🚀 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

Cohort Analysis and Customer Segmentation in Excel

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.

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.

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 using RFM + K-Means clustering. 5 segments identified, ₹1.36 Cr revenue opportunity, 3.59:1 ROI. Python | ML | 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.

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

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.

Performed cohort analysis to boost retention & revenue via SQL insights on customer retention, revenue retention, & CLV by product category, with actionable strategies for high-value categories.

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.

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.

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.

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.

This project leverages RFM analysis, KMeans clustering, and probabilistic models (BG/NBD and Gamma-Gamma) to segment customers and estimate Customer Lifetime Value (CLV) using the Online Retail II dataset. It also integrates XGBoost models to predict future purchasing behavior and CLV, with interactive visualizations via a Streamlit dashboard.

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.

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.