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

FLO wants to determine roadmap for sales and marketing activities. In order for the company to make a medium long -term plan, it is necessary to estimate the potential value that existing customers will provide to the company in the future.

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.

This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.

A Power BI and SQL-based dashboard offering insights into customer behavior, sales trends, and predictive models like churn and Customer Lifetime Value (CLV). This project utilizes a Kaggle dataset, Python for data preprocessing, SQL for data management, and Power BI for dynamic, interactive visualizations.

This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.