Description: This Power BI report provides a comprehensive analysis of customer churn, which is crucial for understanding customer behavior and improving retention strategies. The dataset used in this analysis includes various customer attributes and behavioral metrics that help identify patterns leading to churn.
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Utilized Power Query to clean and transform the data, ensuring that it is ready for analysis. ๐ ๏ธ
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Addressed null values, duplicates, and performed necessary data type conversions. ๐งน
- Visual representation of the overall churn rate, highlighting the percentage of customers lost over time. ๐
- Breakdown of churn by demographic factors such as age, gender, and location, allowing insights into which groups are more likely to churn. ๐ฅ
- Analysis of customer behavior (e.g., usage patterns, purchase history) that correlates with higher churn rates. ๐
- Key Performance Indicators (KPIs) to track the health of customer retention efforts. ๐
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Implemented DAX measures to calculate churn rates, customer lifetime value, and other essential metrics. ๐ข
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Developed a dynamic ranking system to identify top factors contributing to churn. ๐
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Used predictive analytics features to forecast future churn based on historical data. ๐ฎ
- Allow users to drill down into specific segments of the data. ๐
- Enhanced user experience with interactive buttons that guide through different sections of the report. ๐ฑ๏ธ
- Added detailed tooltips to provide additional context and insights directly within the visuals. ๐ก
- Utilized to enable dynamic measures and dimensions within visuals. ๐
- Applied to highlight critical data points and trends. ๐
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Download the .pbix file and open it in Power BI Desktop. ๐พ
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Explore the various pages of the report, interact with the slicers, and dive deep into the analysis of customer churn. ๐ต๏ธโโ๏ธ
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Customize the report further based on your specific needs or business requirements. ๐ ๏ธ
- Understand which customer segments are at higher risk of churn and develop targeted retention strategies. ๐ฏ
- Identify at-risk customers early and take proactive measures to reduce churn. ๐
- Analyze the effectiveness of current retention strategies and provide insights for decision-making. ๐ผ
- The dataset used in this analysis is fictional but modeled after real-world customer churn scenarios. ๐