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5 Ways Visual Recommendations are transforming online shopping

The rise of social media has made us more aware of the power of images in our daily life and how it influences behavior. Recently, academics measured our brain function when we appreciate something beautiful. Their results “suggest that aesthetic appreciation may represent a hedonic feedback over learning progresses, motivating the individual to inhibit motor routines to seek further knowledge acquisition.” This means visually beautiful images will make shoppers excited and happy, stopping them from shopping for alternatives! So how are eCommerce platforms incorporating Visual Recommendations today? We explore 5 ways below:

1. Going down the Rabbit Hole

The rise of social media as a shopping experience has been hard to miss. Pinterest, Instagram, Alibaba, Google Shopping, Snapchat and others offer a visual experience embedded in our everyday lives. This exists as a place and forum of inspiration for shoppers. Through offering a way for users to post, share and search using images, customers will be engaged at a deeper level and mimic the experience of a social media channel. When you show visually similar products to a customer, you can present them with enticing options allowing them to follow their interests in an easy manner, as if they were in a physical store. Eventually, they will go down the rabbit hole of more intriguing and enticing products. 

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2. Lost in Translation

The world is filled with a variety of languages expressing our desires and needs but even for most global companies, only so many languages can be catered for. Visual Recommendations are opening our world to products from every corner of the globe. Don’t know your kimchi from y our kheema? With computer vision there is no need. Many online Chinese retailers have started to use computer vision models to tag and display the products they sell. Using these tools along with standardized check outs and baskets, they have made enormous into foreign countries without speaking a word. 

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3. Contemporary or Modern?

When you need to arrange your products by category, we refer to the Attribute Extraction; what style and category best match the product. Often times, textual data can be noisy and the taxonomy for products will change. In practical terms, a rough grouping of images would be created for an editor or stylist to view. By reducing the poor quality images, the editor can be presented with more closely identifiable and manageable sets of images and by merely checking yes/no and using the fine-grained skills of a stylist, a unique style library can be created.

Technically, a model would be trained on two labels of every image: category and style. A pre-trained model can be used to create the embeddings and from that, you can build two classifiers. In the final step, you can combine the objective function into one. It can be similar to a K-NN model using visual embedding and voting model to improve accuracy.

From the table above, it is clear that the best style accuracy comes from the model that learns both category and style and so, learning to differentiate categories helps to learn style. 

4. Is it me you're looking for?

Most customers have extremely detailed preferences for the features of a product they are looking for. These needs are often very hard to describe with words. Offering Visual AI functions will allow the customer to search for recommendations and filter products based on their own preferences. A computer vision model will struggle to find minor differences but will be able to present the customer with just the right combination of similar items to reveal precisely what they want through an iterative process. The customer will be able to create their own personalized experience each time they shop. 

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5. Eyes Shut!

Some eCommerce retailers are similar to malls we visit offline. They offer relatively open platforms where third parties run shops and post images of their products or services. What happens when offensive images are posted on your site though? How do you sort through millions of images to find out which ones are aligned with your brand values? Machine learning will be able to help protect your brand. By learning what logos and brands are not acceptable and weeding out inappropriate images and content, the products recommended will align more closely with your brand values. 

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With the above, implementing Visual Recommendations to your eCommerce platform will help boost conversions and gauge more brand loyalty from customers as well. Presenting the relevant and suitable products to customers will pinpoint their desires and boost the credibility of your business as well. 

Get in touch with our team today to understand how you can utilize Visual Recommendations within your business. 

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