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Customer Churn Analysis – Excel
Analyzed 20,000 customer records to identify key patterns associated with customer churn and provide actionable retention insights.
Cleaned and validated the dataset with no duplicates or missing values.
Calculated key KPIs including total customers, churned customers, churn rate, and average monthly charges.
Analyzed churn by contract, payment method, and internet service.
Identified month-to-month contracts as the strongest churn indicator, with a 43.18% churn rate.
Provided data-driven recommendations to support customer retention.
Tools: Microsoft Excel, PivotTables, Data Analysis, Data Visualization
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Clean Data. Better Decisions.
Good data work starts with accuracy.
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From data entry and formatting to identifying duplicates and inconsistencies, I focus on keeping every detail clear and consistent.
Accuracy → Consistency → Reliable data.
A Power BI dashboard built to analyze churn behavior across 6,687 customers for a business struggling with unclear retention insights. The dashboard combines geographic churn mapping, category-wise breakdown (Competitor, Attitude, Price), root-cause analysis by churn reason, and demographic segmentation by age group — all powered by Power Query for data modeling and DAX for calculated measures like churn percentage and churn weight. Key finding: price sensitivity drove 62% of churn, and customers under 30 represented the highest-risk segment at 56.6% of total churn weight. The result is a single-page, self-service view that lets stakeholders prioritize retention strategy by actual cause instead of guesswork.
