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Top 5 Innovative Computer Vision Use cases for Retail Outlets

computer vision in retail 1

Computer Vision plays an important role in everyday life – bar code scanners, cashier-less stores, facial recognition, inventory visibility, visual search, etc. are all examples of technologies that apply some form of Computer Vision. In this blog, we take a deeper look at computer vision technology applications for sales outlets and their importance in the retail industry.

Computer Vision is a technology that can surpass human visual abilities. It is a scientific discipline through which machines use images to make decisions about physical objects and scenes. The theory is related to building artificial systems that obtain information through images and multi-dimensional data. How computer vision works is that it acquires an image, processes it, and then understands or classifies it. Artificial Intelligence (AI) further acts based on the understanding of the image.

According to the ‘29th Annual Retail Technology Study: Retail Accelerates’, 3% of retailers are already putting computer vision solutions for retail to work and 40% plan on implementing it in the next 2 years.

The technology might still be in its infancy, but its application is increasing across several industries including retail. In sales outlets, it helps in enhancing operational efficiencies by creating a smooth shopping experience for customers. Retailers are beginning to experiment with Computer vision since it helps them to evolve with the ever-changing shopping practices of consumers.

Table of Contents

Computer vision powered innovation is being used in the following ways in retail outlets

    1. Cashier-less stores

    2. Inventory count and compliance

    3. Using facial recognition to reward customers

    4. Images turned into actionable insights

    5. Measuring footfall, traffic, and interactions

Conclusion

Computer vision powered innovation is being used in the following ways in retail outlets:

1. Cashier-less stores

Computer vision in retail can bring an end to checkouts. Cashier less or ‘grab and go’ stores are one of the most high-profile applications of this technology. These stores enable shoppers to pick an item off the shelf, put it in their shopping bag, and walk out. There is no need to stop at the checkout counter and get the product scanned. The product just needs to be selected and the customer can walk out with it.

Amazon is the highest-profile use case of cashier-less stores today. Computer vision is being used to track shoppers in the Amazon Go concept stores. Hundreds of cameras are strategically placed across the stores to track customer movement. The sensors on the store shelves detect when an item is picked up. The Go mobile app, which enables devices such as iPhone, iPad, and Android phones to use them as ‘sensors’ to capture and share data such as location, situational information, and imagery, registers all the items in the shopper’s shopping basket. The checkout process is thus done away with completely, as the Go app takes money automatically from the customer’s account when they exit the store.  

2. Inventory count and compliance

Untold hours are spent in keeping the inventory up-to-date, and to maintain safety and merchandising standards. All this work is usually done manually by store employees. Managers need to constantly monitor and scan the store to ensure compliance. This approach costs thousands of man-hours, that can be put towards improving customer in-store experience.

Computer vision technology can take over these tasks. Robots can scan shelves for inventory and product compliance. Not only will this improve inventory accuracy but will free up man-hours for other important customer-facing tasks.

A good example of this is Tally – the autonomous shelf scanning robot used by Schnuck Markets, that captures real-time insights of on the shelf operations. The robot goes through the store 3 times a day, scanning 35,000 products on each route. Millions of products are scanned this way, giving Schnuck a more accurate and detailed insight into in-store operations and product flow.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

3. Using facial recognition to reward customers

Facial recognition technology can improve security and authentication in retail outlets. It also helps identify loyal customers as they come into the store. As they enter, a camera scans their face and a sophisticated algorithm sends their information to a sales associates’ system. They can then access the customer’s data to identify taste profile, allergies, preferences, and offer personalized product recommendations through AI-enabled analytics. The technology can assist in a customized shopping experience by going through the customer’s purchase history and past preferences. This can further encourage brand loyalty and help in converting infrequent customers into regulars.

Face scanning bio-metric technology can measure and match special characteristics to identify a subject. Facial-recognition software in devices such as iPhone X can change the way people shop. According to Forbes, more than 1 billion smartphones are going to have facial-recognition features in the next 2 years.

As shown in the image, Skyl implemented a similar Facial recognition system using computer vision that can be used in retail outlets to scan customers.

Facial recognition for customer reward© 2019 Skyl.ai - All rights reserved

4. Images turned into actionable insights

Computer vision in retail can turn actual images into actionable insights. They help in ‘digitizing the shelf’, i.e. give real-time insights into what is happening in the store. The instructions can range from filling an empty space in the store to reduce the number of the same types of products on the shelf next to each other and replacing them with other products. This ensures a better shopping experience for consumers and driving better sales.

Visual search is beginning to enter the digital retail world as it uses relevant images to get better search results. The function is either used to locate a product shoppers are searching for or to find a complimentary item to a product. The technology has a massive potential to impact online shopping as a younger audience is prone to use alternative search options to text. According to eMarketer, 62% of U.S and U.K millennials were found to be most comfortable using visual searches in their digital shopping experience.

5. Measuring footfall, traffic, and interactions

Sensors can help meet retail’s complex requirements. Not only does it count in-store footfall traffic, but it also adds other data such as capture rate of pass-by traffic and provides information on the shopper’s path around the store. This way the promotions that capture customer engagement are clearer than those that do not.

The technology doesn’t just monitor shoppers, but also provides information on customer and associate interactions, providing clear visibility on the store’s service engagement. The information can be used to drive personalized marketing campaigns for the store.

Conclusion

Technologies like Computer vision enable physical sales outlets to gather similar critical information like their online counterparts, leveling the playing field and making customer in-store experience worth the while.

Skyl.ai is an end-to-end Machine Learning platform, which enables enterprises to attain useful information from unstructured data by using Computer vision, Natural language processing, and Data labeling. With state of the art computer vision technology based on neural network architecture, we work with the automatic extraction, analysis, and categorization of information from images in an effective manner. We provide easy to understand guided workflows with  easy to follow computer vision templates.

Skyl.ai can solve challenges in retail with the help of Computer vision by monitoring customer buying patterns and helping in the process of product placement on shelves, which leads to easy discoverability and more sales. The process of retail inventory management can be made easier by Skyl’s computer vision powered solution that figures out items on the shelf that are running low. It gives predictions to send notifications on items that need restocking, thus reducing the cost of periodical checking and driving revenue with high product availability. Retail outlets provide customers with the hands-on shopping experience and Skyl.ai’s Computer vision platform plays an important role in digitizing that experience.

Check out our risk-free trial to build and deploy your own computer vision models.

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