Customer churn analysis for an e-commerce business, using machine learning to identify churn drivers, predict churn probability, and segment churned customers for targeted retention strategies.

A predictive analysis of banking customer demographics to identify churn risks and optimize retention strategies.

Crisis recovery analytics for QuickBite Express using RFM segmentation, sentiment modelling, SLA diagnostics, and incentive ROI simulation. Includes customer churn profiling, restaurant-level impact analysis, and CAC benchmarking vs competitors. Outputs include dashboards and strategic recommendations.

RFM Segmentation & Retention Strategy

Comprehensive customer churn analysis and retention strategy project using Python, machine learning, and data visualization to predict churn and provide actionable insights.

SQL-based customer churn and revenue loss analysis for retention strategy

🤖 A machine learning project to predict customer churn in the telecom industry using Logistic Regression and Random Forest. Includes exploratory data analysis, class imbalance handling, and customer risk segmentation.

AI-powered Customer Churn Prediction Dashboard with Risk Segmentation, Model Evaluation, and Business Insights.

Machine learning-based customer churn prediction system to identify high-risk telecom customers and enable data-driven retention strategies.

Predicts customer churn using ML models (Decision Tree, Random Forest, Logistic Regression, SVM, KNN, XGBoost) in R, with powerful data visualizations to uncover retention patterns and drive actionable insights in customer lifecycle analysis.

Business Intelligence dashboard for telecom churn analysis with KPIs, segmentation, churn-risk scoring, revenue-at-risk metrics, and retention recommendations. Includes predictive risk buckets, executive insights, derived features, and interactive Tableau views for decision support.

Interactive RFM Customer Segmentation & Cohort Strategy dashboard. Processes 105,000 customer records in Python and generates a boardroom-ready executive interface featuring regional performance

AI-driven churn prediction and retention ROI engine that transforms customer risk insights into revenue recovery strategies and executive-ready reports.

Machine learning project to predict customer churn and support retention strategy using threshold tuning, profit simulation, and model interpretation.

An interactive dashboard that identifies high-risk employee cohorts driving 28% of attrition using SQL ETL pipelines and Tableau. Enables proactive HR interventions by revealing key attrition drivers across demographics, tenure and role types, turning raw HR data into actionable retention strategies.

Designed a data-driven intervention framework for high-value customers by integrating growth deceleration detection, churn prediction, and targeted Revenue at Risk quantification.

end-to-end churn prediction pipeline on a 5,630-customer e-commerce dataset incorporating multi-channel engagement signals (email, push notification, social advertising, retargeting) to model customer retention risk across the full marketing funnel.