📊 Service Marketplace Analytics
January 18, 2026 · View on GitHub
📌 Project Overview
This project demonstrates the design and implementation of an end-to-end analytics solution built using a dimensional data model and visualized through QlikView.
The solution analyzes service marketplace transactions and reviews, providing insights into:
- Buyer behavior
- Seller performance
- Service categories
- Revenue trends
- Customer feedback
The backend follows a Star Schema (Fact–Dimension model) optimized for analytical queries and BI reporting.
🏗️ Data Model Architecture
The project uses a fact–dimension model consisting of the following tables:
⭐ Fact Tables
fact_transaction
Captures all service transactions.
Measures & Attributes:
- ServiceCost
- PurchaseDate
- PaymentMethod
Relationships:
- Linked to Buyer, Seller, Service, and Time dimensions
fact_review
Stores buyer reviews and ratings.
Measures & Attributes:
- Rating
- ReviewDate
- ReviewComment
Purpose:
- Enables both qualitative and quantitative performance analysis
📐 Dimension Tables
dim_service
- Service category
- Price range
- Service description
dim_buyer
- Buyer profile
- Buyer location
dim_seller
- Seller profile
- Experience level
- Pricing range
dim_time
- Date
- Day
- Month
- Quarter
- Year
- Weekday
This structure enables fast slicing, filtering, and aggregation in BI tools like QlikView.
🧠 Dimensional Model Design (Star Schema)
The data model follows a Star Schema design where:
- Centralized fact tables store measurable business events
- Surrounding dimension tables provide descriptive business context
Benefits:
- High query performance
- Easy scalability
- Clear business interpretation
- BI-friendly structure
📊 QlikView Dashboard Features
The QlikView dashboard built on this model provides the following insights:
🔹 Key KPIs
- Total Revenue
- Number of Transactions
- Average Service Cost
- Average Ratings
- Total Reviews Count
🔹 Analytical Views
- Revenue by Service Category
- Seller Performance Analysis
- Buyer Location Distribution
- Monthly & Yearly Transaction Trends
- Ratings vs Revenue Comparison
- Review Sentiment Overview (text-based insights)
🔹 Interactivity
- Dynamic filters (Time, Category, Seller, Buyer)
- Cross-filtering between charts
- Drill-down analysis by date and category
- Real-time KPI updates
🛠️ Tools & Technologies
- Data Modeling: MySQL Workbench
- Visualization: QlikView
- Design Approach: Dimensional Modeling (Star Schema)
📌 Key Learnings
- Designed a scalable analytical data model
- Implemented fact–dimension relationships for BI reporting
- Built interactive dashboards using QlikView
- Applied best practices in dimensional modeling
✅ Conclusion
This project showcases the complete lifecycle of a BI solution—from data modeling to interactive analytics dashboards—highlighting how structured data design enables meaningful business insights in service marketplace platforms.