Customer churn is one of the biggest silent threats to any subscription-based business. Understanding why customers leave — and identifying them before they go — can save millions in revenue and strengthen long-term relationships.
This project focuses on analyzing churn patterns using SQL for data exploration and Power BI for visualization. The goal: uncover actionable insights to help reduce churn and improve customer retention strategies.
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Objective: Analyze customer churn data to identify key risk factors and visualize insights for business stakeholders.
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Tools Used:
- SQL (for data cleaning, preparation, and exploratory analysis)
- Power BI (for building interactive dashboards)
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Data Cleaning & Preparation
- Handled missing values and inconsistent entries.
- Converted data types where necessary.
- Created derived columns such as tenure buckets and payment method categories.
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Exploratory SQL Queries
SELECT Customer_Status, Count(Customer_Status) as TotalCount, Sum(Total_Revenue) as TotalRev,
Sum(Total_Revenue) / (Select sum(Total_Revenue) from stg_Churn) * 100 as RevPercentage
from stg_Churn
Group by Customer_Status;
- Interactive filters for contract type, tenure, payment method, and more.
- Dynamic KPIs highlighting churn rates and revenue impact.
- Visual segmentation of high-risk customer groups.
- Clear storytelling visuals for business teams and decision-makers.
- Integrate predictive churn scoring using machine learning models.
- Add sentiment analysis from customer feedback surveys.
- Automate periodic data refresh for near real-time insights.
For questions, ideas, or collaborations, feel free to reach out.
Turning raw data into loyalty — one insight at a time.