Projects

Customer Lifecycle.

Problem Statement:

Businesses aim to identify which customers are most likely to make repeat purchases to focus marketing efforts, optimize customer lifetime value, and build loyalty

Objective:

Create a production-grade pipeline that accurately predicts which customers are likely to return, facilitating actionable business decisions and improvements in ROI

Process Pipeline:
  • Customer Purchase History Data Ingestion
  • Behavioral Feature Engineering using Azure Databricks
  • XGBoost Classifier Model Training
  • Production Deployment via Azure Functions
  • Performance Monitoring and Model Retraining
Repeat Customer Prediction architecture
Technologies & Models:

XGBoost Classifier, Azure ML, Azure Databricks, Scikit-learn

Key Outcomes:

Customer Lifetime Value Optimization, Targeted Marketing Efficiency, ROI Improvement

Intelligence • Architecture • Global Scale

Industries

Global Presence