» How do shopping start-ups use visual AI?

How do shopping start-ups use visual AI?

By Nagi An
— June 15, 2022


Successful shopping start-ups constantly keep up with consumers’ ever-changing needs to run their businesses. One way they do this is to use artificial intelligence to improve their customers’ shopping experience. 

AI is an indispensable tool in retail shopping. According to Statista data, the use of AI in the retail industry was estimated at USD 3 billion in 2019. It’s calculated that the market will reach USD 23.32 billion in 2027. The fashion market is an important segment in the retail market, and per a report released by Reportlinker, the global AI in the fashion market was valued at USD 419.70 million in 2021, and it’s expected to reach USD 1.22 billion by 2027.

Visual AI: computers with eyes

Visual AI is an application of artificial intelligence which is particularly related to the retail market. This technology trains machines so that they can “see” images. Through this training, machines learn to interpret the visual data similarly to humans and can make recommendations and decisions accordingly.

An important application area is face recognition, like the one used in mobile phones’ face identifications. Facial recognition is also applied in offline stores to enhance personalized consumer experiences and prevent fraud. Another well-known use case is Amazon Go stores, where visual AI reads customers’ movements to enable quick cash-outs.

Technologies used in self-driving cars also leverage visual AI. Another critical use case is medical image analysis. 

How is Visual AI used in retail shopping?

Shopping start-ups and retail companies use visual AI to create better customer experiences by improving the visual search process and displaying personalized product recommendations. 

Scrabble wooden pieces forming the word Search

  • Visual Search 

Imagine you liked a product you came across somewhere and went online to search for it. If you search using keywords, you’re limited by your knowledge of the product terminology. On the other hand, if you have the image of a similar product you like to buy, it’s much easier to find it.

Visual search allows you to upload an image via camera to an online store or screenshot it and find visually similar products. Since it’s a very desirable feature of the customer’s shopping journey, companies are investing in it. 

For example, Pinterest has recently acquired The Yes. This AI-powered shopping start-up had been developing visual search capabilities based on data science in order to predict fashion trends. Pinterest is one of the greatest visual search engines in the world and with this move it strives to provide the customers with a frictionless purchasing process from search to shopping. 

  • Product recommendations

Thanks to visual AI, retailers can combine customers’ data with products’ image data and display related product recommendations based on what they already liked. Product recommendations help increase conversion rates and boost sales. Personalized product recommendations account for more than 35% of Amazon’s sales, and 75% of customers are more likely to buy based on personalized recommendations.

In their journey to offer better product offerings, start-ups can build their in-house AI. However, they can also consult the expertise of third-party AI solutions. For instance, pixyle.ai is a company offering a product recommendation engine. Other solutions are syte.ai’s hyper-personalization suite and intelistyle’s styling framework. 

For example, the online rug shop Revival uses stye’s visual discovery suite to break down the items within an image into detailed visual attributes and suggest related products from their catalog. Thanks to this integration, all Revival products have detailed color and motif data assigned to them. As the company’s CEO stated in an interview, keyword search alone isn’t enough to pick up on common features between different products. On the other hand, visual tools can pick up on specific colors and designs more closely.

  • AI-driven product tagging

In all the applications of visual AI, automated product tagging plays a crucial role because it helps build an extensive search database. As a result, shopping start-ups are able to make detailed and highly personalized recommendations to their customers.

Conventionally, retailers tag their products manually. Some retailers have several thousands of, if not millions, items in their inventories. That’s why manual tagging is a very time-consuming process and is also prone to human errors. If the products are not tagged accurately or with enough details, consumers searching for them will never reach them. As a result, there’ll be a significant loss in the company’s sales. 

An automated product tagging powered by visual AI, on the other hand, helps the company improve its inventory management since purchasing managers can make informed decisions on which products to add to the inventory. 

AI-supported tagging also improves shoppers’ textual search experience. The results are more accurate, catalog navigation becomes more manageable, and there are smarter product filters. As a result, companies can make improved personalization offerings that play an important role in purchasing decisions, as mentioned above. 

  • The path to omnichannel

Visual AI is not only for online shopping. In fact, one of its strengths lies in the seamless integration of online and offline stores. Start-up executives can use visual AI to analyze the data and manage physical stores more strategically. 

Final remarks

As our digital world becomes more and more visual, consumers prefer to experience frictionless buying processes, from image-based searching to purchasing. We also know that personalized product suggestions have a decisive role in buying decisions. And on the other hand, companies must do everything possible to improve the inventory management processes to keep up with the fast-paced shopping industry. Visual AI is a powerful technology that can support both parties. 


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