(PowerBI): Customer Churn Analysis

Goals:

My goal for this project was to create a dashboard in Power BI that would provide insights into the churn rate of customers based on various factors such as age, gender, number of financial products, credit score, and region. In addition, the objective was to identify potential churners and develop targeted retention strategies.

Business Needs:

As a data analyst in the financial industry, I understand the importance of retaining customers. High churn rates can result in a loss of revenue and a negative impact on the brand image. Therefore, understanding the factors contributing to customer churn and developing strategies to retain customers is crucial for any financial institution.

Insights:

Through analysing the data using Power BI, I discovered several insights related to customer churn. Some of the key findings include the following:

• Customers in their mid-50s to mid-60s and late 80s are likelier to churn.
• Customers with many financial products are also at higher risk of churning.
• Female customers are more likely to churn compared to male customers.
• Customers with low credit ratings are at a higher risk of churning.
• The region from which the customer comes also affects the churn rate.

Methodology:

I started by collecting data on customer demographics, financial products, credit scores, and regions to arrive at these insights. I then created a dashboard in Power BI that visualised this data and allowed me to slice and dice it by various factors such as age, gender, and region. I then used statistical techniques to identify the factors that contributed most to the churn rate.

Conclusion:

By creating a dashboard in Power BI and analysing customer data, I identified several factors contributing to customer churn. With this knowledge, my financial institution can develop targeted strategies to retain customers and reduce churn rate, resulting in improved revenue and brand image.