As digital and brick-and-mortar shopping experiences continue to merge, image recognition technology solutions are finding their way into not only the hands of tech-savvy consumers, but also those of the retailers that serve them.
This article is part of ‘The Digital Consumer Report‘, a collaborative report by PYMNTS and Samsung Pay that demonstrates how modern merchants use AI and deep learning to bridge the fabled gap between digital and brick-and-mortar commercial channels, creating an all-encompassing, omnichannel shopping experience.
Many use cases have emerged in the last decade, including shelf management. These solutions can aid retailers — particularly brick-and-mortar businesses — in keeping an up-to-date and accurate account of products’ shelf availability, helping them better serve their customers.
Adding this technology to a company’s roster of solutions is often easier said than done, however. It typically involves a long process for those that have not already adopted, one marked by many opportunities for error. That potential for error can magnify exponentially for large retailers with equally large inventories.
Now, image recognition solution providers like Trax are beginning to emerge, working to help retailers adopt and integrate the technology into their operations. Based in Singapore, Trax provides AI- and machine learning-supported image recognition technology to retailers looking to streamline in-store product management. The goal is to use that technology to track product availability and reduce the number of stock assessment inaccuracies due to human error.
In a recent interview with PYMNTS, David Gottlieb, Trax’s general manager of global retail, discussed the company’s vision for digitized brick-and-mortar retailers, including how image recognition and AI can be harnessed to streamline shelf management. Shelf management could not be done frequently before solutions like those offered by Trax and others in the space, Gottlieb explained, largely because it required a manual and labor-intensive process.
“At some point during the day, you’d have an associate walk through the store looking for holes and then maybe scanning them with a scanner,” he explained. This often ate up substantial chunks of time, particularly for retailers with large stores or a wide range of inventory. Trax’s system aims to cut down on the time required to perform shelf management tasks by using a camera for real-time shelf monitoring. It relies on AI-based image recognition to identify changes in stock, helping ensure that retailers obtain an accurate account of which products they have to sell or need to reorder.
Those images can be used for more than just shelf availability information, however. The pictures are also stored to provide a large well of customer and product information, Gottlieb explained, which can then be used for analytical purposes.
“It is not just the image recognition technology we are offering,” he said. “We are storing data on multiple levels — raw data generated from the images, the key performance indicator data, and then meta data, the master data.”
With these use cases in place, image recognition technology can also enhance a brick-and-mortar retailer’s ability to properly assess which customers are buying which products, thus gathering the information they need to determine their future focuses.
If a retailer wants to maximize profits, we can stack or rank the items which have the highest value to the business, or the highest sales velocity.
“[We can provide information on] whatever the retailer is most interested in optimizing,” Gottlieb added.
Trax’s vison for the future of digitized retail is not entirely supply side-centric, though. The company also plans to offer AI-supported image recognition so customers can optimize their shopping experiences. One way to accomplish this is to bring an element of digital into the brick-and-mortar store, allowing customers to “search and filter,” as with a search bar, through items in a real life retail location.
To that end, Trax is currently working on expanding its service to a mobile app customers could use to obtain real-time information on a desired product’s availability. They could then “know where the items were [in the real store location], and they would know the content of the items,” Gottlieb explained. These mobile app development efforts are part of a larger vision, he added — one to build on the foundation of an AI- and machine learning-enhanced “digitized store.”
This makes Trax yet another example of how companies are noting the importance of investing in digital capabilities and, in this case, image recognition technology. Retailers are hoping to capitalize on AI and machine learning to make the consumer experience more seamless and omnichannel-capable than ever, thereby offering consumers the connected and convenient shopping experiences they crave.