📊 Dataset Description
This dataset represents a structured Telecom subscription and billing environment modeled using a Star Schema Data Warehouse design.
It contains customer subscription details, billing transactions, service usage records, and customer complaints data. The dataset is designed for Business Intelligence, Power BI modeling, SQL analytics, and data warehousing practice.
The schema includes:
🔹 Dimension Tables
dim_customer – Customer demographic and subscription information
dim_date – Date dimension for time intelligence analysis
dim_plan – Subscription plans and pricing structure
🔹 Fact Tables
fact_billing – Monthly billing transactions and revenue data
fact_usage_daily – Daily service usage metrics
fact_complaints – Customer complaint records and service issues
The dataset supports:
Revenue Analysis (MRR, ARPU, Growth Trends)
Churn Indicators & Customer Behavior Analysis
Plan Performance Evaluation
Usage Pattern Analysis
Complaint Trend Monitoring
Time Intelligence Calculations (YTD, MoM, YoY)
This dataset is ideal for:
Power BI modeling practice (PL-300 preparation)
SQL analytics training
Data Warehousing workshops
BI portfolio projects
🏢 Business Context Description
This dataset simulates a Telecommunications Company that offers multiple subscription plans to customers.
The company operates under a recurring revenue model where:
Customers subscribe to different service plans
They generate daily service usage
They are billed monthly
They may raise service complaints
Management monitors revenue, customer satisfaction, and plan performance
The business goals include:
📈 Increasing Revenue & ARPU
📉 Reducing Customer Churn
📊 Monitoring Usage vs Plan Efficiency
🛠 Improving Customer Satisfaction through Complaint Analysis
🎯 Identifying High-Value Customer Segments
🧠 Analytical Use Cases
This dataset enables solving real-world BI problems such as:
What is the Monthly Recurring Revenue trend?
Which subscription plans generate the highest revenue?
Which customer segments generate the highest usage?
Is there a correlation between high usage and complaints?
What is the churn risk based on billing or complaint behavior?
Which months show revenue seasonality?
🏗 Data Modeling Structure
The dataset follows a Star Schema Design:
One central billing/usage fact table
Linked to customer, plan, and date dimensions
Optimized for analytical performance and BI tools
Perfect for:
Creating KPIs
Building Executive Dashboards
Practicing DAX measures
Implementing Time Intelligence