Understanding Customer Churn: A Data-Driven Insight

In the world of business, understanding customer behavior is key to improving retention and reducing churn. By analyzing customer data, companies can gain valuable insights to tailor their strategies and boost customer loyalty. In this blog post, we will explore a customer dataset, analyze key features, and visualize the findings in an easy-to-understand way.

Let's Dive Into the Data

We start with a dataset of 200 customers, each having a unique customer ID, age, transaction count, credit limit, and churn status (whether they have stayed or left). Our goal is to understand the relationship between these features and how they might correlate with customer churn.

Customer Data Overview:

• Customer Age: Ranges from 18 to 75 years.
• Transaction Count (Total_Trans_Ct): How many transactions each customer has made.
• Credit Limit: The customer's available credit.
• Churn: Whether the customer has churned ("Yes") or stayed ("No").

The first few rows of data look like this:

Visualizations With Insights

To make the data analysis more intuitive and easy to digest, we'll use some simple yet powerful visualizations.

1. Age Distribution of Customers

A box plot is a great way to display the distribution of customer ages. It helps us see the spread, central tendency, and any potential outliers.

This box plot reveals that most customers are in the age range between 25 and 60, with a few outliers towards the younger and older ends.

2. Transactions per Customer

The number of transactions each customer makes is another important factor. A bar chart can show the distribution of transactions per customer.

From the histogram, we can observe that most customers make between 50 and 100 transactions, with a few customers making exceptionally high transactions.

3. Credit Limit and Churn Status

The relationship between credit limit and customer churn is interesting. Let's visualize it using a bar chart.

The box plot shows that customers who churn tend to have lower credit limits compared to those who stay. This might indicate that customers with lower credit limits are more likely to leave, which could be an area for further investigation.

4. Churn Rate Breakdown

It’s also valuable to break down the churn rate across the dataset. A simple pie chart gives a clear picture of how many customers are staying versus leaving.

As seen in the pie chart, approximately 30% of customers have churned, and 70% have stayed with the company.

Key Takeaways

• Customer Age Distribution: The majority of customers fall between 25 and 60 years of age, with a few younger and older customers.
• Transactions: Most customers have between 50 and 100 transactions, and a small group exhibits high transaction volumes.
• Credit Limit: Customers with higher credit limits are less likely to churn, suggesting that offering higher credit limits might help in reducing churn.
• Churn Rate: About 30% of customers have churned, which calls for targeted strategies to retain them.

What Can Businesses Do with These Insights?

By analyzing customer age, transaction activity, and credit limit, businesses can tailor their retention strategies more effectively. For example, targeting younger customers with fewer transactions for special offers or improving the credit limits of high-risk customers could help in retaining more users.

Conclusion

Data is a powerful tool for understanding customer behavior. By utilizing visualizations like box plots, bar charts, and pie charts, businesses can make sense of complex datasets and identify key trends. Customer churn analysis is just one example of how data can inform decision-making and guide business strategies.

https://github.com/yamini542/Applied-AI/blob/main/customer_churn.ipynb   (Code )