Analysis Of The Telco Customer Churn Data Sets ✓ Solved

Analysis Of The Telco Customer Churn Data Sets

In these modern day competitive business environment, every business strives to grow and mostly need to retain their customers to increase organizational growth, development and effective performance. This analysis focuses on using Customer behavior data to improve Customer Retention. The Churn rate is the number of Customers or subscribers who stop subscribing to a service or company, which means these Customers have “Churned”. Telecommunication companies are concerned about the number of Customers leaving their service.

For this reason, they need to understand who is leaving and why they are leaving. To analyze this, we are provided with data set which includes information about: Customers who left within the last month – the column is called Churn; Services that each Customer has signed up for – Phone, Multiple lines, Internet, Online Security, Online backup, Device Protection, Tech Support, Streaming TV and Movies; Customer Account Information – how long they’ve been a Customer, Contract, Payment Method, Paperless billing, Monthly Charges and Total Charges; Demographic Information about Customers – Gender, Age range, if Customers have Partners and Dependents.

Analyzing the data involves identifying key trends and drivers of customer churn. Here, we will outline the application of the CRISP-DM approach, the visualization of data using tools like Excel and Tableau, and the modeling process using machine learning techniques.

1. Understanding the Problem

The core issue facing telecommunication companies is customer churn, which negatively affects profitability. By understanding the factors that contribute to churn, companies can implement strategies to retain customers.

2. Understanding the Data

The dataset contains various fields that serve as potential predictors for dropout rates among customers. These include demographic information, account specifics, service details, and customer behavior metrics. Key variables identified include:

  • Churn status

  • Tenure

  • Monthly Charges

  • Total Charges

  • Contract type

  • Internet Service type

  • Payment Method

  • Customer demographic details

3. Analyzing the Data through Visualization

Visualizations are essential in interpreting large datasets and revealing insights that drive decision-making. Using Excel and Tableau, we can represent the most critical aspects of the data, including:

Most Commonly Used Internet Service

From the data, Fiber Optics emerged as the most popular Internet service, indicating a clear preference among the customer base.

Payment Methods and Contract Types

The analysis indicates that the majority of customers prefer the Electronic Check payment method and typically choose Month-to-Month contracts.

Drivers of Churn

Through various visualizations, it was identified that key drivers of churn included:

  1. Tenure

  2. Total Charges

  3. Monthly Charges

  4. Contract and Internet Services

4. Building a Predictive Model

To predict churn, we employed a Decision Tree algorithm. This model identified significant variables and their thresholds that contribute to a customer’s likelihood of churning. The model revealed a predictive strength of up to 80% when considering Contract type and other influential variables.

5. Summary of Findings

Our findings indicate that customers with a tenure of less than five months exhibit a higher propensity to churn. In contrast, those with a tenure exceeding five years display lower churn rates. Additionally, Monthly Charges and Total Charges significantly influence customer churn rates.

6. Conclusion

In conclusion, by leveraging data analytics, particularly through the CRISP-DM methodology, we successfully identified the key factors that influence customer retention. Companies can utilize these insights to design targeted customer retention strategies. Recommendations include instituting loyalty programs for long-standing customers and offering promotional discounts for new customers to enhance retention.

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