Machine Learning and Image Recognition for eCommerce
By Ofer Fryman • October 06, 2017
In eCommerce, pictures are worth more than a thousand words, especially when you consider how machine learning, AI, and image recognition are being applied to each individual image. If you’re an online retailer, you must be aware of these digital trends that are reshaping the industry. These technologies provide systems the ability to learn and improve from experience without being programmed, and were previously utilized mainly in social networks, and now they’re everywhere. It is all in an effort to better commercialize images that are being uploaded.
This means product images can now be shoppable. You can go on a fashion website, and shop from any image. You could also draw inspiration from merchandise you see in storefronts, and shop from your phone by posting photos. The same logic applies if you see someone wearing an outfit that interests you, and you would like a shop a similar style for yourself as AI tells you where you can purchase that exact item, or at the very least something similar. The following video shows an example of that.
This technology is currently being used most by online retailers and publishers that are in the fashion scene, but it is slowly making its way into homeware, gadgets, and other popular categories in eCommerce.
Media companies and publishers are particularly enthusiastic about using machine learning and AI because it turns all their images into shoppable ads, including those in print because images can be snapped to purchase products. This is a lifesaver for the magazines that are faring far better online than in print, because it could potentially generate a greater return on investment for them.
AI and machine learning applications are especially useful for e-tailors that look to leverage the holiday season (or multiple holidays throughout the year.) That is because their consumers seek the best shopping experiences during that time, and expect businesses to meet their demands. It’s not for nothing that malls across the US are closing or in the danger zone. Customers are losing interest in leaving their homes to drive to malls, deal with masses of people, and go through piles of disorganized merchandise. Machine learning helps users see their full range of options in a glance, without the hassle or wasted time.
With shoppable images, online shoppers that know what they are looking for no longer need to face the task of coming up with the right search terms, or scrolling through multiple pages of inventory in vain. The reason that attempts at augmenting the keyword search experience with natural language hasn’t really taken off is because shopping is naturally a very visual experience.
Deep-learning is another element that enhances the eCommerce experience. According to Babak Hodjat of Sentient on deep-learning,
Auto-encoding features of images in an inventory based on similarities and differences brings about a rich model of what is available in the inventory, and the model is surprisingly close to how we as humans perceive shoppable items. The model alone, of course, is not enough: We need a way to understand a shopper’s preferences as they interact with the inventory.
Another AI technique, called online learning, can be of use here, where sites are able to analyze every click through an online inventory in real time to understand customer preferences and create a personalized shopping experience. Obviously, other non-visual aspects of shoppable content, such as price, size and match, must also be taken into account, helping to weight the visual models toward user preferences.
The benefits of machine learning in eCommerce, according to eCommerce Nation include:
Increased online sales conversion
Reduced customer support costs
Improved customer and brand loyalty
Improved buyer experience
eCommerce and visual commerce have both rapidly accelerated in recent years. According to a recent report from the US Department of Commerce, this is a large reason why e-commerce sales brought in $394.9 billion in 2016. It is expected that double-digit growth will continue through 2020, when sales will top $4 trillion. The advances in machine learning and AI is making images (even videos) interactive and direct conduits for making online purchases.
Now is the time to embrace the power of machine learning and image recognition for eCommerce.
Today, we’re happy to share that Syte raised $21.5 million in Series B Funding. Viola Ventures lead the investment, alongside high-profile investors Storm Ventures, Commerce Ventures, Axess Ventures, and Lyra Ventures. The total funding to date is $30 million.