Customer Churn Analysis
This case study explores a comprehensive analysis of bank customer churn, aiming to uncover factors influencing churn rates and potential strategies for retention.
If you would like to delve deeper into the importance of churn analysis and its impact on business success, please read my blog post from the below link..
Sector
Financial Services
Tools used
Tableau
Skills
Customer Churn Analysis
Trend Analysis
Data Visualisation
By examining customer attributes such as age, tenure, country, card type, number of products, satisfaction rate, and points earned, we sought to identify patterns and correlations contributing to churn behaviour.
Churn by Customer Demographics and Tenure
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The age group 45-55 exhibited the highest churn rate, suggesting potential challenges in retaining customers within this demographic.
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Customer tenure showed no significant impact on churn, indicating that loyalty alone may not suffice to prevent customer attrition.
Churn by Active Member Status, Country, and Card Type
Insights
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Germany experienced the highest churn rate, double that of Spain and France, underscoring the importance of localised retention strategies.
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Recent customers with a silver card demonstrated high churn rates, which decreased over time. Conversely, churn rates among diamond card holders increased after 6 years, suggesting evolving preferences or needs.
Churn By Number of Products, Satisfaction Rate, and Points Earned
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Customers with more than two products were more likely to churn, highlighting the importance of targeted cross-selling efforts to enhance retention.
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Surprisingly, satisfaction rate showed no clear impact on churn, warranting further analysis to understand the underlying drivers of customer satisfaction and retention.
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Higher points earned correlated with lower churn rates, indicating potential benefits of loyalty programs in reducing customer attrition.
