Textual information is boring, and many retailers have started using visual recommendations for e-commerce. If an online store has thousands of products, text searches and filtering can get annoying. Not everyone has enough time to go through long product lists and compare their features. The best way to avoid the hassle is by using visual recommendations.
Visual recommendation engines use computer vision and AI. Today, nobody likes to read, and research has shown that our brains strive at image identification, rather than reading. The switch from text to visual media can be observed in social networks as well. Facebook and Twitter have the reputation of old-people stuff. Today’s youth uses Instagram, Snapchat, and TikTok, all based on images and videos.
What Are Visual Recommendations and How Do They Work?
Visual recommendations are a process where retailers try to display relevant products to customers based on images they use as a search query. These recommendation engines use an image recognition algorithm to analyze the input. After that, a personalized search tries to match the photo to e-commerce products from the store. The user now sees items similar to his input query without having to explicitly define specific details by filtering.
The content-based recommendation is about more than the uploaded image. It can take user preferences into account. Most online retailers gather data about their users’ shopping habits to display more personalized product recommendations. It’s called behavioral targeting. Together with a recommender system, e-commerce looks to provide a seamless experience for customers to find what they want.
Why Should E-Commerce Sites Use Visual Recommendations?
Visual recommendations are the future, and they are already helping out online retailers. The brand Forever 21 increased its average order value (AOV) by 20% after adopting a recommender system on e-commerce platforms. Here are a few advantages of using a recommender system in e-commerce:
- Introduce your e-commerce products to customers
A crucial aspect of online shopping is making your customers see a wide range of your products as soon as possible. In some cases, shoppers know what they want, but they can’t find the words to express it. That’s when your recommendation engine comes into play.
Let’s say someone is a Kylie Jenner fan and wants to dress like her. Using a recommender e-commerce system, you could upload a photo of Kylie dresses in specific clothes. The recommendation engine should display items similar to what she’s wearing. Also, users might not even be able to explain some products using words that are only familiar among fashion designers. The point is customers don’t have to explain anything, they just point at a picture and say: “I want this!”
- Speed up mobile shopping
People don’t have hours to spend looking for products online. If a customer wants a watch with a steel bracelet, a black background, and minimalistic hour markers, your filters and keywords might not be able to display similar products. But, if customers know a similar watch, they can upload a photo and find similar ones in no time.
It leads to a simplistic mobile design. Online shopping on a mobile device is often challenging. Filtering is often slow on mobile websites with product lists refreshing all the time. With visual recommendations, a customer is within a few clicks away from their desired product.
- Get to know your customer and offer better results
After several successful uses of your visual search engine, you can use behavioral targeting to generate a personalized search. You can identify features your customers prefer, like color, pattern, and style. Instead of settling on selling a single item, you can recommend a wide array of products that might suit your customer. Recommender systems in e-commerce are crucial in making customers buy more than one item.
The ultimate goal is to keep your customers engaged. Amazon offers a prime example, with great suggestions and offering bundles of several items often purchased together. It’s a strategy that helped Jeff Bezos cross the $200 billion mark!
Top 3 Visual Recommendation Engines
There are almost no visual recommendation engines as open-source solutions. Still, if you want to take your store to another level, you should consider these top-class services:
SalesVision offers AI-powered recommender systems for e-commerce. It enables site visitors to upload any image, and its recommendation engine will find similar items in your online catalog. On top of all that, customers will receive content-based recommendations and a personalized search platform. SalesVision offers retailers to try out their features for free, while only paying the use of servers that tag user-uploaded images. Their pricing is flexible and affordable. What makes them stand out from the competition is the personalized and unique approach to each customer.
SalesVision offers a 95% accuracy rate, which beats any recommendation system in e-commerce. AI algorithms work in the cloud with advanced image recognition. There is no need for manual image cropping, and any image is supported. An important aspect that sets it aside from the competition is the attention to detail in images. In the fashion industry, the smallest features separate one product from another! SalesVision also offers a Chrome plugin that suggests visual recommendations from your store while users are using Google to look for items.
SalesVision uses fashion tagging to extract important information from a photo. For example, if you upload a photo of a girl wearing a white top, it needs to tag the top according to its characteristics. The important features are sleeve length, collar, color, etc. Once the image is tagged properly, SalesVision’s AI algorithm will check your store for similar items and display them to the user.
Syte.ai works similarly: users upload a photo, and the algorithm tries to match it to items in your online inventory. It offers a recommendation engine as well, to keep shoppers on your site longer.
Syte.ai offers automatic deep tagging features for a wide array of products. Even after the image upload, Visual Navigation enables customers to navigate through the list of recommended items and narrow down their search.
Slyce.it offers the mobile Visual Search SDK. Apart from the visual product recommendation system for e-commerce, it also provides barcode and catalog scanning. It integrates into mobile apps as simple as a camera button. They are more mobile-oriented, but Slyce.it also offers web store integration and other useful features to retailers.
Featuring a recommendation system for your e-commerce platform is the perfect step forward for your store. Every poll suggests that the majority of online shoppers want to see this in more online stores. Every e-commerce platform benefitted from implementing these AI-powered systems.
Few recommendation systems for e-commerce are open-source. But, investing in these technologies will pay off in the long run. Shopping trends in 2020 are destined to shift further to the Internet. The pandemic managed to raise awareness of the importance of online shopping. It’s up to retailers to take advantage of this and elevate their business to another level.
Visual product recommendations work using artificial intelligence. Like AI, they are improving all the time. Recommendation systems for e-commerce will improve on accuracy and provide a more seamless shopping experience in the future.