The Customer Churn Analysis project examined a dataset of nearly 200,000 customer records containing demographic details, transaction histories, and behavioral patterns. The main objective was to gain insights into customer churn and return behaviors in order to support data-driven retention strategies and long-term business growth. By analyzing such a large and diverse dataset, the project aimed to uncover patterns that explained why customers leave, what drives them to return, and how businesses can optimize customer engagement.

The process began with data preparation in Excel, where missing values were addressed, duplicates removed, and derived fields created to improve analytical accuracy. This clean dataset enabled a robust Exploratory Data Analysis (EDA), which revealed trends across customer demographics, purchasing behaviors, churn rates, and product return activities. These insights were then translated into interactive Power BI dashboards, offering stakeholders a dynamic way to visualize churn patterns, segment customers, and explore return behaviors in real time.

To measure performance and guide strategic decisions, the project established Key Performance Indicators (KPIs) such as churn rate, return rate, average purchase value, and Customer Lifetime Value (CLV). Together, these metrics provided actionable benchmarks to evaluate loyalty, track retention efforts, and identify growth opportunities. By combining Excel for preparation and Power BI for visualization, the project delivered a comprehensive framework that not only highlighted the risks of customer attrition but also empowered the business to develop targeted initiatives that maximize customer value and long-term success.