clv-analysis

🚀 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. 🚀

The second iteration of Cuana, an E2E customer analytics solution for churn/CLV prediction, segmentation & lead scoring

End-to-end MLOps pipeline for e-commerce customer analytics. It uses the Online Retail II dataset to run RFM segmentation, churn prediction, and CLV modeling on Spark. Airflow orchestrates the workflow, MLflow tracks experiments and models, DVC versions data, and Streamlit provides an interactive UI—services are containerized with Docker.

Customer analytics project with segmentation and CLV prediction

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.

Dashboard built in streamlit for customer behaviour analysis, covering RFM, CLV and more

Cohort Analysis and Customer Segmentation in Excel

An end-to-end Python data analysis project using RFM Analysis, Recency–Frequency Segmentation, and Historical Customer Lifetime Value (CLV) to uncover customer behavior, revenue concentration, and high-value retention opportunities.

End-to-end customer analytics project applying SQL feature engineering, RFM segmentation and historical Customer Lifetime Value (CLV) analysis using PostgreSQL and Python.

4-layer MySQL data warehouse (staging → warehouse → analytics → presentation) for e-commerce customer analytics. RFM segmentation via window functions, CLV/churn tracking, and a Star Schema built from a synthetic 250-customer, 1,500+ order dataset; MySQL, Python.

A full data analytics case study that identifies why telecom customers churn, predicts future churn with machine learning, and visualizes actionable business insights in Power BI dashboards.

A data science project that builds a predictive model to estimate Customer Lifetime Value (CLV) using customer transaction data, enabling businesses to improve customer retention and targeted marketing.

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.

An end-to-end customer analytics project using the Online Retail II dataset. This work features RFM segmentation, churn prediction with XGBoost, Customer Lifetime Value (CLV) forecasting with BG/NBD & Gamma-Gamma models, and statistical A/B testing.

Customer segmentation driving ₹1.36 Cr revenue with 3.59:1 ROI using RFM analysis and K-Means clustering on 5,000 customers | Python, Scikit-learn, Marketing Analytics

A Relational Database System for Online Shopping featuring Inventory Triggers, ACID Transactions, and Customer Lifetime Value (CLV) Analytics.

End-to-end Retail CLV platform — BG/NBD · Gamma-Gamma · XGBoost churn · T-Learner uplift · K-Means segmentation. Interactive Streamlit dashboard with real-time customer scoring, Pareto analysis, churn risk matrix, and RFM explorer. https://poorut.github.io/Retail_CLV_Model/ Access the App using the link below

Customer segmentation using RFM and K-Means clustering. Includes BG/NBD models for probabilistic CLV forecasting and churn risk identification

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.