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.
The product managers felt that the markdown strategies were wasting their time. They only wanted to approve or correct these strategies. On the one hand, the teams were overloaded. On the other hand, they chose a pricing strategy that left a big waste to empty the department stores'. Our client's data scientists wanted to create an automated solution.
The data scientists soon realized that they needed help managing this project from start to finish. They also discovered that data platform production required a different skill set than data exploration. It required a robust data platform that could easily expand. At that point, they turned to VirtusLab.
VirtusLab expanded the already created data platform with additional pipelines and automated manual processes from day one of the collaboration. As a data science engineering team working closely with the client, VirtusLab took on the task of delivering well-designed and extendable data pipelines. We enabled data scientists to explore data faster by providing libraries and tooling templates. During this productionization, VirtusLab also teaches data scientists to create production-ready solutions.
By using the new data platform, the retailer benefited in several ways:
