This project analyzes online retail transaction data to identify distinct customer segments using RFM (Recency, Frequency, Monetary) analysis and calculates Customer Lifetime Value (CLV) using Predictive CLV models.

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

The case study is based on how a subscription-based e-commerce business employed customer-centric strategies to reduce churn and increase customer lifetime value. How companies are Maximizing customer spending and loyalty while minimizing subscription cancellations to enhance profits and long-term business sustainability in an e-commerce model.

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.

R-Package for estimating CLV

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.

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

A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more

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

🎓📚📈 Collection of scientific publications that explore, model and predict customer churn and lifetime value (CLV)

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.

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.

Create an advanced data engineering pipeline that processes and analyzes sales data from an e-commerce website using Apache Airflow for workflow management and ClickHouse as the high-performance data warehouse.

Clustering and predicting customer lifetime value with machine learning and RFM analysis.

The primary goal of the Customer Lifetime Value Project is to develop a robust framework for predicting the potential value that a customer will generate over the course of their relationship with the business. By analyzing historical customer data and behavior, the project aims to create models that can forecast the expected revenue

Location-based Marketing for High-Value Customers with Predictive CLV 

Evaluating customer value and targeting

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.

BG|NBD Model uses binomial probability to determine Customer Life Time Value and the likelihood of which customers are 'alive'

CLV forecasting through the use of RFM variables