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

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.

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.

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

The binary build of LEO CDP Free Edition for training purposes

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

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

Coursera-Customer analytics

Predicting customer churn using scikit-learn

Pickl.AI’s Datathon - 4

Bootcamp Women in Data - Bogotá, COL

Customer Segmentation - Using k-means, About: Customer Segmentation is a popular application of unsupervised learning. Using clustering, identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits.

Trained a Probabilistic Model to forecast the frequency of purchases and how likely a customer is to churn in a given time period using their historical transaction data.

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.

This repo is a code demo that implements a custom Customer Retention Analysis class with a number of helpful methods/functions to generate customer churn insights frequently used for marketing analytics to understand the growth and change of your customer base (new vs retained vs lost) .

Methods for doing customer analytics in R

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.