Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

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.

The datasets, codes and results for the AIIDE21 accepted paper: "Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain".

Churn customer is a customer which has stopped doing business with a company or organization. This can occur for various reason, such as dissatisfaction with product or service, poor customer service, or competition with other company. In this project, I predict the churn customer with machine learning model

ChurnRadar: Unveiling Customer Churn Patterns with Predictive Insights

A churn model is a mathematical representation of how churn impacts your business. Churn calculations are built on existing data (the number of customers who left your service during a given time period). A predictive churn model extrapolates on this data to show future potential churn rates.

Try to predict and identify bank customer churn

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 retail company wants to create a roadmap for its sales and marketing activities. To plan for the medium to long term, the company needs to predict the potential value that existing customers will bring in the future.