customer-retention-analysis

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

End-to-end retail analytics using SQL and Power BI. Analyzed $2.27M retail revenue using SQL and Power BI to uncover customer behavior, retention patterns, product performance, profitability drivers, and sales trends.

Stop losing SaaS users silently. Catch cancel intent in real time, score every user by churn risk, and act before they leave. Free plan available.

End-to-end data analysis project using SQL and Power BI to identify customer retention gaps, revenue concentration risks, and growth opportunities through RFM and cohort analysis.

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

RavenStack SaaS Analytics is an end-to-end business analytics project built to investigate customer conversion, support workload, and churn/retention for a SaaS business. I worked with five interconnected datasets covering customer accounts, subscriptions, feature usage, support tickets, and churn events.

End-to-end telecom customer churn, revenue risk, and retention analytics using Python, SQL, and Power BI.

Interactive Power BI dashboard for revenue, channel, product, and customer retention analysis using DAX and cohort modeling.

End-to-end data analytics project that predicts customer churn, explains why customers leave, and translates model output into a revenue-prioritized retention strategy — built across Python, PostgreSQL, and Power BI.

PwC Switzerland Power BI Job Simulation on Forage - March 2025.

Enhanced telecom customer retention with a dynamic Power BI dashboard. Analyzed customer data to proactively identify churn risks, visualizing trends and insights. Empowered data-driven strategies for effective customer retention.

AI-powered churn prediction pipeline integrating clustering, association rule mining, and ensemble learning,built for interpretable customer retention analytics.

Built 6 interactive Power BI dashboards for a PwC Switzerland Forage simulation — analyzing call center performance, customer churn (34.1%), and workforce diversity. Includes stakeholder email and actionable business recommendations.

Here you can see the projects I'm been involved with. ☺☺

A data-driven framework to identify high-potential markets and operational risks in e-commerce expansion. Delivered in 4 hours

Customer retention cohort analysis using Pandas, Seaborn, and MatPlotLib python libraries.

A Streamlit-based churn prediction app using a trained Random Forest model to analyze customer behavior and predict churn based on demographics, spending, interaction history, and service usage.

Analyzed Flipkart customer support data using Excel to uncover customer retention drivers and built an Excel dashboard for actionable insights.

Predicting customer retention in an e-commerce platform using classification models. Includes data preprocessing, feature engineering, and model evaluation (Logistic Regression, SVM, Random Forest, KNN, Decision Tree). Best model achieves 83% accuracy and perfect recall. Ideal for business use.

The logistic regression model was used in analyzing customer retention.