This Looker Studio project demonstrates advanced business intelligence skills through comprehensive customer loyalty program analysis. As a Data Visualization Specialist, I created an interactive multi-page dashboard that transforms raw customer transaction data into actionable business insights using Google's Looker Studio platform.
📂 Interactive-Customer-Analytics-Dashboard/
│
├── 📂 Data/
│ ├── CustomerLoyaltyProgram.csv # Primary dataset (1,000+ records)
│ ├── cust_loyalty_table.csv # Transaction data subset
│ └── cust_table.csv # Customer demographic data
│
├── 📂 Screenshots/
│ ├── Page1_Product_Sales.png
│ ├── Page2_Customer_Analytics.png
│ ├── Page3_City_Sales_Performance.png
│ ├── Page4_Geographic_Insights.png
│ ├── Page5_City_Sales_Performance
│ ├── Page6_Regional_Revenue_Map.png
│ └── Page7_Revenue_Fluctuations_Across_Countries.png
│
├── 📜 README.md # This documentation
└── 📜 Simple_Dashboard.pdf # Dashboard documentation
- Page Customization: Professional blue background with landscape layout
- Multi-Page Structure: 4 distinct analytical pages with logical organization
- Page Management: Renamed pages (A - Product Sales, B - Customer, etc.)
- Navigation Setup: Intuitive page switching and management system
- Data Integration: Uploaded and configured 3 separate CSV data sources
- Table Relationships: Established Left Outer Join on Customer ID
- Blended Bar Chart: Gender-based quantity sold analysis with dual datasets
- Dynamic Aggregation: Implemented SUM/AVG metric toggling with sorting
- Drop-down Controls: Gender selection for dynamic chart filtering
- Fixed-size Lists: Product line selection with checkbox interface
- Advanced Filters: Year-based and country exclusion filters
- User Testing: Validated all controls in View mode for optimal UX
- Geographic Bubble Map: City-based sales distribution with product line colors
- Interactive Heatmap: Quantity sold density visualization with satellite layer
- Hierarchical Treemap: Country → Province → City drill-down analysis
- Temporal Slider: Year range selection (2017-2020) for time-based filtering
- Gender Purchasing Patterns: Distinct quantity sold differences between males and females
- Regional Performance: Geographic hotspots for different product categories
- Customer Segmentation: Loyalty status impact on purchasing behavior
- Revenue vs Unit Price: Correlation analysis across different cities
- Product Line Comparison: Performance variations across categories
- Seasonal Trends: Year and quarter-based sales patterns
- Sales Concentration: Major cities driving majority of revenue
- Regional Preferences: Product line popularity by geographic area
- Market Penetration: Customer density mapping for expansion planning
- Multi-Table Data Blending with Left Outer Joins
- Google Maps Integration with bubble and heatmap layers
- Hierarchical Data Representation using treemaps
- Dynamic Parameter Controls for user-driven exploration
- Professional Color Schemes with consistent branding
- Multi-Page Navigation with logical information flow
- Responsive Design Principles for different screen sizes
- User-Centric Interface with intuitive control placement
- Data Hierarchy Implementation for drill-down capabilities
- Filter Propagation across related visualizations
| Column | Description | Type | Source Table |
|---|---|---|---|
Customer_ID |
Unique customer identifier | String | Both Tables |
Full_Name |
Customer's complete name | String | cust_table |
Gender |
Customer gender | String | cust_table |
City |
Customer location city | String | Both Tables |
Country |
Customer location country | String | Both Tables |
Order_Year |
Transaction year (2015-2020) | Integer | cust_loyalty_table |
Product_Line |
Product category | String | cust_loyalty_table |
Quantity_Sold |
Units sold per transaction | Integer | cust_loyalty_table |
Unit_Sale_Price |
Price per unit | Float | cust_loyalty_table |
Revenue |
Total transaction revenue | Float | cust_loyalty_table |
Loyalty_Status |
Customer loyalty level | String | cust_loyalty_table |
Months_As_Member |
Loyalty program duration | Integer | cust_loyalty_table |
- Start with Product Sales Page: Review overall sales performance
- Analyze Customer Demographics: Understand purchasing patterns by gender
- Explore Geographic Distribution: Identify sales hotspots and opportunities
- Use Interactive Controls: Filter data by year, product line, and location
- Study Data Blending: Examine the Left Outer Join implementation
- Analyze Control Configuration: Review drop-down and slider setups
- Evaluate Visualization Choices: Understand chart type selection rationale
- Replicate Techniques: Apply similar approaches to your projects
- Follow Exercise Sequence: Complete the 4 exercises step-by-step
- Experiment with Controls: Test all interactive elements
- Modify Visualizations: Practice with different chart configurations
- Extend Functionality: Add additional data sources or visualizations
✅ 4 Comprehensive Dashboard Pages with distinct analytical focuses
✅ 10+ Advanced Visualization Types including maps and treemaps
✅ Multiple Data Source Integration with seamless blending
✅ Complete Interactive Controls Suite for user exploration
✅ Professional Dashboard Design following BI best practices
✅ Geospatial Analysis Implementation with Google Maps integration
- Google Account (free)
- Looker Studio access (available at no charge)
- Web browser with internet connection
- Primary:
CustomerLoyaltyProgram.csv(IBM Cognos Analytics showcase) - Derived:
cust_loyalty_table.csvandcust_table.csvsubsets - Total Records: 1,000+ customer transactions
- Time Range: 2015-2020 data
- Uploaded 3 CSV files to Looker Studio
- Validated data types and field formats
- Created calculated fields for enhanced analysis
- Established 4-page dashboard architecture
- Applied consistent color scheme and styling
- Implemented professional layout principles
- Configured table relationships using Customer ID
- Implemented Left Outer Join for comprehensive analysis
- Created blended data sources for cross-table queries
- Added user controls for dynamic filtering
- Configured filter propagation across visualizations
- Tested all controls in View mode for optimal UX
- Created geographic maps with multiple layers
- Implemented hierarchical treemaps with drill-down
- Added temporal filtering with slider controls
- Primary: Professional blue theme across all pages
- Highlighting: Contrast colors for important metrics
- Consistency: Uniform palette for brand recognition
- Logical Flow: Progressive analysis from overview to details
- Information Hierarchy: Clear visual hierarchy for data presentation
- White Space Management: Optimal spacing for readability
- Intuitive Navigation: Clear page switching and controls
- Responsive Design: Adaptable to different screen sizes
- Accessible Design: Color choices for visibility
- Google Looker Studio Documentation
- IBM Cognos Analytics Resources
- Data Visualization Best Practices
- Business Intelligence Trends
- IBM for the comprehensive Business Intelligence curriculum
- Google for providing Looker Studio as a free BI platform
- Coursera for the structured learning environment
- Business Intelligence Community for best practices and inspiration
This educational project uses IBM's showcase dataset for learning purposes. The dashboard implementation and visualization techniques are shared for educational and portfolio development.
⭐ If you find this dashboard insightful, please share feedback! ⭐
Lab Completed: December 2025
Last Updated: December 2025