Streamlining Markdown Pricing with 10 m GBP uplift in revenue
A global retailer wanted to streamline their pricing approach for reducing product prices across all their stores. They faced an inefficiency of manual processes, resulting in loss of time and money. By automating A/B tests for Machine Learning-based markdown pricing, they swiftly implemented the most effective pricing strategies, potentially increasing their annual sales by £10 million.
Our client implemented product markdowns to clear inventory and encourage customers to purchase products that were slow-selling or going out of season. However, they faced the challenge of inefficiency in manual A/B testing of markdowns, resulting in potential calculated losses that could be optimised.
The retailer aimed to automate their pricing determination process for markdowns but encountered scepticism regarding the relevance of automated procedures. The client sought proof that automated procedures are the future of the company. This was the moment they reached out to VirtusLab.
They established distinct prices for stores using both merchandisers, conducting manual markdowns, and machine learning models to finally compare the outcomes and identify the most effective strategy. The idea was to prove the pricing model’s efficiency by conducting A/B tests for various pricing strategies in stores.
VirtusLab developed a framework to streamline and fully automate A/B tests. By providing the Automated Testing and Orchestration (ATO) with a list of products and models to compare, the A/B tests were run entirely autonomously.
This included training the models, optimising the prices, selecting stores, uploading different prices to stores, and retrieving the test results from an API endpoint once the tests were complete.
We successfully integrated various components, such as different APIs for store pricing, ML pipelines for model training and obtaining optimised results, and a store selection service.
User defines A/B test: choose which models to compare, define the products, define the percentage of stores for each group of the A/B test
ATO automatically select the stores and determines the prices for the different groups of the A/B test
ATO uploads prices to stores.
User can retrieve the result of the A/B test.
Automated A/B tests enabled our client to:
Languages: Python
Database: Postgresql
CI/CD: Azure Devops, Github Actions
Infrastructure: Azure (cloud), AzureApp Service, AzureML, Postgresql DB, Terraform