The main goal of this project is to analyze customer data and predict which individuals are likely to churn. By identifying potential churners in advance, we can take proactive measures to improve customer satisfaction and retention, thereby reducing churn.
The analysis also aims to publish key findings based on the data and provide valuable insights to improve customer experience and bank performance.
To perform the analysis, we considered various factors that impact customer behavior. The dataset includes 21 attributes, such as age, salary, marital status, credit card limit, credit card category, and more, collected from 10,000 bank customers.
The following factors are identified as significant contributors to customer attrition:
- Customer's Age
- Income Category
- Gender
- Tenure of Relationship with the Bank
- Marital Status
- Education Level
- Average Card Utilization
Here are some important insights derived from the data analysis:
-
Age Group with the Highest Attrition: Customers in the '40-50' years age group exhibit the highest attrition rate, followed by the '50-60' years group.
-
Age Difference Between Male and Female Customers: The average age of male and female customers, both in the existing and attriting groups, is very similar, hovering around 46 years.
-
Gender Distribution: Females make up a larger proportion of both the attriting and existing customer groups.
-
Higher Attrition for Females: The likelihood of attrition is higher for female customers compared to male customers.
-
Education Level and Attrition: Customers with Doctorate and Post-graduate education levels have a significantly higher attrition rate compared to other customers.
-
Marital Status: Married customers represent the largest portion of those who have left the bank.
-
Tenure and Attrition: The highest attrition rate is observed among customers who have been with the bank for 30-40 months, followed by those with 40-50 months tenure. Thus, attention should be given to customers in the 20-30 months range.
-
Card Utilization: A clear trend shows that customers with lower card utilization have a higher chance of attrition.
Based on the analysis, we can conclude that factors such as age, income category, gender, relationship tenure, and card utilization play a key role in predicting customer attrition. By targeting high-risk customers and providing them with personalized services, banks can reduce churn and enhance customer loyalty.
I would appreciate any feedback or suggestions on this project. Please feel free to share your thoughts and insights. Thank you!