🚀 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.

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.

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

This project performs cohort analysis to estimate Customer Lifetime Value (CLV) by analyzing weekly revenue and user registrations over 12 weeks, forecasting future revenue, and providing actionable insights for marketing and business strategy.

"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

This project analyses customer retention, churn and customer lifetime value (CLV) on the Google Merch Shop, focusing on weekly behaviour trends and CLV predictions. The findings identify key factors impacting retention, CLV, and customer acquisition cost (CAC) effectiveness.

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.

A data science project leveraging Python and Scikit-Learn to build predictive models that estimate customer lifetime value (CLV). Includes data cleaning, feature engineering, and model selection to identify key drivers of CLV, supporting strategic decision-making in customer retention and marketing.

This repository analyzes global e-commerce trends and their effects on traditional retail. It includes data preprocessing, Customer Lifetime Value (CLV) calculations, and What-if analyses to explore pricing strategies, providing insights into the evolving retail landscape.