🚀 Welcome to the YouTube Data Analysis and Insights project! 📊

Optimize marketing strategies and enhance decision-making. Explore customer data, segment behavior, calculate CLV, analyze demographics, and visualize insights. 🚀

This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.

RFM model-based Customer Segmentation using Clustering, Classification and BTYD Models

"Analyze customer behavior using RFM and CLV models for effective profiling. This project integrates RFM segmentation with Customer Lifetime Value (CLV) analysis to create detailed customer profiles, visualize insights, and develop targeted marketing strategies. Includes data, code, and visualizations

Demonstrates how Python's lifetimes package can identify high-value customers and predict their future purchasing behavior. Utilizing the BG/NBD model to forecast purchase frequency and the Gamma-Gamma model to estimate transaction value, this repository aids in crafting targeted marketing strategies.

The Global E-commerce & Retail Analysis project involves data preprocessing, dimensionality reduction with PCA 📉, CLV calculation and What-If analysis . Key insights include effective PCA for data reduction, detailed CLV analysis across segments , and the impact of pricing strategies on sales.