Click, Find, Buy – Visual Search in eCommerce is Here to Make Your Life Easy!
When you are on holiday, it’s hard to resist looking at all the other people who have worn the best holiday clothes, sometimes you wish you had a way to get the exact same ones. Visual search solves this for you.
Simply click the picture of clothes, accessories, or objects you like and let the search engine find some similar ones for you and suggest websites where you can buy them. A simple three-step process – Click, Find, Buy and your shopping is done!
All the leaders in the eCommerce Industry have said that visual search is the ‘next big thing’. People don’t like typing anymore but they love snapping photographs. If an eCommerce website allows its users to upload pictures and identify the exact same product or recommend similar products the sales are bound to skyrocket.
Pinterest is Trending with its new Visual Search app – Lens
Pinterest states that 85% of all their searches are done on mobile devices, and 87% of their users bought something they found in the pins.
People browse on Pinterest to know about new trends, find some useful things that make life easy. If they can buy what they see, it is going to be nothing less than a shopping revolution.
How do you experience this unique visual search feature on Pinterest? Well, it’s simple, just download their application called Lens, take the picture of the product you like and buy it on the Pinterest recommended websites, or save the picture on your Lens board for future purchases.
Shopping for Home Decor? Here is How you can Make it an Unforgettable Experience for your Customers
First, let’s watch an episode of our favorite sitcom FRIENDS together –
In this episode of Friends, Ross is shopping for a couch with Rachel. The scene where he annoyingly said, “I’m sorry I just want to make sure I bought the right couch. I need a couch that says, ‘kids welcome here,’ but that also says, ‘come here to me…’ for the ladies.”
Well, your customers may have different intentions unlike Ross, but wanting the furniture that’s designed in their minds is all that they are looking for.
Every small detailing is framed in their minds and you might show them all of your collection but if it does not match what they want then it’s a waste of time.
Personal preferences have to be held high by every brand and for that Visual search, navigation is a much faster and easier option. It suggests to users what’s best suited for them based on the images of furniture they like and allows them to purchase it from websites that have those kinds of stocks.
Here is How The Biggest eCommerce Giant, Alibaba is Utilising Visual Search to Shark-Peak Sales
Pailitao, the Visual Search app by Alibaba had over 10 Million customers within a year of its launch.
Whether you are watching television, eating at your favorite restaurant, or just taking a stroll in the park with your friends, you can click images of the products you like using Pailitao and find similar items on Taobao, Alibaba’s online shopping website.
Taobao has billions of products listed in every product category like fashion, apparel, shoes, bags, makeup, food, furniture, etc.
Shoppers at Taobao seemed to be particularly interested in purchasing accessories worn by celebrities or influencers in photos. Now they can find those items on Taobao’s website and purchase them in a jiffy.
Artificial Intelligence (Computer Vision) Based Visual Search
Big data regions in the field of computer vision. The pool of data fed into the system has the ability to predict, this is what makes an AI project super successful.
A typical visual search application is similar to facial recognition, it is like the moving force for the evolution of visual search engines.
Machine Vision applications are fed with millions/ billions of images of one particular category. Machine co-relates the visual searches to all the images that are labeled accordingly in its bank and picks out the one that is almost identical.
Data Annotation in Visual Search
Computer Vision models can be used to develop robust methods for recognizing objects in images. However, these models need large amounts of training data. All the images trained have to be precisely annotated for the machine learning model to identify them.
Image annotation is a technique to attach labels to an image. This can range from one label for the entire image set or numerous labels for every group of pixels within the image.
The annotated images are referred to as the ground truth data and fed into the computer vision models to power visual searches. For example, if the images of handbags are not labeled as handbags then no visual search engine will be able to identify that image.
So it’s of utmost importance that intricate data annotation is done on images before they are fed into the system.
Manual Vs Crowdsourced Vs Automated Data Annotation
Think about this there are billions of images to be annotated, getting that done manually is time-consuming and costly. But the irony is it is still not a reliable annotated data set.
Crowdsourcing of data labeling is assigning the labeling task to freelancers around the world to complete the task of data annotation. Many projects break down the task of labeling into micro-tasks and assign it to different individuals to manually complete data labeling.
Automation solves the problem by annotating billions of images in the shortest time with minimal human assistance and gives you a reliable training dataset to build machine learning models.
The way-forward is adopting automation, that too industry-specific one. Labellerr is a retail and CPG and eCommerce focused, highly accurate, and smart data annotation platform with machine learning capabilities.
Sign-up here and start labeling by connecting your data from cloud/edge such as GCP, AWS, Azure to integrate computer vision AI on eCommerce
Reach out to us to know more about how you can implement this solution for your business. With our plug-n-play, API solution businesses can customize according to their scenario easily and swiftly.