podcast listener analysis

Accompanying notebook for Data Konferences Feb. 2018 (Madrid)

To identify best and valuable customers for the company, to analyse the customer needs and wants & develop marketing strategies to retain them and invest in the right customer category to increase company profits. Implement Customer LifetTime Value (CLTV) in order to distinguish customer based on their potential lifetime profits, thus invest in …

Measuring Customer Lifetime Value through Buy Till You Die (BTYD) model

Predicting Customer Lifetime Value

Repository contains Customer Lifetime Value Prediction for Automobile Insurance Company in USA

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

Project for customer management in the Marketing Analytics Department of a large retail bank. The aim of this project is to know which marketing activity effectively retains customers. We have information about individual customer profitability (CLV) and a survey was conducted as well. A research model explaining/predicting individual customer p…

CLV prediction with BG/NBD model, xgboost, lightgbm

Demonstration of Probabilistic BTYD models

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

Data Science notebooks

Customer Base Analysis with Recurrent Neural Networks

What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.

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

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.

Evaluating customer value and targeting

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

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

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