Using Cloud Analytics for Decision Making in Retail for Market Dominance
One of the leading retail convenience stores in the US was growing significantly over the years. With a consistent inflow of customers, large chunks of data were collected that were either scattered or lying hidden in silos for exploration. The need was to put the data to use for improvising sales. To overcome this scenario, they approached Glorious Insight to help them use the untapped data source. On a thorough analysis, the Glorious Insight team found out that the retail chain required advanced data architecture.
Check out in the case study how Glorious Insight migrated the data analytics solutions to the cloud for maximizing the scope of its usage for management, marketing, and sales promotion.
Customer Background
As one of the fastest-growing retail chains in the US, there was significant data accumulation happening at the retail store but traditional data infrastructure was incompetent to adhere to such significant data scaling. As a result, the retail chain collaborated with Glorious Insight to fix sales, supply chain management, and ad-hoc reporting.
Challenge
As a rapidly growing retail chain, the vision of the client was to open more than 10,000 retail outlets in North America. But such an extensive level of expansion demanded agile data architecture solutions. Such data management could pave the way to handle huge data chunks for business insights to influence decision making for sales, stock keeping, supply chain movement, finance management, and ad-hoc. The retail giant faced an insurmountable pressure to deploy faster data management architecture to report to C-suite, storefront operators, and franchisees on a real-time basis. One of the prime reasons was dependence on outdated codes that resulted in significant downtime on their e-Commerce stores, costing them revenues and reputation.
Solution
Experts at Glorious Insight carefully analyzed all the issues and suggested to improvise their data governance via two models; (i) Upgrade to a newer version of database, or; (ii) re-architect the IT infrastructure to re-engineer the data analytics for living up to their vision of 10,000 stores. Considering the budget proposition and scalability, re-architecture ended up as a more viable option. The reason to choose this was that Glorious Insight presented 10 to 25 use cases justifying the POC (Proof-of-Concept) where enterprise-wide modern data foundation had improved data aggregation and analysis.
But the retail store needed highly agile enterprise analytics that could migrate almost 3 years of their historical data that stood at more than 16 terabytes for collective analysis. So, Glorious Insight provided a customized cloud platform that improved the data ingestion from multiple stores on a single dashboard. They were not required to scale up their servers after such leverage, saving considerable infrastructure cost. The AI-based model used for predictive analysis was providing intelligent analytics to the store to handle their challenges and encourage customers to adopt self-service models. With the new cloud system, query and reporting improved significantly. Such leverage allowed accessing current and historical intelligence at a much faster pace to launch new KPIs for enhancing sales and customer experiences. There was greater transparency pertaining to store daily sales, gross profits, merchandise, inventory counts, write-offs, invoicing, and order data. Such insights helped them understand customer’s pattern of purchase and preferences to speculate about future predictive sales via data visualization tools. Having a robust predictive capability streamlined the scope for faster expansion.
There was a significant decline in the downtime and more footfall or logins at the physical as well as the online e-commerce store. At the same time, there was a remarkable reduction in IT infrastructure and software licensing that saved more than 40% of their revenues. The use of analytics also ensured new revenue-generating opportunities at their existing stores through reporting & maintenance. Hence, they were inching closer to fulfill their vision of 10,000 stores in North America.
