Customer Churn Analysis

In this project, I leveraged the combined power of Microsoft Power BI, Microsoft Excel, and SQL to create a comprehensive customer churn report for a bank. This endeavor involved analyzing and visualizing vast amounts of data to gain valuable insights into customer behavior and identify potential churn indicators.

Firstly, I utilized SQL to extract and manipulate the relevant data from the bank's database. By writing queries, I retrieved key information such as customer demographics, transaction history, account details, and customer churn status. This allowed me to have a solid foundation for my analysis.

Next, I imported the extracted data into Microsoft Excel for further data cleansing, transformation, and enrichment. Excel's robust functionalities enabled me to organize the data effectively and perform calculations, such as churn rate, average transaction value, and customer lifetime value. Additionally, I used Excel's pivot tables and charts to summarize and visualize the data in a clear and concise manner.

Finally, I employed Microsoft Power BI to create dynamic and interactive dashboards and reports. Power BI allowed me to combine data from multiple sources, integrate the enriched data from Excel, and build visually appealing visualizations. With Power BI's drag-and-drop interface, I designed intuitive charts, graphs, and tables that provided key insights into customer churn patterns, highlighting factors such as transaction frequency, customer satisfaction scores, and account activity.

By leveraging the capabilities of Microsoft Power BI, Excel, and SQL, I successfully developed a comprehensive customer churn report for the bank. The report not only showcased the current churn status but also enabled the bank's stakeholders to make informed decisions and implement effective retention strategies, ultimately improving customer satisfaction and loyalty.