The Future Of Retail Depends On Real-Time Inventory Tracking
Shopping behaviors during the pandemic have accelerated the need for a unified customer experience across various channels. 59% of customers expect to receive consistent service on every channel. In order to win business and customer loyalty, shoppers require a hyper-personalized retail experience.
The same survey indicates that 32% of customers expect personalized content and product recommendations based on their past interactions with the business. Real-time data analytics gives retailers the power to monitor and respond to customer behavior in-store, on social media, or web browsing by implementing personalized ads and offers. But “real-time” requires processing data close to the customers, anywhere they happen to be.
This level of coordination and distributed computing adds complexity to retail development teams that are already stretched thin. Businesses that are unable to capitalize on real-time insights are the ones that get left behind. Retailers need to leverage the power of edge computing.
Edge computing in retail
88% of retailers either have deployed workloads or plan to deploy workloads at the edge. The goal is to bring data, computing, and storage near to customers, stores, distribution centers, and staff. This move to the edge of the network supports real-time analysis and personalization efforts closer to where data is generated.
But in-store edge servers require frequent maintenance and security upgrades, and the initial set-up can present logistics challenges. For a retailer with thousands (or even just hundreds) of locations across the country, it’s cost-prohibitive to deploy on-prem data processing and analytics at every location. While cloud computing is an option, millions of terabytes of data will need to travel over the network, adding egress costs and delays.
Edge computing can handle applications and workloads at scale by computing data through network nodes and data stores that are close to where the data is generated, eliminating data transfer delays to the centralized cloud. Many organizations deploy a hybrid model of edge, cloud, and on-premises data centers to deliver low-latency and the fast response times that Artificial Intelligence (AI) applications and real-time supply chains require - across all data sets.
With the acceleration of new technologies to draw customers to the store and online from AR/VR experiences to self-service technologies, the edge lets retailers develop and update new applications much faster. This allows retailers to offer new services to customers ahead of the competition while reducing resource costs. Additionally, shoppers and staff can experience faster response times on their website or mobile apps because of the close proximity of data on the edge.
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Process data close to customers to deliver a unified experience
AI/ML is required to accurately predict demand and even in some cases to create personalized experiences with computer vision and other technologies. Having data and advanced analytics at the edge lets retailers deliver mass personalization in real-time and compete on a level field with large e-tailers. Product leaderboards, real-time data analysis, and historical customer behavior data support dynamic pricing capabilities to let retailers set the best price as real-world conditions change.
Incorporating legacy back-end systems can be a challenge so many retailers are taking advantage of headless e-commerce and using APIs to build a seamlessly interconnected ecosystem for omnichannel experiences. It is much easier to build APIs that allow retailers to connect to all of their systems without worrying about syncing issues or migration. With headless e-commerce, retailers can easily provide an identity-aware customer experience across any channel with access to commerce, rewards, and promotion functions.
Real-time inventory and logistics
Inventory is the backbone of any retail org. Sales, profits, and customer satisfaction are all directly linked to effective and efficient inventory management. Retailers require global visibility — as well as the ability to support different local and regional assortments. Inventory is always in motion: Deliveries, pickups, and returns, shoppers in-store, and third-party retailers. Delayed response times or inventory issues can lead to discrepancies, bad customer experiences, and cart abandonment. Fun fact: ecommerce stores lose $18 billion due to cart abandonment alone.
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Traditional data systems already struggle to provide a real-time view of inventory, making it near impossible for retailers to meet customers’ demand for fast responses and consistent experiences across every channel. As retailers support more interactive online and in-store experiences, data speed becomes even more critical with the gamification of apps, personalized paths, virtual mirrors, and smart shelves.
The real-time edge can support retail inventory management with low latency across multiple geographies at significantly less cost than the cloud - without the hassle of maintaining servers. The edge also improves omnichannel experiences with faster performance and time to market.
Check out our webinar to learn more about real-time inventory management at the edge.
Macrometa for retailers
Data is active everywhere retailers do business with the Macrometa Global Data Network (GDN). Retailers can build a real-time inventory view for in-store, in-warehouse, and fleet management. Data is replicated in milliseconds and reconciled with automatic conflict resolution. Local copies of inventory data are kept in any of the Macrometa GDN 175+ points-of- presence.
Macrometa lets you store, serve, and process your data within 10ms of 80% of the global population.
Data is close to the point of consumption - around the world - near stores and customer locations. Retailers can meet security and privacy standards like GDPR based on their guidelines.