OroCRM - an open-source Customer Relationship Management application.

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

Data Science & Machine Learning Internship at Flip Robo Technologies

Retainful Website

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

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

Loyalty Bridge is a web-based loyalty management system that enables businesses to track and incentivize customer loyalty.

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.

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.

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.

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.

Contains Multipage Streamlit applications showing all steps of machine learning pipeline with additional recommendations at the end.

Using cohort analysis to measure customer retention.

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.

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.

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.

Классификация клиентов банка для прогнозирования вероятности открытия депозита.

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.

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.

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