This project dives deep into the sales, delivery, and customer feedback data of major grocery delivery platforms – Blinkit, Swiggy Instamart, and JioMart. It is designed to showcase my ability to clean, analyze, and visualize data using Microsoft Excel.

Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

Decision-support tools for pricing, cost, cash flow, and customer value analysis.

CLV and Churn Prediction with Business Insights and Discount simulation Dashboard

Data-driven customer retention strategy using churn risk, customer value, and ROI-based intervention simulation on the Online Retail II dataset.

A spatially-resolved, multi-wavelength analysis framework for studying sunspot Center-to-Limb Variations (CLV) using SDO/HMI and SDO/AIA data.

End-to-end Customer Lifetime Value (CLV) prediction pipeline using RFM analysis and CatBoost

Power BI dashboard analysing customer lifetime value and promotional cohort effectiveness for an airline loyalty program.

🚀 EcomOpti: 6-phase ML pipeline for telco churn prediction, CLV estimation & retention campaign optimization. Features survival analysis, causal uplift modeling, budget optimization, FastAPI microservice & interactive Power BI-style Dash dashboard.

Customer segmentation using RFM analysis and CLV modeling with Power BI dashboard

End-to-end Customer Lifetime Value (CLV) Prediction & Retention Analytics System built with Python, XGBoost, and Streamlit — includes RFM segmentation, cohort analysis, persona insights, model monitoring, drift detection, logs analytics, and automated executive summary reporting.

Customer segmentation and CLV analysis; hierarchical clustering, PCA, propensity scoring; 5,765 users, $23M CLV, 97.2% confidence

R-Package for estimating CLV

Python package to compute Lyapunov exponents, covariant Lyapunov vectors (CLV) and adjoints of a dynamical systems.

Customer Lifetime Value (CLTV) modeling for ecommerce using predictive analytics. Includes BG/NBD & Gamma-Gamma models, customer segmentation, SQL pipelines, and Power BI dashboards for actionable marketing insights.

This SQL project focuses on calculating customer lifetime value and segmenting customers using Recency, Frequency, and Monetary (RFM) analysis. The goal is to identify high-value, loyal, and at-risk customers for targeted marketing.

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.

CLV PULSE - A DYNAMIC CUSTOMER LIFETIME VALUE PREDICTOR MODEL USING MACHINE LEARNING

By understanding and predicting CLV, PT Asuransi Mobil Sejahtera can make better decisions about how much they should invest in customer acquisition and retention, as well as how they can improve customer satisfaction to increase CLV.

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.