Companies usually have a greater focus on customer acquisition and keep retention as a secondary priority. However, it can cost five times more to attract a new customer than it does to retain an existing one. Increasing customer retention rates by 5% can increase profits by 25% to 95%, according to research done by Bain & Company.
Customer Churn is one of the most important and challenging problems for businesses such as Credit Card companies, cable service providers, SASS and telecommunication companies worldwide. Even though it is not the most fun to look at, customer churn metrics can help businesses improve customer retention.
The objective of this project is to perform a comprehensive analysis of customer churn in the telecom industry using Python libraries. Customer churn refers to the phenomenon where customers discontinue their services or stop using a product or service provided by a company. In the telecom industry, customer churn is a critical challenge as it can have a significant impact on a company's revenue and market share.
This project aims to utilize Python libraries for data analysis, visualization to gain insights into customer churn patterns to identify potential churners. By understanding the factors contributing to churn, telecom companies can develop targeted strategies to retain valuable customers and reduce customer attrition rates.
- Find the average Churn?
- What are average values of numerical features for churned users?
- How much time (on average) do churned users spend on the phone during daytime?
- What is the maximum and minimum length of international calls among loyal users (Churn == 0)?
- Who do not have an international plan?
- Find the insights about churn rate over International Plan?
- Find the insights about churn rate with customer service calls?
- Find the insights about churn rate with Many_service_calls?
- Find the insights about churn rate with customer service calls & Many_service_calls?
Here we are using a random churn dataset of Telecommunication Industry, which is collected form https://www.kaggle.com/ . It contains data about:
- state
- account length
- area code
- phone number
- international plan
- voice mail plan
- number vmail messages
- total day minutes
- total day calls
- total day charge
- total eve minutes
- total eve calls
- total eve charge
- total night minutes
- total night calls
- total night charge
- total intl minutes
- total intl calls
- total intl charge
- customer service calls
- churn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
** The Dataset, analysis report and analysis notebook are uploaded above. Thank You!