Python & SQL - Bank Customer Retention Analysis Project

Telco Customer Churn Analysis: Leveraging SQL and Machine Learning to transform customer data into actionable retention insights.

Customer Churn Analysis using AWS, SQL, and Machine Learning to identify key churn drivers and improve customer retention strategies

Data analysis project identifying key drivers for customer retention and lifetime value (LTV) in e-commerce using Python and SQL.

This project analyzes customer churn for MTN using SQL Server. It covers data cleaning, KPI calculation, churn trend analysis, and churn driver insights. The goal is to identify why customers leave and recommend strategies to improve retention.

Dive into data with SQL based customer retention and behavior analysis using 3 table relational dataset. Focused on deriving insights through filtering, aggregation and joins

Customer Retention Analysis using Olist E-Commerce dataset to determine how online retailers can turn one-time shoppers into repeat buyers.

End-to-end SQL project involving data cleaning, customer retention analysis, behavioral segmentation, revenue estimation, and churn risk insights.

SQL-based analysis of the Olist E-Commerce dataset focusing on customer retention, revenue metrics, product performance, and operational insights.

SQL + Excel project analyzing customer retention, value tiers, and actionable marketing insights

Customer retention cohort analysis using SQL/Python

Cohort-based customer lifecycle and retention analysis (MySQL + Power BI) identifying early-stage churn drivers and revenue concentration in an e-commerce business.

In this project, I explored customer lifetime value segmentation, cohort-based revenue trends, and retention dynamics using raw SQL and PostgreSQL. This hands-on analysis helped me identify high-impact customer segments, evaluate revenue evolution across years, and measure churn behavior in detail.

Customer segmentation and retention analysis using Python and SQL

Customer Cohort and Retention Analysis using SQL on Online Retail Dataset

Customer retention cohort analysis using SQL and Power BI to analyze churn, retention, and revenue trends.

E-commerce Sales & Customer Retention Analysis using Python, SQL, and Power BI

Complete Customer Retention Cohort Analysis using SQL (PostgreSQL), Power BI, Python & DAX

SQL analytics case studies including revenue segmentation, customer analysis, and retention modeling.

Analysis of customer churn, funnel bottlenecks, and retention strategy using SQL and Advanced Excel.

End-to-end customer retention cohort analysis using SQL, Python, and Power BI to uncover churn patterns and retention KPIs.

SQL-based cohort retention analysis on Olist e-commerce dataset using MySQL, CTEs, recursive CTEs, retention metrics, and churn analysis to uncover customer behavior patterns.

### 3. Customer Behavior Analysis via SQL **Tools:** SQL (BigQuery) **Description:** Engineered complex SQL queries (JOINs, window functions, CTEs) to analyze 1M+ customer records. Identified churn risks using RFM analysis, boosting retention.

Analyze customer retention and subscription data using advanced SQL techniques to identify patterns, evaluate churn risks, and optimize strategies for increasing customer retention. It involves building SQL queries for segmenting users, calculating retention and churn metrics, cohort analysis, and predicting high-risk customers.

SQL and Python analysis of bank customer churn patterns to identify at-risk customers and provide data-driven retention strategies for financial institutions.

Analyzed 50,000+ customer records using SQL queries to identify retention patterns. Built statistical analysis and visualizations in Excel highlighting key retention metrics for business insights.

Customer Registration & Conversion Analysis A SQL-based analysis project to track customer registration trends, measure conversion rates, and compare performance across acquisition channels. The goal is to optimize customer acquisition, retention, and engagement.

This repository contains a comprehensive analysis of customer churn using SQL and Power BI. It includes SQL scripts, interactive dashboards, and the dataset for identifying key factors influencing customer retention.

SQL-based analysis of e-commerce customer churn, identifying churn drivers and providing actionable recommendations for retention. Leverages BigQuery for data storage and analysis.

End-to-end SaaS customer churn and retention analysis using Python (SQL-based analysis), Excel, and Power BI. The project explores historical churn patterns, customer engagement, inactivity signals, and risk segmentation to identify early warning indicators and actionable retention insights.