customer-retention

There are 29 repositories under customer-retention topic.

  • crm-application

    oroinc/crm-application

    OroCRM - an open-source Customer Relationship Management application.

    Language:PHP9401380284
  • django-crm

    DjangoCRM/django-crm

    CRM with Tasks management, Email marketing and many more. This Django CRM software app is built for individual use by businesses of any size or freelancers and is designed to provide easy customization and quick development. ⭐️

    Language:Python655937
  • jdmaturen/shifted_beta_geometric_py

    An implementation of the shifted-beta-geometric (sBG) model from Fader and Hardie's "How to Project Customer Retention" (2006)

    Language:Python562421
  • Lab-of-Infinity/Internship

    Data Science & Machine Learning Internship at Flip Robo Technologies

    Language:Jupyter Notebook20107
  • retainful/site

    Retainful Website

    Language:JavaScript135015
  • TimKong21/PwC-Switzerland-Power-BI-in-Data-Analytics-Virtual-Case-Experience

    Comprehensive Power BI dashboards showcasing insights on Call Centre Trends, Customer Retention, and Diversity & Inclusion to drive business impact.

  • retainful/woocommerce

    Abandoned Cart Recovery Email and Next Order Coupon Plugin for WooCommerce. Easily recover abandoned carts with a single click and drive repeat purchases with Retainful

    Language:PHP65402
  • easonlai/analyze_customer_reviews_with_aoai

    This demo repository demonstrates how to analyze customer reviews with Azure OpenAI Service (AOAI). I leveraged "ASOS Customer Review" from Kaggle to obtain valuable insight from the customer review content.

    Language:Jupyter Notebook4206
  • iamkartiknayak/Loyalty_Bridge

    Loyalty Bridge is a web-based loyalty management system that enables businesses to track and incentivize customer loyalty. With Loyalty Bridge, customers earn loyalty coins for each purchase, which can then be redeemed for discounts on future purchases.

    Language:CSS4103
  • hemantdpatil/Predictive-Marketing-Analytics

    Assessed brand loyalty patterns and price elasticity metrics to provide insights for brand’s market growth. Recommended strategies for the top 3 brands based on competitor analysis, customer profiling and customer retention through RFM analysis and multinomial logit.

    Language:SAS3001
  • ronak-07/Customer-Retention-Using-Neural-Network

    Creation of a MultiLayer Perceptron using Back Propagation Algorithm. It was trained to efficiently classify the data into two sets:exit and stay. This was able to predict whether a customer might stay with the bank or leave it in future.

    Language:Python2000
  • Yonas650/ML-Driven-CLV-Prediction

    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.

    Language:Jupyter Notebook2100
  • JKReyner/interconnect

    A machine learning model to forecast customer retention, as well as performing exploratory data analysis to examine which metrics may be most relevant to increase retention.

    Language:Jupyter Notebook1100
  • KelvinLam05/cohort_analysis

    Using cohort analysis to measure customer retention.

    Language:Jupyter Notebook1100
  • mounikapalli/Hotel-Domain-Analysis

    This project showcases the application of data analysis techniques to solve real-world business problems in the hotel industry. By mastering essential Python libraries and methodologies, valuable insights have been derived to support Atliq Grands in enhancing customer retention and boosting revenue.

    Language:Jupyter Notebook110
  • aichner/KISy-Webapp-Prototype

    🎨 Prototype for the easy-to-use web applications to build up customer retention.

    Language:CSS0160
  • AlaaNabil98/CodeClause_Customer_Churn_Rate_Analysis

    This is A Telco Customer Churn Rate Dashboard project that provides insights into customer behavior and churn rates. The dashboard was built using Microsoft Power BI.

  • eiliaJafari/Comprehensive-market-data-analysis

    An comprehensive data analysis of a particular market and its customers.

    Language:Jupyter Notebook0100
  • exelero565/Project_4_ML

    Классификация клиентов банка для прогнозирования вероятности открытия депозита.

    Language:Jupyter Notebook0100
  • mohitsac/PwC-Switzerland-Power-BI-Virtual-Experience

    Comprehensive Power BI dashboards showcasing insights on Call Centre Trends, Customer Retention, and Diversity & Inclusion to drive business impact.

  • NidhiMeher/Churn-Analysis

    Customer churn demographics and insights.

  • SadeTosin/Connecttel-Customer-Churn-Prediction

    This project promises to predict and prevent customer attrition, ensuring long-term loyalty and competitiveness, by leveraging supervised machine learning algorithms.

    Language:Jupyter Notebook0100
  • Sambhaji-Dhage/PwC-Switzerland-Power-BI-Virtual-Internship

    PwC Switzerland Power BI in Data Analytics Virtual Case Experience helps build foundation in data analysis and visualization with Power Bi

  • VishnuTejaDumpala/Hotel-Domain-Analysis

    This project showcases the application of data analysis techniques to solve real-world business problems in the hotel industry. By mastering essential Python libraries and methodologies, valuable insights have been derived to support Atliq Grands in enhancing customer retention and boosting revenue.

    Language:Jupyter Notebook0100
  • BigWheel92/customer-retention-prediction

    an implementation of ann-based model to predict customer retention

    Language:Jupyter Notebook20
  • Loveleen-DS/Lifetimes-package-for-CLV

    Demonstrates how Python's lifetimes package can identify high-value customers and predict their future purchasing behavior. Utilizing the BG/NBD model to forecast purchase frequency and the Gamma-Gamma model to estimate transaction value, this repository aids in crafting targeted marketing strategies.

    Language:Jupyter Notebook10
  • MohammadShabazuddin/Advanced-Customer-Retention-Strategies-in-Telecom-Attrition-Prediction-and-Analysis

    This project develops a predictive model for customer attrition in the telecom industry using advanced machine learning techniques to identify high-risk customers and enable proactive retention strategies.

    Language:Jupyter Notebook10