Case Study: Retail
Lack of a data science approach leads to high cost markdown waves, usage of Excel formulae based process is less accurate and inconsistent and manual process limits the number of markdown waves leading to increased cost.
Created a modernized/automated tool on a cloud platform by using advanced statistical models and machine learning algorithms to predict optimum Markdowns. The tool is capable of integrating with multiple data sources and can be easily and remotely accessible. This helped the client to plan better markdown strategies backed by empirical data from the ML models.
Decentralized price recommendation engine helped store directors in setting the right price while maintaining full pricing autonomy for store directors, gaining an 18% increase in profit over the previous year.