Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

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.

Tools for Customer Segmentation using RFM Analysis

Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.

Customer & Purchase Analytics using Segmentation, Targeting, Positioning, Marketing Mix, Price Elasticity

This repo hosts the course content of Customer Analytics, taught at Tilburg University by George Knox last taught Fall 2022.

The binary build of LEO CDP Free Edition for training purposes

Bootcamp Women in Data - Bogotá, COL

Predicting customer churn using scikit-learn

The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.

Customer Analytics for a FMCG company (K-means clustering, PCA, logistic regression, linear regression)

Methods for doing customer analytics in R

This is an end-to-end ML project, which aims at developing a classification model for predicting if a customer for an ecommerce business will churn or not in the following month

Key: descriptive statistics and exploratory data analysis, forecasting (linear regressions, ARMIA, Prophet), and a Tableau dashboard that delivers customer insights such as RFM analysis.

Coursera-Customer analytics

This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.

Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.

Customer segmentation, price elasticity modelling and conversion modelling.