In online retail, businesses often emphasize Customer Lifetime Value (CLV) but may overlook fulfillment costs, leading to profit erosion.
A machine learning pipeline was developed to optimize CLV and fulfillment strategy using the UCI Online Retail Dataset and Azure ML.
The pipeline involved cost estimation, fulfillment cost simulation, feature engineering, training a Random Forest Regressor for CLV prediction, and visualizations.
An interactive dashboard in Azure ML was created to visualize customer lifetime value accumulation, purchase frequency bins, and country rankings based on CLV.