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