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https://github.com/shivamsharma32/customer-churn-analysis-power-bi-
This project is about analyzing and visualizing customer churn data using Power BI. Customer churn is the percentage of customers who stop doing business with a company over a given period of time. It is an important metric for businesses to understand why customers leave and how to retain them.
https://github.com/shivamsharma32/customer-churn-analysis-power-bi-
data-analysis dataanalytics datavisualization powerbi
Last synced: 13 days ago
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This project is about analyzing and visualizing customer churn data using Power BI. Customer churn is the percentage of customers who stop doing business with a company over a given period of time. It is an important metric for businesses to understand why customers leave and how to retain them.
- Host: GitHub
- URL: https://github.com/shivamsharma32/customer-churn-analysis-power-bi-
- Owner: shivamsharma32
- Created: 2024-01-26T06:11:22.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-01-26T06:26:13.000Z (11 months ago)
- Last Synced: 2024-03-10T16:48:49.597Z (10 months ago)
- Topics: data-analysis, dataanalytics, datavisualization, powerbi
- Homepage:
- Size: 299 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer-Churn-Analysis-Power-Bi-
This project is about analyzing and visualizing customer churn data using Power BI. Customer churn is the percentage of customers who stop doing business with a company over a given period of time. It is an important metric for businesses to understand why customers leave and how to retain them.
# Data Source
The data used in this project is from a CSV file that contains information about the customers of a telecom company, such as their demographic, service, billing, and churn status. The data was obtained from Kaggle1.
# Data Analysis
The data analysis process involved the following steps:
Importing the data into Power BI Desktop and performing some data cleansing and transformation using Power Query Editor.
Creating a data model and defining relationships between the tables using Power BI Desktop.
Creating calculated columns and measures using DAX formulas to perform some calculations and aggregations on the data.
Creating various charts and graphs using Power BI Desktop to visualize the data and explore the patterns and trends of customer churn.
Creating interactive filters and slicers using Power BI Desktop to allow the user to drill down into the details and compare different segments and scenarios.
# Data Visualization
The data visualization part of the project consisted of creating a dashboard that displays the following information:
The overall churn rate and the number of customers who churned and stayed.
The distribution of churn by gender, seniority, partner, dependents, and payment method.
The monthly charges and total charges by churn status and service type.
The correlation between churn and various factors, such as tenure, contract, internet service, and online security.
The customer satisfaction score by churn status and service type.
The regional analysis of churn by state and area code.
# Data Insights
Some of the key insights derived from the data analysis and visualization are:
The churn rate of the telecom company is 26.54%, which is quite high and indicates a need for improvement in customer retention strategies.
The customers who churned tend to have higher monthly charges, lower tenure, month-to-month contracts, fiber optic internet service, and no online security or tech support.
The customers who stayed tend to have lower monthly charges, higher tenure, longer-term contracts, DSL internet service, and online security and tech support.
The customer satisfaction score is negatively correlated with churn, meaning that the more satisfied the customers are, the less likely they are to churn.
# Dashboard
