Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
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.
Buy Till You Die and Customer Lifetime Value statistical models in Python.
R-Package for estimating CLV
Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.
A python package to train & evaluate Customer Lifetime Value(CLTV) models using Neural Networks & ZILN loss(developed by google)
🎓📚📈 Collection of scientific publications that explore, model and predict customer churn and lifetime value (CLV)
This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.
The python version of the lab exercises from the coursera class Foundation of marketing analytics.
This repo hosts the course content of Customer Analytics, taught at Tilburg University by George Knox last taught Fall 2022.
CLV prediction with BG/NBD model, xgboost, lightgbm
Customer Lifetime Value, Returns Predictions, Recommender system and sales analysis on UC Irvine online sales dataset.
The purpose of this project is to recommend personalized products for segments by finding product associations.
Syracuse University, Masters of Applied Data Science - MAR 653 Marketing Analytics
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.
BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business
Analysis and Prediction of Customer Lifetime Value using R.The insights were then compiled into a report using R markdown.
Predicted clv making it easier for the auto-insurance companies to decide on the premiums of their incoming clients and thus balance the total risk in the market
data visualization, customer segmentation, CLV and next purchase prediction
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