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.

Customer Segmentation using RFM Analysis

The binary build of LEO CDP Free Edition for training purposes

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

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

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

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

Coursera-Customer analytics

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

Pickl.AI’s Datathon - 4

Predicting customer churn using scikit-learn

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) .

Bootcamp Women in Data - Bogotá, COL

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.

Customer segmentation, price elasticity modelling and conversion modelling.

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.

"Business Insights from Transaction Data" is a Python project for e-commerce and financial services to optimize customer funnel and KPIs. It involves creating synthetic data to model realistic transaction and customer data, which is then analyzed and visualized to support data-driven decision making.