In today's competitive retail landscape, forecasting demand accurately makes or breaks any business. A major global retailer relied on a slow and resource-intensive forecasting process that made it hard to keep up competitiveness.
Our client, a global retailer, aimed to automate their Machine Learning (ML) pipeline to leverage user behavior data and gain a competitive edge by predicting customer needs more accurately. To do so, the retailer lacked the expertise in Big Data, software development, and data transformation.
Our client, a worldwide retailer, runs a big shopping e-commerce platform that experienced a significant delay between customer actions and tailored recommendations. VirtusLab ensured the system's high availability and scalability.
Our client’s data analysis was hindered by the inability to isolate price changes. Employees from other departments had difficulty performing their work efficiently. VirtusLab enhanced the default method of overwriting the entire table in Spark.
Our client, a global retailer, offers seasonal and end-of-line deals. Therefore, the goods must leave the shelves and the department stores' before the next season starts and new goods are delivered. For this purpose, the retailer introduced a pricing strategy that lowers the prices of the products within a particular time window.
Once the client reached out to VirtusLab for assistance, we improved the user interface by using a Design System named Tetrisly. We streamlined processes, cut steps and features, and made it more user-friendly. This resulted in consistent service and a better guest experience.
VirtusLab helped the client in empowering their teams with a cutting-edge platform. It replaced an arduous process using Excel, optimizing exchange time through seamless digital interactions.
A world-leading retailer aimed to unify their in-house platform to enable self-service for their employees. In cooperation with VirtusLab, they reduced the paperwork and shortened company processes, increasing employee productivity.
How VirtusLab's solution streamlined data processes for an insurer, reducing costs, enhancing accuracy, and fostering data-driven decisions.
Our global freight forwarder client needed to optimise its storage usage and operational costs. With VirtusLab's help, they migrated from CosmosDB to Azure storage which resulted in a significant reduction in costs and storage space.
Our client, a top retailer, aimed to improve its growing e-commerce platform’s reporting. VirtusLab’s product manager played a pivotal role in organising the vast network of reporting and reducing data platform structures by 30%.
Our client, a leading UK retailer, aimed to extend its ML Forecasting Framework to other subsidiaries within its group network. The objective was to transition the extensive platform from Hadoop to a dedicated Azure Databricks environment, including all library dependencies and datasets.