Bank Customer Churn Analysis with Tableau
Which Customers are more likely to churn?
Introduction
Customer churn is one of the biggest challenges in the banking industry.
“Customer churn means customers leaving the bank”
Acquiring a new customer is more expensive than retaining an existing one. By understanding which customers are more likely to leave, banks can take proactive measures to improve customer retention and satisfaction.
The goal of this analysis is to identify characteristics of customers who are most likely to churn.
To better understand the factors influence customer churn, I analyzed a banking customer data set containing 10,000 customers and made several charts to get deeper insights.
Let’s dive in…
Key Metrics at a Glance
The dataset contained 10,000 customers .Out of 10,000 customers,
7936 — Customers stayed
2037 — Customers churned
resulting in a churn rate of 20.4%. This means approximately one out of every five customers has exited the bank.
Although majority of customers retained with the bank, churn rate of 20% is still significant and highlights the importance of customer retention strategies. These KPI metrics provide an over view of the customer base and the overall churn situation.
To get deeper insights, let’s explore the distribution of churn and analyze the key factors influencing customer attrition.
Churn Distribution — Donut Chart
The donut chart provides a clear overview of distribution between retained and churned customers.
Insight : Around 80 % customers retained, Around 20 % customers churned
Tableau technique
Donut Chart was created using a dual axis pie chart with a dummy measure to create the hollow center.
To understand, which customers are likely to leave, lets analyze age group wise next..
Does Age influence customer churn?
This chart reveals a clear trend. Churn rates tend to increase with age. Younger customers in the age of 18–30,31–40 has the lowest churn % and it increase after 40 and age group 51–60 has highest churn rate of 56 %
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This clearly indicates that age is a influencing factor of churn rate. One Possible reason could be older customers may have changing financial needs, different service expectations, or may be willing to switch banks if better alternatives available. Therefore the bank need to focus this age group of customers to retain them.
Churn rate by Country
Customer churn varies significantly across countries. Germany records the highest churn rate at 32% compared to spain 17 % and France 16 %.This suggests that customer retention challenges may be required more in German market and needs further investigation to understand the reason.
To get more clarity, I made a Heat Map chart with age group, country and churn rate
Heat Map
From this chart we can understand that, across all countries churn rate is more in age group 51–60.Bank needs to target this age group customers for retention. While in German After 40 years of age, churn rate is high compared to other countries. Hence more focus required towards German customers above age 40.
While age influence customer churn, lets check, does Gender influence churn?
Churn rate by Gender
This analysis shows that female customers have higher churn rate compared to male customers. This suggests that female customers are more likely to leave the bank. Further investigation is required to understand the reason behind this trend and the bank needs to take measures to retain them.
Now let’s explore how churn is influenced by active and non active customers
churn by active/Inactive
This chart clearly shows that Inactive customers are at higher churn rate of 27 % compared to active customers indicating that customer engagement plays a crucial role in customer retention.
Therefore, banks should closely monitor customers activity levels and pro actively identify inactive customers. Timely interventions such as engagement campaigns, personalized offers can reduce churn rate.
Next,let’s analyse churn rate with no.of products customers have
Lollipop Chart — Customer Churn Vs no.of products
The lollipop chart was used to analyze how customer churn varies based on number of products held by customer.
An interesting pattern emerges from this analysis. Customers with 2 products exhibit the low churn rate. wherein customers with more products exhibit higher churn rate.
Bank should investigate why customers with multiple products are more likely to leave and identify opportunities to improve customer satisfaction.
Conclusion
This analysis explored customer churn pattern using Tableau and revealed several factors that influence churn. By identifying high risk customer segments and understanding the factors driving the churn, the bank can take proactive measures to improve customer satisfaction and focus on customer retention.
This analysis revealed that older customers, female customers, inactive customers, customers from Germany, customers with more products were more likely to churn. These insights can help bank to focus their retention efforts where they needed most.
Final input : “ Satisfied customer stick forever, So better retain them”
Thankyou for reading! I hope you find this insights valuable.
Happy reading!!!
My Tableau dashboard link
