September 15, 2020
Visual AI is disrupting the way we do retail. The combination of computer vision and AI has brought us technologies like automatic tagging and visual search that benefit both customers and retailers. Customers get an improved, more personalized shopping experience, while retailers get valuable information, higher conversion rates, and reduce resource spending.
With the rising demand for online shopping, retailers are looking to incorporate visual AI into their e-commerce solutions. Studies show that customers are really eager to utilize visual search as part of their shopping experience. Furthermore, a report by Sparktoro showed that Google Images is the second leading participant in the search engine industry, with 21% of searches originating there. This means that visuals aren't going anywhere.
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. In 2007, Google included more image data in its database, like URL and resolution. Two years later, Google enabled users to use the feature to find similar images.
The most important event was probably in 2011 when Google introduced its Search by Image feature. It enables people to upload an image, rather than entering keywords, resulting in a collection of matching photos, sites featuring the image, and various sizes of the same picture.
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.
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 search is something that happens in our minds every day. For example, when you're looking for your coat, you use your coat's image from your head to recognize it in the reality around you. 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. They can download pictures from the Internet, take screenshots, or take photos of whatever they want to search for. In return, they get a list of images that look similar.
Visual search combines computer vision and machine learning algorithms. Although computer vision has been around for a while, it was AI-based machine learning algorithms that enabled it to understand image context. Computer vision not only allows the computers to see, but it also allows them to characterize what they see before deciding what to do about the info.
Machine learning basically offers knowledge that computer vision requires to recognize what is displayed within a picture. AI engines can identify the context of the image and, therefore, detect the images with the most similar context. Online retailers can provide their website users with more reliable outcomes than just utilizing keyword searches.
By the end of 2020, 35% of marketers plan to optimize their websites for visual search. Thanks to the growing adoption of visual search, the global market is expected to exceed $14,727 by 2023, growing at a CAGR (Compound Annual Growth Rate) of 9%.
People take pictures of everything. Not just places they travel to, but also random items they don't want to forget. Visual search makes these pictures actionable. A picture of your shopping list turns into a full shopping cart. A picture of a random dress you saw turns into the best dress you've ever possessed. Visual search opens so many new opportunities.
To perform visual search, deep neural networks imitate the functions of the human brain, identifying target objects and recognizing them in other images. They have to detect components of the image, understand what they are about, and conceptualize related objects.
Machine learning and neural networks are examining related features inside the picture and identifying them on website photos. These features may involve textures, shapes, or colors. Visual search also based 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. The 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. Moreover, these networks work unsupervised. This means that they operate based on input signals without any human intervention. This will transform the way we search entirely because we can get more reliable answers than full-text searches.
Visual search is part of what is considered sensory search, which includes searching by voice, text, and vision.
Even though both image search and visual search are related to images, they are significantly different. In image search, the user submits a text-based query to seek the closest image option, while in visual search, the text query is replaced by an image. Search engines need more complex structures to conduct an active visual search than conventional image searches.
As we already mentioned previously, image search has been present since 2001, when an enormous number of people were searching for Jennifer Lopez's dress. 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.
As the average person uses search 3-4 times per day, text search is still the most popular way of searching. Existing full-text search methods have, though, started to outdate. They don't offer the context and convenience that users seek while searching for a particular product.
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 eliminate these challenges. As 90% of the information that our brains perceive is visual, it's natural that visual search could provide a much more comfortable experience for users.
Visual search can transform how we communicate with our surrounding world. It's no surprise that its adoption is growing rapidly because our brains only need 13 milliseconds to identify an image. Our society is visually-driven, so it seems reasonable to use a photo to start searching for something.
Visual search makes product discovery much more comfortable, 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. Recognizing the potential, giants like Google and Amazon have created their visual search engines, which represents a significant boost to the technology growth.
Although still in its beginnings, visual search is a powerful technology that is yet to be revealed. 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%.
Visual search detects patterns in the image and then identifies them in website photos, all thanks to AI. This brings several main advantages for e-commerce store managers:
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'll locate it straight away.
They can search for it without needing to wonder how to explain it. Also, consumers don't have to spend hours going through a vast list of products to find the exact product they are looking for. In no-time, they will discover the visually most similar item with visual search.
Generation Z purchases online. In the US alone, this generation has a spending power of over $143 billion. 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.
Gen Zers also love social media. 85% of them learn about new products on social media. Instagram is the app where they found the newest products, followed by Facebook in second place.
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 get to the products they found 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 product.
New website visitors need something to attract them to become buyers and then turn into loyal customers. Creating a personal connection with them is crucial to encourage them to make a buying decision. Allowing them to search by images enables customers to establish a more emotional connection with the brand. Knowing that finding products on your website is easy, they will become loyal customers and less sensitive to price.
New visitors that come to your website need a reason to stay. And with the perks that visual search brings, you're giving it to them.
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 simply too complicated.
On the other hand, visual discovery takes them straight to the item. It eliminates the entire process of entering a keyword, going through the list, trying another keyword, and then another until they finally find what they need. Instead, they 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.
Moreover, the human brain is designed to identify visual objects. With visual search integrated, it will be easier for customers to remember the items and come back to complete the purchase.
The growing popularity of visual search has increased the importance of SEO. This is because there's also competition in image search results. In this situation, metadata and schema markup are critical for SEO, as search engines can take data from there. If they want to be better than the competition, online retail stores need to work on them. This way, they will achieve better results than with a solely text-oriented SEO strategy.
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.
Visual search does this. With visual search, they don't need to type in keywords and scroll through a long list of results. The product they want will appear instantly. Thanks to the short and efficient searching process, visual search can reduce shopping cart abandonment.
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.
Originally introduced back in 2014, Target's 'In a Snap' feature enables consumers to browse for products from magazine covers and printed advertisements – eliminating the need to check for the items online manually. Although this update no longer seems available, Target's mobile app still enables shoppers to check in-store barcodes and reveal further info, such as comments and ratings. This makes shopping much more convenient, eliminating the need to ask in-store staff for more assistance.
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.
That's why text search has become very limiting. Visual search takes the boundaries off 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 are away from seeing all that it can do. In the following years, we expect it 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.