With the introduction of Pinterest Lens and Google Lens, a fight for visual search engine dominance has erupted. Visual search could totally change customer preferences and buying choices, in addition to providing a new sales source for e-commerce stores.
In today’s world of quick gratification, visual search will pave the way for “snap and surf” shopping by streamlining the search interface. This bodes well for e-commerce stores that create product listing ads (PLAs) and online catalogues for the visual web.
Though it is still in its infancy, optimising for visual search could significantly boost the user interface, conversion rate, and online traffic of your website. Images, on the other hand, are often paid very little consideration by SEO researchers, who usually concentrate on optimising for speed rather than alternate qualities and appeal.
Although visual search would not replace keywords or the relevance of text-based search, it has the potential to fully disrupt the SEO and SEM industries. I’d like to go through some of the basics of visual search and how they can impact our digital marketing approach in the future.
What is visual search?
Huge search companies are now using three separate visual search processes:
Textual questions are used in traditional image search.
Structured data is used to determine related characteristics in reverse image search.
Image searches on a pixel-by-pixel basis that allow you to “snap and scan” by image or by sections of an image.
In this blog, I’ll mostly concentrate on the third kind, which helps customers to find information or items online by merely posting or snapping a photo and concentrating their question on the portion of the image they’d like to study. It works similarly to text search, but with an icon reflecting the query that is being matched to it. TinEye created the first visual search tool, which is still in use today. This type of image search compared the image to other photographs on the web based on similarities such as shapes and colours. TinEye, on the other hand, offered a small variety of search applications by failing to trace out the outlines of various objects in images.
Image recognition technologies today can identify various shapes and outlines within a single image, allowing users to fit to different objects. Microsoft’s image search technology, for example, helps users to search for individual objects pictured inside a wider image.
Microsoft is now focusing on identifying whether a chosen component of a picture has a shopping purpose and displaying “related products” in these cases. Unfortunately, Microsoft’s visual quest is limited to a few industries, including home appliances and tourism.
This technology is currently limited. Pinterest, Microsoft, and Google are both investing in a visual search platform driven by machine learning and deep neural networks.
The aim is to teach robots to recognise various shapes, sizes, and colours in pictures in the same way as the human brain does. We don’t see a sea of points and dashed lines when we look at individual images. Based on previous encounters, we automatically recognise patterns and forms. Unfortunately, we really don’t really grasp how our brains process pictures, so scripting this into a computer poses some obvious challenges.
To develop their process, visual search engines have come to rely on neural networks that use deep learning technology. Companies like Google profit from their abundance of data, which encourages their Lens framework to continually refine its search capabilities. Google Lens does not only recognise different objects within images, but it can also match them to nearby sites, provide user feedback, and sort listings using the same rules that control its own search algorithms.
Future of Visual search
But, what are the implications of this technology for consumers and businesses? Imagine being able to take a photo of a restaurant and have a search engine tell you the name of the restaurant, its venue, peak demand hours, and the night’s menu specials. This system may be used to take a snapshot of a pair of shoes in a magazine or from a stranger and order them right there.
Visual quest places users really high in the funnel for e-commerce stores. You will entice shoppers to make a purchase decision on the spot by using exclusive photographs, product ratings, and a good product summary.
This will also help to broaden the spectrum of competition. Pinterest’s visual search engine is without a doubt one of the most disruptive on the industry. However, Pinterest’s search engine just redirects pinners to Pinterest pages, so you’ll need to have a presence on this website to meet such community members.
With the emergence of voice search and natural language processing (NLP) following this trend, this technology may help jump-start the interface-free SEO trend. (However, I believe that keywords and text-based search will continue to be essential, even for shopping and purchasing decisions.)
Visual search would create a fresh revenue source for e-commerce shops while also greatly improving the shopping experience for users. This will have a significant effect on SEO and paying advertising, reintroducing a renewed emphasis on image optimization, which has long been overlooked by SEO practitioners. This latest frontier in search will only strengthen current SEO tactics and highlight the importance of optimising for mobile search and your visual web presence.