September 24, 2020
Visual search has been quite a buzzword in the e-commerce world during the past few years. It's probably the most convenient way of finding a product.
For example, let's say you're walking down the street and you see something beautiful in a shop window, but you don't have the time to enter the store. You also don't really know what keywords to use to Google it. So, what do you do?
If only there was a way to take a picture of it and get a list of the products that look alike? Well, that is what visual search does. With visual search, users can simply upload a picture and get a query of the most similar products in the webshop in just a few seconds.
This AI-based technology is disrupting the e-commerce market, and it's wildly popular among young consumers who want to get quick results. What is more, Gartner research predicts that by 2021, e-commerce stores that will incorporate visual and voice search in their websites will get a digital commerce revenue increase by 30%. This will boost the image recognition market's growth, expected to reach $77 billion by 2025.
However, to make sure your products are easily accessible and prepare your e-commerce store for visual search, you need to create a clean, structured, and up-to-date image database.
Images are an essential part of each product page of your e-commerce store. In an age when both B2C and B2B clients search for products online, you have to provide a clean product catalog with images that show the looks and the functionality of the product.
Images are the only way your online customers can get a perception of what the product looks like, and that's why your image database has to be well-optimized. With an image database that contains the right product keywords, your customers who use visual search will have a painless product discovery experience. Also, a well-managed image database will build you a good brand image as the market leader, establishing a strong relationship with your customers.
Allow your customers to search through your digital product catalog with ease by giving them the ability to scan, filter, and sort your online shop's product details. This is something you can achieve by tagging all your product images.
Every product in an online shop consists of many tags set to identify its attributes, functionality, and the group to which it belongs. These tags should describe everything about the product, including its color, type, brand, size, etc. For example, a coat could have the following tags: winter, wool, long-sleeve, green, sports, etc.
Tags play a crucial role when customers filter the products based on the attributes they are looking for in a product. That's why they need to be accurate and precise, allowing customers to find what they want quickly.
The manual product tagging method can, however, be very unreliable. For retailers with large image datasets, where product tagging can last for weeks, it's even more difficult. Retailers could save a lot of resources if they were to automate this entire process.
Automatic product tagging replaces manual product tagging by leveraging AI algorithms to organize and tag images in the product catalog based on their features. This way, online retailers can focus on more meaningful tasks instead of repetitive manual image tagging prone to errors.
These algorithms speed up the tagging process, automating it and removing the need for human intervention, thanks to Deep Learning. This is a method that creates the metadata of catalog items. It operates in a way that examines the image and identifies characteristics linked to specific keywords.
The Deep Learning algorithm processes the images' components, selects their attributes and discovers the specific objects. An automated product tagging system has the ability to improve the processing time of an image database by up to 90%.
Advanced image algorithms enable completing the entire tagging process in just a day, eliminating days and weeks of an average worker's commitment. That is why this technology has been embraced by many e-commerce shops, making it an integral part of their online sales strategy.
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As for the image format, here are some tips on how to prepare them for the visual search process:
Preparing your image database is crucial to optimizing your e-commerce store for visual search. As the manual image tagging process can be very exhausting and time-consuming, automatic tagging is a more painless option. After incorporating Pixyle.ai's automatic tagging system, you can organize your entire image database in just a few hours.
Now, you're all set for visual search optimization. The Pixyle.ai visual search engine will help you create a seamless conversion route and a more customized consumer interface. Make your products easy to find, maximizing customer engagement. Most importantly, keep users happy.