How much Visual Search adds to the eCommerce App?

Obviously, SEO as well as various search tools are an essential part of the eCommerce industry. It may seem, that SEO technologies have already gained their maximum: there are lots of various instructions, tutorials, and various tools, that are used to improve search query optimization. However, there is no absolute, especially in the field of software technologies. So is it possible to predict the future of search engines?

What is Visual Search?

Actually, thanks to the development of machine learning and artificial intelligence, various industries gained new ways to improve, and SEO is not an exception. For instance, such technologies allow increasing the number of search approaches.

Clearly, sometimes it is easier to show a picture than describe the object you are looking for. It is the main principle of visual search. Actually, this technology is not new. Probably, most of us at least once used Google "search by an image" feature. In fact, Google Pictures is an alternative search engine, that coexists with the usual text search query.

Actually, such search engines were developed by various tech giants. For example, Microsoft also owns a visual search, also known as Bing Visual Search. The main issue is that despite the first samples of such tech being developed in the early 2000s, it wasn't adopted by smaller companies, that had limited resources. At the moment, the situation is changing, and various eCommerce platforms and corporations like Ikea, Meta, Pinterest, etc.

So how does it works? Talking about the user experience, it is very simple. For instance, if users want to find a similar image, its origin, or find a picture with a higher resolution on Google, all they need to do is to download the image sample, open the Google images search, and tap the "camera" icon. Then, in the pop-up window, they have to past the URL of the image (in case there is no downloaded copy), or choose the "upload an image" option and then click on the "choose file" button. Afterward, choose the file on their device and tap on "upload". That's all, the rest will be done by Google itself.

Thanks to machine learning and AI, Google algorithms pick similar images and show them to users. The explanation of how it works on the code level is too extensive and deserves a separate article, therefore let's limit ourselves to the theoretical part.

Is It Worth Considering?

The short answer is: yes. Speaking more broadly, before adopting the existing software or developing your own, it is worth weighing all the pros and cons and also understanding what this technology can bring you, as well as do you actually need it.

Clearly, the main industry, where such software is gaining popularity and use cases is eCommerce. In this industry, visual search can become a powerful tool, that can help to increase customer engagement, proposing a faster response rate and improving the quality of services, improving the search queries, and providing more detailed and fitting results.

It is obvious, that the main purpose of the eCommerce sphere is to offer the buyer exactly the product he is looking for and sell it. Thus, the visual search option can help customers to look for a specific product or similar to it. For example, imagine you have found the ideal shoes that fit your outfit or a table that will be a perfect addition to your kitchen. The only problem is that you saw it on Pinterest or Instagram, without any needed information about the brand or name of the object you are looking for. So, how to find it in the marketplace and buy it? It is unlikely that you will be able to describe this object in words and immediately find it on eBay or Amazon.

Besides, how will you describe such common things? "A beautiful wooden table on 4 legs"? "Cool red sneakers with white laces"? Undoubtedly, such descriptions will result in hundreds of results, so be ready to spend some time surfing the web.

At the same time, imagine if you were able to take a screenshot or a picture of the product you are interested in, show it to the AI and get the most matching results. This is what visual search proposes. What's more, what will be your reaction, if we told you, that such a tool is already used in various cases?

What is the Situation at the Moment?

In order to illustrate the benefits of adopting or developing such a search engine, let's consider some of the modern cases.

Google Lens

It is probably one of the most advanced examples of visual search engines. As was mentioned before, Google was among the pioneers in this field. Therefore, they had the opportunity to improve it and develop it into a standalone service, available to identify the visual objects and show similar ones, as well as a short explanation of what is shown in the picture.

At the moment, it is already presented on multiple platforms, including iOS, Android, and Desktop versions. Also, the functionality is growing as well. For instance, it is able to distinguish the text and translate it. Additionally, it can perform calculations too.

Google lens is incorporated into the Google ecosystem, which allows it to use its other services like Google search, which highly increases the capabilities of the application. In the context of eCommerce, it helps to identify various objects and show similar products on various marketplaces, proposing to buy them.

Going back to the above example, the customer can take a snap of the table or shoes, upload it to Google Lens, and get the link to these or identical products in an online shop. Obviously, it highly simplifies the searching process, proposing the needed results.

It is possible to assume, that in the nearest future the Google Lens API specifically for eCommerce will become available, just like any other services from the developer. The same assumption can be made regarding Microsoft Bing Visual Search.

Pinterest Lens and "Rich Pins"

Another pioneer in the visual search market is Pinterest. It started as a web application, which can be described as photo storage, allowing its users to "pin" any photo while surfing the web. Additionally, it is partly similar to social media platforms, meaning that other users can see your pinned photos if you want them to.

In addition, users can surf the website, watching other users' collections and pins. Pinterest also provides useful search options. For instance, it analyzes and categorizes users' pins and proposes relevant images. As a result, if you are collecting design ideas for your apartment, interest will propose to you other pins on this topic.

Pinterest expanded its functionality and popularity by adding a "Pin it" extension for browsers, which improved the user experience, simplifying the saving opportunities. Later, the Pinterest API appeared. Hence, the functionality was increased for the website owners as well.

One of the last innovations, made by Pinterest, are so-called "Rich Pins". In fact, this solution increased the number of possibilities to use Pinterest for merchants and online retailers. For instance, it allows advertising of various products and goods on the website. This feature is free and has only one major requirement - the user who uses this feature has to have a business account.

In other words, rich pins became visual links to the advertised product, that are proposed to the users in accordance with their interests. Moreover, Pinterest provides an internal visual search feature. For instance, users found some things they want to buy. All they need is to choose the "search" button in the corner and highlight the object in the image. The application will show comparable pinned objects, including the rich pins. Then, the users find the needed pin and get all the required information about the product, as well as the kink to the merchant or online retailer where to buy it.

Ikea AR and GrokStyle

Ikea is another great example of how to successfully integrate visual search technology into the eCommerce industry, creating a useful visual search app.

Actually, Ikea adopted the technology, developed by GrokStyle. The final application seems to be a perfect solution for the company that creates furniture. Ikea Place App provided an augmented reality feature, allowing users to plan their apartment, using virtual furniture.

Yet, it is not the final functionality of the app. In addition to the great idea of giving design planning tools to the customers, developers implemented the point-and-search function, developed by GrokStyle. As a result, users had on opportunity to open the app on their mobile device, point its camera at the object, for example, table, and find additional information about it. Frankly, Ikea limited the catalog to their product only.

Nevertheless, the Ikea application was deactivated, while the GrokStype product was redeemed by Meta in 2019. At the moment, it is unclear how exactly this technology is used, regardless, it is a great example of how visual search can coexist with AR, increasing the usage purposes of such technologies in the eCommerce industry.

Summary

Summing all the above, it becomes clear that visual search tools are an influential addition to the eCommerce sphere. It helps to improve user engagement, increase functionality, and simplify online searches.

Moreover, image search can help the marketplaces or merchants to attract new potential customers. For instance, Asos used visual search functionality to develop StyleMatch. It is an online shopping tool with visual search capabilities, that improves the customer experience by proposing a similar product to the one, the user is looking for by uploading its picture. This allows Asos to analyze the most searched items and create an analog. Clearly, Asos offers its own products.

In the case, if you are looking for some examples of developed eCommerce platforms - meet our projects list.