https://github.com/gokulgowthams/customer-risk-analysis
https://github.com/gokulgowthams/customer-risk-analysis
data-visualization powerbi
Last synced: 5 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/gokulgowthams/customer-risk-analysis
- Owner: GokulGowthamS
- Created: 2024-08-09T14:48:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-17T10:54:22.000Z (over 1 year ago)
- Last Synced: 2025-04-05T19:43:19.821Z (about 1 year ago)
- Topics: data-visualization, powerbi
- Homepage:
- Size: 8.45 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Customer Retention and Risk Analysis Dashboard for Internet Service Provider
This README provides an overview of the Customer Retention and Risk Analysis Dashboard designed for an Internet Service Provider (ISP). The dashboard leverages Microsoft Power BI to deliver actionable insights into customer behavior, retention rates, and potential churn risks, enabling data-driven decision-making to enhance customer loyalty and reduce churn.
The purpose of this dashboard is to:
Monitor Customer Retention: Track and analyze customer retention rates over time, identifying trends and areas of concern.
Identify High-Risk Customers: Pinpoint customers at high risk of churning based on key indicators such as service usage, customer support interactions, and demographic factors.
Optimize Retention Strategies: Provide insights to refine customer engagement strategies, reduce churn, and improve overall customer satisfaction.
Key Features
1. Customer Overview
Customer Demographics: Visualizes customer data segmented by age, gender, location, and whether they have partners or dependents. This helps in understanding the demographic profile of your customer base.
Account Information: Displays key details such as contract type, payment method, monthly charges, and tenure as a customer.
2. Churn Analysis
Churn Rate Over Time: Tracks the churn rate across different time periods, identifying spikes or declines in customer retention.
High-Risk Customer Identification: Highlights customers at risk of churning based on factors like low service usage, frequent complaints, or long resolution times.
Churn Drivers: Analyzes factors contributing to churn, such as dissatisfaction with services, billing issues, or technical problems.
3. Service Usage Analysis
Service Adoption: Shows the distribution of customers across different services (e.g., internet, streaming, tech support), helping to identify popular and underutilized services.
Multiple Service Users: Identifies customers who subscribe to multiple services, as these customers are typically more loyal and less likely to churn.
4. Customer Support Analysis
Ticket Analysis: Tracks the number of support tickets (administrative and technical) raised by customers, helping to identify common issues and areas for improvement.
Resolution Time: Measures the average time taken to resolve customer issues, with an emphasis on minimizing delays that could lead to dissatisfaction.
5. Predictive Risk Modeling (Optional)
Churn Prediction: Utilizes machine learning models to predict which customers are most likely to churn in the near future, allowing proactive engagement.
Risk Scoring: Assigns a risk score to each customer based on their behavior, service usage, and interaction history.
The dashboard is powered by data from the following sources:
Customer Database: Contains demographic details, account information, and churn status.
Service Usage Logs: Tracks customer interactions with various services provided by the ISP.
Customer Support System: Records support tickets and resolution details.
This dashboard is a powerful tool for ISP stakeholders to gain a deeper understanding of customer behavior, identify risks, and implement strategies to improve retention. Regular use will help maintain a loyal customer base and drive long-term business success.