A majority of shoppers still prefer a physical store. In the US alone, nearly 91% of all retail sales are generated in bricks-and-mortar stores. Shoppers, however, expect stores to provide a unique and frictionless retail experience. Computer Vision and Internet of Things (IoT) technology promise a smooth shopper experience while increasing store efficiency.
Defining a frictionless retail experience
A frictionless retail experience eliminates all those processes or steps that negatively impact a pleasurable customer experience – for example, waiting in long checkout queues, products not in stock or not where they should be, pricing errors.
This entire endeavour culminates in the aisle where every customer is looking for an efficient, pleasurable and frictionless experience. The Future of Retail survey reports that almost 60% of consumers would shop at Amazon Go, a physical grocery store that allows shoppers to simply add items off shelves to their carts and walk out of the store without queuing up to pay.
With customers in the driver’s seat, physical retailers need to streamline the shopping experience and consistently exceed customer expectations to remain relevant.
Retailers are making early in-roads
More and more companies are looking towards digital transformation to remove friction from shopping journeys, piloting in-store technology like Internet of Things, sensor fusion and robotics.
US fashion retailer American Apparel, for instance, has deployed IoT and uses RFID chips to enhance in-store inventory management and maximize customer sales. Retail start-up Dandy Lab’s store of the future uses data generated from IoT to create personalized shopping experiences.
These are all encouraging signs, but there are still gaps in understanding what’s happening on the shelf. Retail is detail — it’s important to be able to understand to the minutest detail where products are and how up-to-date this information is. How can retailers and suppliers get the kind of real-time, scalable shelf data that can enable frictionless retail and doesn’t cost a fortune?
Keeping an eye on the shelf
Getting this ongoing shelf visibility is possible with a combination of Computer Vision technology and IoT. Latest advances in store management technology mean that you can continuously track real-time shelf conditions by analyzing shelf images captured by wireless IoT shelf cameras, autonomous robots and AR-powered mobile apps.
Watch this video of Trax’s store management solution in action at NRF 2018.
At a time when store labour has been cut to the bone, Computer Vision and IoT can help create a closed loop where the right people get the right insights, at the right time to improve operational store level processes.
When you consider the fact that 90% of retailers will implement buy online, pickup in store by 2021, it’s critical that existing HQ systems like supply-chain planning, warehouse management and merchandize planning all have access to one thing – accurate shelf data.
Addressing privacy and cost concerns
As the use of cameras becomes pervasive, physical retailers will have to be transparent in their data collection practices, respect individual’s security and privacy concerns. For example, Trax’s real-time solution features cameras that have in-built sensors to flag occlusions like human presence. Any shelf image that captures a person is immediately discarded, and the above process recurs until the camera captures a picture without personally identifiable information.
In-store IOT cameras are an investment, and the economics should work both for technology providers as well as retailers. At Trax, for example, we develop our own technology for the in-store IOT cameras to drive costs down.
So, what’s next for retailers?
Amazon has already thrown the gauntlet with its Go store. So if you want to perfect your retail basics, drive a differentiated brand experience and take your shoppers across the last mile with no friction, put shelf management on your technology roadmap now.
Prime movers will have the luxury of a learning curve period that is critical in understanding the true value of camera and sensor data to make better merchandising and marketing choices.