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Optimizing Customer Value and Fulfillment Strategy with Azure ML

  • 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.

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