65% would like to scan items and see product information on phones, 50% want contactless payment options, and 20% are ready to use augmented reality magic mirrors to try on clothing.1 Such advanced and immersive technologies powered by the internet of things (IoT), sensors, and analytics can help retailers bridge the gap between in-store and online experience while providing troves of real-time data to drive business outcomes such as improving customer lifetime value (CLV) and increasing sales and conversions. But many retailers are unable to seize these opportunities as they struggle with the fundamentals of checkout experience such as performance and availability issues resulting in long wait times, and lack of online like digital capabilities such as product locator, rich product content, and in-store personalization.
To achieve a rich, high-performing, and uninterrupted in-store checkout experience, the store infrastructure must be equipped for heavy processing and handling large volumes of data transfers to the central instance.
This white paper discusses how edge computing can give retailers a strategic advantage in the highly competitive retail landscape in enabling a rich in-store experience. It also explains how retailers can realize edge computing for checkout with lightweight, open-source technologies.
CHALLENGES WITH LEGACY SYSTEMS AND TRANSFORMATION WITH CLOUD AND MICROSERVICES
Besides impacting the time to market (agility) of new features, it resulted in a heavy and complex store hardware footprint, requiring high manual intervention and maintenance costs, and also special tools and software not just to distribute and deploy new releases but also to monitor infrastructure across the retail estate. Also, new releases required heavy change management and cutover activities at stores, impacting the time to market.
To simplify store infrastructure, reduce the high upfront infrastructure and maintenance costs, and be agile, several retailers have shifted to modern architecture with microservices and cloud technologies that can be scaled on demand and offer high availability with near-zero maintenance needs. But cloud architecture poses a fresh set of challenges, primarily around performance caused by network latencies between stores and the cloud, and availability. This has a direct bearing on customer experience, brand value, and profits of the retailer.
The key limitations of cloud architecture for in-store are:
Resiliency: Downtime due to service and network availability
Performance: Network latency, causing speed issues
Cost: High stress on network and bandwidth needs
Digital: Lack of digital capabilities at stores limiting online-like rich, personalized shopping experience at stores
NEXT-GEN CHECKOUT EXPERIENCE WITH EDGE COMPUTING
Leveraging edge computing as a complementing technology to cloud computing not only saves time and money, but also solves latency, bandwidth, autonomy or compliance issues (see Figure 1).
With edge computing, the central cloud capabilities are extended to the edge by leveraging a central container-based orchestration platform that can seamlessly manage the edge nodes (network and storage equipment), and, thereby, software rollout/upgrades and associated configurations. Edge computing can handle applications and workloads for thousands of store locations by running computational power through nearby edge nodes rather than risking data transfer speed and bandwidth challenges by accessing all services and data directly from the cloud.
With edge computing, retailers can realize scalable checkout service that can support multiple channels and also deliver next-gen in-store experiences (see Figure 2) with key capabilities such as rich product content, visual search, voice assistants, contextual recommendations, fraud detection using computer vision, and cashier-less autonomous checkout.
EDGE COMPUTING WITH LIGHTWEIGHT, OPEN SOURCE FRAMEWORKS
This can further be extended to enable a high-performing, personalized digital checkout experience at stores with resiliency. Though there are a few edge solutions such as AWS Outposts and Snowcone (Amazon), and Azure Stack Edge and Azure Arc (Microsoft), these are all more focussed on the IoT edge architecture and require proprietary hardware and software platforms, resulting in high costs and vendor lock-in.
KubeEdge, which is a scalable, flexible, and lightweight open-source technology that can support heterogeneous platforms is a good alternative to the proprietary high-cost solutions for edge computing. It can extend native container orchestration (Kubernetes) and device management to the edge and provide core infrastructure support for networking, application deployment, and metadata synchronization between cloud and edge.
When evaluating edge architecture options, retailers must consider several factors:
Can the cloud capability be seamlessly extended to the edge?
Does it enable centralized app distribution and management?
Is the edge architecture scalable to thousands of store locations?
Can services be deployed, upgraded, and rolled back seamlessly?
Does it have built-in monitoring and self-healing capabilities?
Does it require proprietary hardware or software?
Can existing store hardware (virtual machines or the physical ones) be attached to the edge cluster to act as the edge gateway?