Purvaja Patil

Financial stimulus impact - Reduced the impact created due to financial stimulus using t-test and CAGR method with an objective to have better forecast for Spring’22. Grid parameterisation - Enhance the accuracy by optimising the parameters for better prediction which benefited the client with $52m savings. Segmentation - Clustering stores for better inventory allocation based on demographics and sales pattern. AUR correction - Using standard IQR technique to identify outliers in AUR caused due to pandemic and treated using control group logic. Opportunity - Helps business in identifying missing opportunity at product level for better inventory allocation. Ad-hoc - Normalising the sales impacted due to covid-19 by analysing the trend and imputed using average of historical sales. R-Shiny tool - Built multiple dashboards with an objective to automate and create user friendly experience for business to consume it. Forecast review - Defined rules giving visibility to classes needing immediate attention. Achieved 85% accuracy compared to the intuitive based approach.

Forecasting - Forecasted the number of advertisements using Time Series for business to understand the Share of Voice. Shiny tool - Created a dashboard to give various insightful visualisations and with advanced functionalities. Ad-hoc - Handling ad-hoc requests to answer business questions and insights useful for business.