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Customer-churn-analysis-powerBI

Objective:

An in-depth analysis will identify key factors contributing to increased customer churn at the bank. Insights from customer behavior, transactions, and feedback will inform strategic decisions to improve retention and lower churn rates. Actionable recommendations will be provided for effective customer loyalty initiatives.

Tools used: Power BI and Power Query

Data: sourced from a commercial bank with all the personal information been removed to protect customer's private information.

Steps involved:

  1. Data Preparation
  2. Data categorization and Grouping
  3. Formatting
  4. Data transformation
  5. Data modelling
  6. Visualization report involving DAX measures.

Conclusion:

This Power BI analysis transformed raw data to reveal insights on salaried customer churn factors. Stakeholders can now focus on key issues, implement changes, and monitor results for effective churn management.

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