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.

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.

CLV prediction with BG/NBD model, xgboost, lightgbm

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

The purpose of this project is to recommend personalized products for segments by finding product associations.

Customer Lifetime Value, Returns Predictions, Recommender system and sales analysis on UC Irvine online sales dataset.

BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business

data visualization, customer segmentation, CLV and next purchase prediction

Syracuse University, Masters of Applied Data Science - MAR 653 Marketing Analytics

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

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.

Analysis and Prediction of Customer Lifetime Value using R.The insights were then compiled into a report using R markdown.

This project predicts Customer Lifetime Value (CLV) for e-commerce. It aims at forecasting the revenue a business can expect from a customer over time. I did an explatory analysis. From Linear Regression to Neural Networks, explore how different models perform in predicting CLV.

Explore the world of data-driven customer analysis and lifetime value estimation. This project dives into customer segmentation, geographic analysis, time series insights, stock trends, and product descriptions. Join us on our journey of data exploration and optimization.

Predicting Customer Lifetime Value

No description provided.

Understanding the customer life cycle Acquiring customer data Applying big data concepts to your customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers Uncovering attrition patterns Applying predictive analytics in multiple use cases Designing data processing pipelines Implementing continuous improvement

A major non-life insurance company wants to evaluate customer lifetime value based on each customer’s demographics and policy information including claim details. The CLV is a profitability metric in terms of a value placed by the company on each customer and can be conceived in two dimensions: the customer`s present Value and potential future Value.

By understanding and predicting CLV, PT Asuransi Mobil Sejahtera can make better decisions about how much they should invest in customer acquisition and retention, as well as how they can improve customer satisfaction to increase CLV.

Customer Segmentation, Purchase Pattern Analysis, Customer lifetime value, XGBoost Classification

Analysis of an E-Commerce customer database that lists purchases made by over 4000 customers over a period of one year, developed a model that allows to anticipate the purchases that will be made by a new customer, during the following year, from its first purchase.

End-to-end Customer Lifetime Value (CLV) Prediction & Retention Analytics System built with Python, XGBoost, and Streamlit — includes RFM segmentation, cohort analysis, persona insights, model monitoring, drift detection, logs analytics, and automated executive summary reporting.

No description provided.

A modular analytics project using real-world e-commerce data for cohort, LTV, and pricing analysis.