Customer Success Story: National Retail Company

Retailer Improves Store Operations and Increases Customer Satisfaction with TigerGraph

The company is one of the largest supermarket chains in the United Kingdom, and one of the most recognizable and cherished brands in the country. The retailer had revenues of tens of billions of British pounds in 2021, and has over 1,000 stores and nearly 200,000 employees. The company has built out its business portfolio with a number of strategic acquisitions in recent years.

We are already seeing an impact of the TigerGraph software and are moving towards running our business with fewer issues and lower risks.

Director of Operations, National Retailer

The Challenge

The company has a complex network of applications, reports and data pipelines from systems. There wasn’t, however, a way to map out the connectivity of this complex estate. Consequently, when a failure occurred, intense manual effort was needed to identify the impact, leading to goods not being delivered to stores and customers experiencing poor customer service. Moreover, senior managers would be contacted at all hours of the day to help troubleshoot causes and identify corrective actions. This had an effect on the profitability of the business and also the reputation of individuals within the operational and data teams along with the reputational image with the public.

The Solution

TigerGraph provides the retailer with a platform to drive operational excellence. This platform offers the ability to create a digital twin of the company’s applications, data pipelines, report generation, and extract, transform, and load jobs – this enables an understanding of the hierarchical connectivity of the network and an ability to perform root cause analysis and identify the “blast radius” or “domino effect” of a failure – and results in stakeholders being quick and easy to identify, while reducing the time to fix and mitigate internal and external customer inconvenience.

The Results

Customer Success Story

The retailer has proven an ability to reduce the time to perform root cause analysis significantly and is automating many processes to reduce the need for human intervention and risk to the organization. The company is taking significant strides towards operational excellence and understanding why something has gone wrong quickly, how to fix it and inform stakeholders of trouble coming so they can plan accordingly. This leads to improvement in customer experiences overall. Next, the retailer will leverage machine learning to create predictive models that reduce the time and cost of implementing fixes even further by streamlining processes, making legacy systems redundant and avoiding unnecessary and unused reports.