Credit Card Analysis Dashboard

📋 Project Overview

This project was developed as part of a data analysis capstone to provide actionable insights for a financial institution offering credit cards. The Credit Card Dashboard enables decision-makers to visualize customer satisfaction, spending behavior, and financial trends. By analyzing key performance indicators (KPIs), it helps optimize strategies and enhances customer retention.

---

🚀 Background & Problem Statement

Background

The financial institution needed a comprehensive and interactive dashboard to monitor customer credit card usage and satisfaction metrics. The aim was to simplify the tracking of spending patterns, identify high-value customers, and predict trends impacting financial growth.

Problem Statement

The organization faced the following challenges:

1. Difficulty tracking key financial metrics like revolving balances and utilization ratios.
2. Limited understanding of customer demographics and satisfaction scores.
3. Missed opportunities to identify patterns that could boost customer engagement and revenue.

Goals of the Dashboard:

• Track and visualize critical KPIs in real-time.
• Identify trends in customer behavior across demographics and product usage.
• Enable data-driven decisions to improve financial outcomes and customer satisfaction.

---

🛠 Solution: Credit Card Dashboard

The Credit Card Dashboard was built to address these challenges and features:

KPI Tracking

• Customer Satisfaction Metrics: Average satisfaction scores segmented by demographics.
• Revolving Balance Trends: Insights into balances carried forward monthly.
• Average Utilization Ratio: Monitors credit card utilization for risk assessment.

Sales and Customer Metrics

• High-Value Customers: Filters for identifying top spenders.
• Demographic Insights: Trends across age, education, marital status, and income levels.

Visualizations

• Revolving Balance Weekly Trends: Line chart showing balance changes over time.
• Demographic Segmentation: Pie charts for customer breakdown by education and income.
• Top Customers by Spending: Tabular summary highlighting top-performing customer groups.
• Customer Satisfaction by Region: Heat map with regional satisfaction scores.

---

🔍 Visualizations Used

This project leverages various Power BI visualization features:

• Line and Area Charts: To highlight trends in balances and spending.
• Pie and Donut Charts: For proportional analysis of customer demographics.
• Tables and Matrices: Detailed views of financial data and satisfaction scores.
• Heat Maps: Regional breakdowns of satisfaction levels and spending.
• Interactive Filters: Drill-through capabilities for exploring deeper insights.

---

🛠️ Technologies and Tools

This project incorporates:

• Power BI: For dashboard design and interactivity.
• SQL: For data transformation and database integration.
• DAX: For custom KPI calculations and data modeling.
• Power Query: For data cleansing and preprocessing.
• Excel: Used during initial data preparation stages.

---

🚀 Project Workflow

1. Data Preparation: Cleaned raw credit card and customer data using SQL and Power Query.
2. KPI Development: Built DAX measures for key metrics like utilization ratios, satisfaction scores, and spending trends.
3. Visualization Design: Developed an interactive Power BI dashboard for detailed insights.
4. Publishing and Sharing: Published the dashboard and enabled gateway for real-time data updates.

---

💡 Key Insights

The Credit Card Dashboard provides the following actionable insights:

1. High-spend customers drive over 60% of the revenue; targeted marketing can enhance this further.
2. Younger customers (ages 25–35) show the highest utilization ratios but lower satisfaction scores.
3. Monthly revolving balances are increasing year-over-year, indicating opportunities for promoting installment loans.
4. Satisfaction scores in the Southeast region are significantly higher than in the Northeast, suggesting regional service improvements can boost performance.

---

🔮 Next Steps

1. Predictive Analytics: Incorporate machine learning to forecast customer churn and satisfaction trends.
2. Automated Reporting: Schedule automated emails summarizing dashboard insights.
3. Expanded KPIs: Add metrics such as Lifetime Value and Default Risk Score.
4. Real-Time Data Integration: Use APIs to connect live transaction data for dynamic updates.

---

📁 Repository Structure

.
├── data/
│   ├── credit_card.csv               # Credit card transaction and financial data
│   ├── customer.csv                  # Customer demographic and satisfaction data
├── sql/
│   ├── Table_Creation_Queries.sql    # SQL scripts to create database tables
│   ├── Data_Insert_Queries.sql       # SQL scripts to populate tables with data
├── reports/
│   ├── Credit_Card_Dashboard_Insights_Report.pdf # Sample insights report
├── dashboard/
│   ├── CreditCardDashboard.pbix      # Power BI file for the dashboard
├── README.md                         # Project documentation

---

📈 Dashboard Screenshots

(Include screenshots here of key visuals in your Power BI dashboard for a complete view)

---

📫 Contact

• Ankit Bhatia - Graduate MIS Student at University of Texas at Arlington | Ex-Business Data Analyst at Tata Consultancy Services (TCS)