This project focuses on analyzing credit card customer data and transaction behavior to derive meaningful business insights. The goal was to build an interactive Power BI dashboard that helps understand customer demographics, spending patterns, and weekly revenue trends.
The project consists of two dashboards:
- Customer Report Dashboard
- Transaction Report Dashboard
Through this project, I aimed to simulate a real-world business scenario where data is transformed into actionable insights using Power BI.
The main objectives of this project were:
To analyze customer demographics such as age, income, education, and occupation To identify spending patterns across different categories To track weekly revenue trends and transaction performance To understand credit utilization behavior To create an interactive dashboard for better decision-making
- Power BI Desktop – Data visualization and dashboard creation
- Power Query – Data cleaning and transformation
- DAX (Data Analysis Expressions) – Creating calculated columns and measures
- GitHub– Project documentation and version control
Two datasets were used:
This table contains transaction-level data, including:
Total transaction amount and volume Credit limit and utilization ratio Expense type (Bills, Fuel, Travel, etc.) Weekly data (Week Number, Week Start Date) Interest earned and delinquent accounts
This table contains customer demographic details such as:
Age, Gender, and Marital Status Income and Job type Education level Location (State, Zipcode) Customer satisfaction score
Both tables were connected using Client_Num, forming a customer-level data model.
During the data preparation phase:
Created a one-to-one relationship between the Customer and CreditCard tables Built calculated columns such as: Age Group Income Group Numeric Week Number (for proper sorting) Handled data type issues (e.g., converting week values from text to numeric) Ensured all columns had 100% valid values with no errors or nulls
Key visuals:
- Customers by State
- Customers by Marital Status
- Customers by Age Group
- Customers by Education Level
- Customers by Gender
- Customers by Income Group
- Customers by Occupation
KPI:
- Total Customers
- Average Income
- Average Customer Age
- Average Credit Limit
- Average Satisfaction Score
Slicers:
- Gender
- Age Group
- Income Group
Purpose: To understand who the customers are and how they are distributed across different segments.
Key visuals:
- Revenue by Spending Category
- Weekly Revenue Trend
- Revenue by Education Level
- Revenue by Occupation
- Revenue by Card Category
- Revenue by Use Method
- Credit Limit vs Utilization (Scatter Plot)
KPI:
- Total Revenue
- Total Transactions
- Current Week Revenue
- Previous Week Revenue
- Interest Earned
- Average utilization
Slicers:
- Gender
- Week Number
- Expense Type
Purpose: To analyze how customers spend and how revenue changes over time.
- Spending is highest in categories like Bills and Fuel
- Revenue shows fluctuations across different weeks
- Higher credit limits do not always mean higher utilization.
- Income and age groups show different spending patterns.
- Educated and working professionals contribute significantly to revenue.
- Customer demographics strongly influence spending behavior.
- Car/house ownership correlates with higher average credit limits.
- Week values were stored as text, causing sorting issues, and converted to numeric for trend analysis.
- Circular dependency errors during week sorting were resolved using a separate numeric week column.
- Scatter plot initially showed one point; fixed using legend disaggregation.
- Ensuring slicers interacted correctly with all visuals required configuration.
- Focus more on high-revenue categories like Bills and Fuel by giving offers and rewards
- Target high-value customers (educated and working professionals) with personalized services -Encourage customers with high credit limits to use more through discounts and EMI options -Improve weekly revenue by running regular offers and campaigns -Create different strategies for different income and age groups
This project demonstrates an end-to-end Power BI workflow:
- Data cleaning and transformation
- Data modeling
- DAX calculations
- Interactive dashboard design
- Business insight generation
Zeenaat Hussain