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.

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

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

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.

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.

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.