Technology Transformation in Retail – Part II

For years now, many retail stores have gone through drastic technology transformations in order to stay on foot. Big brands such as Walmart, Target, Macy’s and Amazon have heavily invested in technology solutions to fulfill the constant needs the modern customer requires. This is the second of a 4 part series of articles about technology innovation in retail. In this series, we detail new trends and techniques being used by big players in the retail industry. You can check the other articles of this series in the links below:

• Part I: Machine Learning in Retail
• Part III: Mobile Applications in Retail
• Part IV: Beacons

Face and Object Recognition in Retail

The use of face and object recognition, or computer vision, is one of the most popular topics when talking about digital transformation in retail. From applications ranging from crime prevention to customer sentiment detection, there’s a myriad of possibilities to be explored and companies are just getting started.

Perhaps the most known example of the use of these technologies is the Amazon Go store. Using computer vision and a mobile app, the store is able to track all the present items, customers and what’s in each customers basket. Amazon is considering to open 3,000 Amazon Go stores until 2021. In China, where surveillance cameras are everywhere and the use of face recognition for security purposes is common, it’s already possible to make payments with only your face. The technology developed by the start-up Face++ is used by several apps to confirm the identity of the user, authorize financial transfers and provide access to buildings. Now, shops and restaurants seek after the technology to improve their clients’ experience.

Besides security and automation, computer vision can be used to better understand the needs of customers. Cameras can be used to detect facial expressions and discover what emotions people display when looking at a product or a shop window. Using entity tracking, it is possible to discover how many times people pick a product before buying or return it or which departments of a store are more visited and at what times.

Data collected using computer vision can be fed to machine learning models to make them more accurate. Additionally, the analysis performed on it can be the driver behind important changes in the way the staff deal with its customers, how and which products are presented or even in the decision of where a new store should be opened. In the next article, we’ll talk about mobile apps and how they can improve customer experience in retail.

About the author

Matheus Gonzaga is a Data Scientist at Poatek. He loves learning new machine learning techniques and is interested in all topics involving AI. His hobbies are tabletop RPGs, video games and climbing.