https://github.com/madhuresh2011/telco-customer-churn-analysis-project-using-python

The analysis primarily investigates factors influencing customer churn, particularly focusing on payment methods and contract types.
https://github.com/madhuresh2011/telco-customer-churn-analysis-project-using-python

csv data-analysis matplotlib numpy pandas pyhton seaborn vizualisation

Last synced: 9 days ago
JSON representation

The analysis primarily investigates factors influencing customer churn, particularly focusing on payment methods and contract types.

• Host: GitHub
• URL: https://github.com/madhuresh2011/telco-customer-churn-analysis-project-using-python
• Owner: Madhuresh2011
• Created: 2024-10-15T17:33:52.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-01-02T08:00:41.000Z (14 days ago)
• Last Synced: 2025-01-02T08:31:32.740Z (14 days ago)
• Topics: csv, data-analysis, matplotlib, numpy, pandas, pyhton, seaborn, vizualisation
• Language: Jupyter Notebook
• Homepage:
• Size: 522 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# TELCO CUSTOMER CHURN ANALYSIS

## Objective:

- The analysis primarily investigates factors influencing customer churn,

particularly focusing on payment methods and contract types.

## Key Insights:

- Contract Type: Customers on month-to-month contracts show a

higher tendency to churn compared to those on yearly or bi-annual

contracts. This suggests that long-term contracts may improve

customer retention.

 - PaymentMethods: A significant proportion of customers using

electronic checks are more likely to churn compared to those using

other payment methods (credit cards, bank transfers, etc.). This could

be due to convenience or trust issues associated with electronic check

payments.

### ChurnRatebyTenure:

 - Customers with shorter tenure (less than one year) are more likely

to churn, indicating the criticality of initial engagement strategies.

### Visualizations:

- The visualizations, including bar plots and line graphs, highlight the

disparity in churn rates by different contract types and payment

methods. They also show trends over customer tenure, supporting the

need for personalized retention strategies.

### Executive Summary:

#### Objective: 

- The analysis explores customer churn patterns, focusing

on various factors such as payment methods, contract types, tenure,

and demographic attributes.

- The goal is to identify which factors are

most strongly associated with higher churn rates to guide customer

retention strategies.

#### Key Insights & Findings:

- Contract Type and Churn:Customers on month-to-month

contracts exhibit the highest churn rate, with 42% of such customers

likely to churn.

- In contrast, customers on one-year and two-year contracts have

churn rates of 11% and 3%, respectively.

- Implication: Longer contract periods serve as a strong retention tool,

as customers with extended commitments are far less likely to leave.

#### Visualizations & Data Insights:

- BarCharts and Line Graphs: 

The visual representation of churn by payment method clearly shows

that customers using electronic checks churn almost three times as

much as those using more traditional or secure methods like credit

cards.

- Customertenure vs. churn rate visualizations reveal a clear declining

trend in churn as customers' tenure increases, underscoring the need

for early-stage customer loyalty programs.