Customer Churn Analysis

Tools Used: SQL, Power BI, Excel Objective: Identify factors contributing to customer churn and visualize churn rates across different customer segments. Project Steps: Data Preparation (SQL): Write SQL queries to pull customer data, including demographics, transaction history, and customer support interactions, from multiple tables in the database. Data Analysis (Excel): Use Excel to clean and prepare the data. Perform exploratory analysis to calculate churn rates, identify trends, and categorize customers into different risk levels using advanced Excel formulas. Dashboard Creation (Power BI): Build a Power BI dashboard that highlights churn patterns, risk factors (e.g., low usage, high complaints), and provides actionable insights. The dashboard enables filtering by customer demographics, subscription type, and support tickets. Outcome: The analysis helps the company proactively engage at-risk customers, reducing churn by 15%.