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

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

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

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.

Cohort Analysis and Customer Segmentation in Excel

Customer analytics project with segmentation and CLV prediction

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.

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

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

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

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.

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

A Streamlit-based dashboard that predicts a customer's future spending in the next 3 and 6 months, classifies customer type (Retail or Wholesaler), and visualizes their past purchasing behavior using transactional data.

Final project of the International Master in Data Science in which our team develop marketing strategies for a fashion retail company targeted at specific customer segments and provide them with customized offers. The segmentation was done by employing RFM analysis in conjunction with unsupervised clustering algorithms.

The team developed a Sales Forecasting Analytics System for NSF Global Sdn. Bhd., improving data-driven decision-making. They processed and cleaned datasets, implemented Prophet for time series forecasting, and designed interactive visualizations. Automating the data pipeline reduced processing time and project delivery efficiency.

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

"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 repository was created for my Power BI Project (Olist E-commerce Dashboard).