Automated intelligent markdowns for a global retailer

10 minutes read
case-study
ClientNDA
IndustryRetail

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.

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case-study

The challenge

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. 

The solution

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. 

The results

By using the new data platform, the retailer benefited in several ways:

  • Better quality data through a sustainable data platform
  • More accurate application of the pricing strategy
  • Less waste in the department stores'
  • Automated reporting and alerting
  • Gained information through the creation of a production-ready data platform
  • Improved green sustainability

The tech-stack

ETL component

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Spark-logo
python-logo

Modelling part

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Azure_Machine_Learning
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API

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Kubernetes-logo

CI/CD

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