potential and started adopting visual search. This brings them more traffic and conversions, taking the customer experience of their e-commerce stores to the next level.
Visual AI engines can identify the context of the image and, therefore, detect the images with the most similar context. This allows online retailers to provide shoppers with more reliable outcomes than just utilizing keyword searches.
This is all made possible by combining computer vision and machine learning algorithms.
Although computer vision has been around for a while, it was AI-based machine learning algorithms that enabled visual search engines to understand image context.
Computer vision allows computers to see and characterize what they see.
To perform visual search, deep neural networks imitate the functions of the human brain. They detect components of the image, understand what they are about, and conceptualize related objects. These components may involve textures, shapes, or colors. Visual search also bases its results on specific metadata and keywords found in the picture.
However, this process doesn't happen as easily as the human brain performs it.
That’s where machine learning comes in play.
Machine learning powers computer vision engines with the knowledge they need to recognize what is displayed within a picture.
Computer vision algorithms have to learn from a large number of images to be able to perform this task better. The more images they process, the more precise they get. These networks work unsupervised. This means that they operate based on input signals without any human intervention.
Visual search is part of what is considered sensory search. This includes searching by voice, text, and vision.
Even though both image search and visual search are related to images, they are significantly different.
Image search has been around for ages.
Image search is performed when a user goes to the search field and types in a text query.
Then, the search engine provides a SERP (Search Engine Results Page) with a list of images that match the text query.
By choosing smart filters from the menu bar, you can narrow down the list of photos in results.
In visual search, the text query is replaced by an image. This is also called “reverse image search”, “search by image” and so on.
Search engines need more complex structures to conduct an active visual search than conventional image searches.
Text search is still the most popular way of searching (The average person uses search 3-4 times per day). However, existing full-text search methods have slowly started to become outdated.
Text search don't offer the context and convenience users need while searching for a particular product. The main challenge when it comes to expecting users to describe what they’re searching for is what’s called a vocabulary gap.
The vocabulary gap happens when:
For example, you've been looking for a black bag for months, and you just saw the perfect one for you.
If you type "black bag" in some large e-commerce store, you could get thousands of results. You certainly won't start checking all the bags the website suggested.
If the e-commerce store offered a visual search option, you could upload a photo of the bag you saw and find the most similar one in seconds.
It's evident that text search has many limitations.
A study by Baymard Institute that analyzed the behavior of the customers of 19 also confirms this. According to the survey, 70% of e-commerce stores need users to "search by the exact jargon for the product type that the website uses." This means that if someone uses a different word for the same product, they won't be able to find it. A gap like this could cause many missed conversions for the store and customers that don't experience the best buyer journey.
Those aren't the text search's only drawbacks.
Managers in e-commerce shops have many difficulties predicting the correct terms that consumers would be using.
Moreover, certain items are very complicated to explain because everyone uses specific terms to define them. This contributes to failures when managers struggle to develop clear communication with consumers who are searching for such products.
Visual search can transform fashion ecommerce: it improves product discovery and creates a better, smoother customer experience, opening new opportunities for both online retailers and customers.
What's more, we frequently try to choose an entire style, costume, or design instead of a single item. Visual search technology can bring these objects together based on visual similarities in a way that text could never capture them.
Although still in its beginnings, visual search is a powerful technology that is yet to be fully utilized. Recognizing the technology’s potential, giants like Google and Amazon have created their visual search engines. A lot of ecommerce stores follow suit. In a recent research, Gartner discovered that by 2021, early adopter brands that will include visual and voice search in their e-commerce websites would increase their digital commerce revenue by 30%.
But using visual search technology in fashion retail has many benefits that go beyond increased revenue.
Here’s a rundown of the most important ones.
Visual search detects patterns in the image and then identifies them in website photos, all thanks to AI. This results in the following advantages:
Visual search lets users quickly discover the products they are looking for. They literally can just take a snapshot of what they've found, and they can find the most visually similar item in an online store straight away, without needing to wonder how to explain it or spending hours browsing a vast list of products to find the exact product they are looking for.
A report by Global Web Index discovered that 72% of people born between 1995 and 2010 had purchased something online in the last month. What is more, 6 out of 10 are buying through their mobile devices.
Why is this important? Because in the US alone, this generation has a spending power of over $143 billion.
What is really interesting about Gen Zers is that they prefer buying more personalized products.
They also love social media: 85% of them learn about new products using Instagram or Facebook.
This means that there has never been a better momentum for making your brand visible outside of conventional search areas.
Visual search can help you make your online retail offering more personalized. Furthermore, you can enable Gen Zers to find the products they saw on social media much faster. The only thing they need to do is screenshot the Instagram photo and use it to search for a particular clothing item.
Searching solely by text can be a lengthy process. The buyer could give up buying somewhere on the way because finding the exact product they want is too complicated.
By enabling them to use visual search, you take them straight to the item.
Visual search eliminates the entire process of entering a keyword, going through the list, trying another keyword, and then another until a shopper finally finds what they need.
Instead, your shoppers just have to upload an image. This image will take them to the very thing they are searching for, and make it far simpler to convert.
This allows you to:
New visitors that come to your website need a reason to stay. Today’s shoppers are used to great online experiences and that’s exactly what you need to give to them if you want to encourage them to make a purchase.
Spearfishers are customers who are searching for a particular item. They know exactly what they want and want to get to it fast. This means that you should minimize the number of steps they need to go through to buy the product.
Asos launched its visual search tool called Style Match back in August 2017.
Thanks to this feature, shoppers can use the app to take a snapshot, adjusting the focus on the product they want. Then, they can use this picture to find it in the ASOS product catalog. Users can also upload their own images.
For example, when their favorite influencer posts a photo wearing a really cool dress, they can upload the picture to ASOS and get a list of similar items.
The visual search feature of eBay helps customers to check eBay for a product when they encounter an image of it on the Internet.
When they see the product on some website, they can easily click on the "Find it on eBay" button, and eBay's algorithm will scan its online shop for the specific products.
The company uses a convolutional neural network, a deep learning algorithm that goes through the product catalog on eBay and looks for correlations based on visual comparisons.
In 2014, Amazon integrated visual search into its flagship iOS app, offering consumers the ability to use their mobile camera to browse. It's mainly intended to capture the 'showrooming' shopper – someone who visits a physical store but checks online prices to compare. Although it doesn't recognize every specific piece, reviews suggest it's especially useful to identify photos of DVDs or records.
The AI-powered visual search feature from Marks & Spencer lets its smartphone device users upload a snapshot of any outfit and locate similar products in less than ten seconds.
The Style Finder was intended to help retailers become digital-first, as their mobile app now accounts for over three-quarters of their online traffic. With this feature, the retail giant wants to increase the number of online sales by 2022 to one-third of all purchases.
Research by The Intent Lab showed that when it comes to clothing or furniture online shopping, 85% of users find visual information more critical than text information.
The explanation for this is simple—our brains are designed to prioritize visual information. In fact, 90% of the information our brains receive is visual.
Visual search helps users overcome the limits of text search and offers a fast and efficient product discovery experience that enables visitors to find products quickly.
Although many major players have started to leverage this innovation, we still haven’t seen all that it can do. In the next few years, we expect visual search to become more popular, improving the accuracy and legitimacy of search results.
Neural networks and machine learning will be even smarter in the future as the number of people using visual search rises. Given that Google and other giants are moving the limits of what search represents, e-commerce store operators must prepare for the future of search, walking in the direction of sensory search.
With technologies like AI and computer vision becoming a part of our everyday lives, we must keep in mind that young online buyers like millennials and Gen Zers continue to engage with brands that provide such services and demand them to do so.
In a world where visuals rule, visual search allows our brains to do what's natural for them—rely on what they see. A brand that uses visual search is a brand that looks forward to the future.
Pixyle AI generates e-commerce product data that enables brands, retailers and marketplaces to deliver exceptional product discovery experiences.
