Atom_camp_sql_project

Telco Customer Churn Analysis

This repository contains SQL queries to analyze customer churn reasons based on many factors from the Telco Customer Churn dataset. The focus is on customers who churned and why they churned and how we can solved those problems.

Dataset Description

The Dataset The Telco Customer Churn dataset consists of several tables, including:

Demographics Location Population Services Churn Status

Key Attributes in the Dataset

1.Tenure in Months:

How long the customer has been with the company.

2.Monthly Charges:

The amount charged to the customer every month.

3.Contract Type:

The type of contract (monthly, one-year, two-year).

4.Churn Reasons:

Reasons for customer churn, such as “Competitor had better devices” or “Competitor made better offer.”

Key Problems Identified:

1. Dissatisfaction: Many churned customers cited reasons such as "Competitor had better devices" or "Competitor made a better offer."
2. High Churn in Specific Payment Methods: Customers using Bank withdrawals and credit cards were more likely to churn.
3. Younger Customers Have a Higher Churn Rate: Younger customers tended to churn at higher rates than older customers.

Solutions Proposed:

1. Competitive Device Offers: Introducing better device upgrade programs to retain customers.
2. Flexible Payment Methods: Offering more diverse payment options such as mobile payments, wallets, or automated billing to improve customer experience. 3.Customized Plans for Younger Customers: Using data-driven campaigns to personalize service offerings and retain younger customers.

Conclusion

This analysis demonstrates how companies can leverage customer churn data to make proactive, informed decisions. By addressing key areas of dissatisfaction, enhancing payment flexibility, and creating tailored marketing strategies, telecom companies can significantly reduce churn and build stronger customer relationships.