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.

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

This code supports the "Why CLV should be an Organization's North Star Metric" article written in Medium.

CLV forecasting through the use of RFM variables

Accompanying notebook for Data Konferences Feb. 2018 (Madrid)

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

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

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

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.

Sales, revenue and CLV analysis. Completed with churn prediction using naive Bayes. Considerations, notes and final insights are provided along the code

End-to-end Customer Lifetime Value (CLV), RFM segmentation & cohort retention analysis using Python, SQL & Tableau.

Data Science notebooks

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.

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

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.

podcast listener analysis

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

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.

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.

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