This project provides an in-depth analysis of customer churn patterns, focusing on key factors that drive churn rates. The Power BI dashboard helps businesses understand customer demographics, behavior, and areas that need attention to reduce churn.
- Data Sources: The analysis uses a dataset with customer information, including demographics, services subscribed, and churn status.
- Visualizations: Offers various charts to showcase churn rates, customer segmentation, and trends by demographics.
- Metrics: Focuses on churn rate, retention rate, and customer lifetime value (CLV).
- Actionable Insights: Provides recommendations to improve customer retention strategies.
- Power BI: Used to create the interactive dashboard and visualizations.
- Excel: Employed for initial data cleaning and preparation.
- Dataset: Provided as an Excel file (
02 Churn-Dataset.xlsx).
The dataset contains the following key columns:
- Customer ID: Unique identifier for each customer.
- Churn Status: Whether the customer has churned (Yes/No).
- Demographics: Information such as age, gender, income level, and location.
- Services Subscribed: Details about the services each customer uses (e.g., internet, phone).
- Monthly Charges: The monthly amount billed to the customer.
- Tenure: The length of time the customer has been with the company.
Here are some of the key visualizations included in the dashboard:
- Churn Rate by Demographics: Bar charts showing the churn rate across different age groups, income levels, and regions.
- Service Usage vs. Churn: Visualizes the correlation between services subscribed and churn rates.
- Monthly Charges vs. Tenure: Scatter plot showing how billing amounts and customer tenure relate to churn.
- Customer Segmentation: Pie charts and bar graphs displaying customer demographics and retention patterns.
- Download the
.pbixfile and open it in Power BI. - View the Dashboard: Explore interactive visualizations to gain insights into customer churn patterns.
- Adjust filters: Use Power BI filters to examine different customer segments and factors affecting churn.
- Target High-Risk Customers: Customers with higher monthly charges or shorter tenures show a higher likelihood of churning. Consider offering them promotions or better service packages to retain them.
- Demographic Patterns: Certain age groups or regions are more prone to churn. Tailoring customer engagement strategies to these demographics may help reduce churn.
- Service Optimization: Services like internet and phone show higher churn rates. Consider improving service quality or revising pricing models for these products.
This project is licensed under the MIT License - see the LICENSE file for details.
- LinkedIn Post: Project Overview
- Contact: Sunny Kumar