clv

There are 35 repositories under clv topic.

  • pymc-labs/pymc-marketing

    Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

    Language:Python71026556199
  • mukulsinghal001/customer-lifetime-prediction-using-python

    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.

    Language:Jupyter Notebook1362051
  • station-10/awesome-marketing-machine-learning

    A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more

  • bachmannpatrick/CLVTools

    R-Package for estimating CLV

    Language:R55913314
  • valendin/rfm2lstm

    Customer Base Analysis with Recurrent Neural Networks

    Language:Jupyter Notebook18322
  • ThomasSavary08/Lyapynov

    Python package to compute Lyapunov exponents, covariant Lyapunov vectors (CLV) and adjoints of a dynamical systems.

    Language:Jupyter Notebook15105
  • akulumbeg/awesome-clv-models

    🎓📚📈 Collection of scientific publications that explore, model and predict customer churn and lifetime value (CLV)

  • k-bosko/CLV_prediction

    Predicting Customer Lifetime Value

    Language:Jupyter Notebook12209
  • sibylhe/clv

    CLV prediction with BG/NBD model, xgboost, lightgbm

    Language:Jupyter Notebook10104
  • mtahiraslan/flo_cltv_prediction

    FLO wants to determine roadmap for sales and marketing activities. In order for the company to make a medium long -term plan, it is necessary to estimate the potential value that existing customers will provide to the company in the future.

    Language:Python7100
  • dixitamol/banking_CLV_CRM

    Project for customer management in the Marketing Analytics Department of a large retail bank. The aim of this project is to know which marketing activity effectively retains customers. We have information about individual customer profitability (CLV) and a survey was conducted as well. A research model explaining/predicting individual customer profitability is expected, along with a theoretical rational for these hypotheses and test the hypotheses. Multiple independent variables very tried to come up with some meaningful conclusions.

  • nanthasnk/Customer-Lifetime-Value-Prediction

    Repository contains Customer Lifetime Value Prediction for Automobile Insurance Company in USA

    Language:Jupyter Notebook4000
  • RiadBensalem/E-commerce-Sales-Analysis-Pipeline

    Create an advanced data engineering pipeline that processes and analyzes sales data from an e-commerce website using Apache Airflow for workflow management and ClickHouse as the high-performance data warehouse.

    Language:Jupyter Notebook4102
  • dkekre21/customer-segmentation-and-LTV

    To identify best and valuable customers for the company, to analyse the customer needs and wants & develop marketing strategies to retain them and invest in the right customer category to increase company profits. Implement Customer LifetTime Value (CLTV) in order to distinguish customer based on their potential lifetime profits, thus invest in long term customer relationship strategy for the customer segments.

  • joseramoncajide/data_konferences

    Accompanying notebook for Data Konferences Feb. 2018 (Madrid)

    Language:Jupyter Notebook2200
  • ramachandra742/Data-Science-notebooks

    Data Science notebooks

    Language:Jupyter Notebook2100
  • Reign2121/Customer_Value

    Evaluating customer value and targeting

    Language:R2100
  • valendin/give-blood-till-you-die

    Demonstration of Probabilistic BTYD models

    Language:R2100
  • anVSS1/PFA--Revealing-Insights-

    This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.

    Language:Jupyter Notebook110
  • basel-ay/Customer-Lifetime-Value-Prediction

    Clustering and predicting customer lifetime value with machine learning and RFM analysis.

    Language:Jupyter Notebook1100
  • DataRaul/Customer_Churn_AB_NaiveBayes

    Sales, revenue and CLV analysis. Completed with churn prediction using naive Bayes. Considerations, notes and final insights are provided along the code

    Language:Jupyter Notebook1000
  • tushar2704/CLV-Prediction

    The primary goal of the Customer Lifetime Value Project is to develop a robust framework for predicting the potential value that a customer will generate over the course of their relationship with the business. By analyzing historical customer data and behavior, the project aims to create models that can forecast the expected revenue

    Language:Jupyter Notebook110
  • 1401Dev/Customer-Lifetime-Value-Prediction

    A data science project leveraging Python and Scikit-Learn to build predictive models that estimate customer lifetime value (CLV). Includes data cleaning, feature engineering, and model selection to identify key drivers of CLV, supporting strategic decision-making in customer retention and marketing.

    Language:Jupyter Notebook0100
  • ARUNJOGLE/clvpulseplus

    CLV PULSE - A DYNAMIC CUSTOMER LIFETIME VALUE PREDICTOR MODEL USING MACHINE LEARNING

    Language:JavaScript0100
  • chanalytics-ai/clv

    This code supports the "Why CLV should be an Organization's North Star Metric" article written in Medium.

    Language:Jupyter Notebook0100
  • REAtes/Customer-Segmentation-and-Revenue-Prediction

    This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.

    Language:Python0100
  • REAtes/Customer-Segmentation-and-RFM-Analysis

    This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.

    Language:Python0101
  • saulventura/BTYD

    Measuring Customer Lifetime Value through Buy Till You Die (BTYD) model

    Language:R0101
  • Shaffilza/CLVLocMarketing

    Location-based Marketing for High-Value Customers with Predictive CLV 

    Language:Jupyter Notebook0100
  • YoungScienza/Bachelor_Thesis

    CLV forecasting through the use of RFM variables

    Language:Jupyter Notebook0100
  • CarlosADuranVIllalobos/Power_BI_Dashboard

    A Power BI and SQL-based dashboard offering insights into customer behavior, sales trends, and predictive models like churn and Customer Lifetime Value (CLV). This project utilizes a Kaggle dataset, Python for data preprocessing, SQL for data management, and Power BI for dynamic, interactive visualizations.

    Language:Python
  • Niteshchawla/Subscription-Based-Ecommerce-Case-Study

    The case study is based on how a subscription-based e-commerce business employed customer-centric strategies to reduce churn and increase customer lifetime value. How companies are Maximizing customer spending and loyalty while minimizing subscription cancellations to enhance profits and long-term business sustainability in an e-commerce model.

  • nitinjosephrepo/Estimating-Customer-Lifetime-Value-BG-NBD-Model

    BG|NBD Model uses binomial probability to determine Customer Life Time Value and the likelihood of which customers are 'alive'

    Language:Jupyter Notebook10
  • pramodkondur/Customer-Segmentation-RFM-CLV

    This project analyzes online retail transaction data to identify distinct customer segments using RFM (Recency, Frequency, Monetary) analysis and calculates Customer Lifetime Value (CLV) using Predictive CLV models.

    Language:Jupyter Notebook
  • stillmatic/podcasts

    podcast listener analysis

    Language:R30