Today Artificial Intelligence is more than futuristic concepts and sci-fi movies. It is actively used in our daily lives, and simply refers to a machine or program’s ability to learn and think for itself.
Since AI has been empowering many of the world’s top technology solutions, it was only a matter of time before it came to e-commerce. Within E-commerce, AI is being applied to make the industry more and more customer centric. This is evidenced in the past purchased based sales predictions and product recommendations made by online retailers such as Amazon, in virtual personal assistants such as Siri and Cortana, AI enabled Chatbots, Smart Logistics and automated warehouses etc.
The need for visual search in e-commerce
With Ecommerce companies offering a catalog of thousands of products, customers are becoming more impatient in the buying process trying to figure out what they want in the shortest possible time. With this comes the challenge of making the search process short and seamless.
One of the important use cases of AI is in making search engines smarter. Whether it is making text search more semantic and conversational or improving the voice search features or performing search via images, AI is making groundbreaking strides in all of them.
Some of the e-commerce companies are investing heavily on getting all of the 3 search methods on their websites to make a more responsive search platform. With text and voice search basically being used for specification based products like electronics, visual search provides a newer and easier alternative for fashion and lifestyle products that are difficult to describe in words. For example, if you looking to find similar celebrity outfits and know of just the right keywords, you might get you a little far - provided, of course, that a blogger has mentioned the same outfit somewhere.However, with visual search combined with properly indexed images, you can find the right outfit in a single click.
Today, visual search has escalated the level of engagement that customers can have with e-commerce websites as well as the offline retailers. Whether that’s searching for a product page online or getting relevant product recommendations, smartphone apps are becoming more accurate and faster at predicting what you need.
Visual Search in online and offline retail stores has opened the door to an all-new shopper experience. It allows users to scan images of products they may be interested in, whether it’s on friends or in a showroom. Providing relevant and accurate product results in return ensures that users can shop at anytime, from anywhere.
The Relationship of AI and Visual Search: The Technology behind
Visual search happens to be a relatively recent trend. This is because of some recent advancement in technology. Recognizing a pair of shoes photographed in a studio can be easy, but matching it with a pair worn by an individual in a camera image requires acute image recognition skills that, until now, were not available.
Visual Search is built using Deep Neural Networks, a subset of machine learning in AI . Deep Neural Networks are in turn built as a replica of the Neural Networks of the human brain. In simple words, Deep Neural Networks make computers intelligent enough to cluster and classify information which may be text, images or video like humans do using their biological neural network.
Let’s take an example of a sofa. To make the machine/computer understand a sofa via deep learning, it is fed with at least a few thousand different pictures of sofas. The algorithms are designed to extract features that collectively put together make a sofa i.e. multiple seating area, back rest, arm rest, back cushion, side cushion etc. So, the next time you input a picture , the computer will give an output of whether or not it’s a sofa.
In fact, if you post the complete picture of a living room, it will individually identify the different objects you trained the deep neural networks on like sofa, chairs, coffee table, rugs etc. Furthermore, the technology is also adaptive. It recognizes a user’s search pattern to provide accurate purchase predictions.
For example, once the picture of a shoe is uploaded, the results will show different types of shoes with a similar style in the first set. If the user clicks on the an athletic shoe, the machine deciphers the user’s interest in athletic wear and will provide more results from the same category.
By providing accurate results, deep learning technology in online retail ensures that users find exactly what they are looking for in the first or second iteration. This creates delightful user experiences, leading to an increase in conversion rates.
In today’s computing world, Neural networks and deep learning cater the best solutions to many problems in image recognition, speech recognition, and natural language processing.
E-commerce companies adopting visual search
While there are many brands in the market that rely on artificial intelligence to procure trends data and analysis, few have partnered with tech firms to enable smarter search.
With Pinterest leading the way last year, companies that mine consumer data use visual discovery and optimization in some form. They understand the hidden context behind these images and try to ascertain contextual networks between products. Since the search feature is immensely helpful, customers end up taking and uploading pictures frequently.
A popular fashion e-commerce app called ASOS uses visual search in a huge way. For the company, about 80% of its traffic and 70% of the sales come from mobile devices. This means that it’s a popular platform on which to innovate. Visual search is just the beginning for ASOS, which plans on expanding the tools available to its customers. They want to take advantage of the need for finding a dress similar to one you may have seen online (celebrities, influencers, friends, etc.).
Visual search has become one of the most successful technological integrations into the online market, boosting the effectiveness of e-commerce companies on a global scale. With the rising economy and the emphasis on digitization, it definitely holds a strong promise. Today, companies are looking for product differentiation through tech, and visual search advancements offer just that.
What are your thoughts on Visual search in eCommerce? Write to us at firstname.lastname@example.org and check out our Visual Search here.
“Turing Analytics is a machine learning company that delivers intelligent solutions to Retailers helping them improve product discovery, customer engagement and boost conversion rates. They expertise in building Visual Search & recommendation solutions, which enable retailers to recommend visually similar products to their customers and search products by uploading a picture.”