This project analyzes a digital music store database using SQL to uncover valuable business insights related to customer behavior, artist performance, genre popularity, and revenue generation.
The goal is to simulate real-world business scenarios and answer strategic questions that can help management make informed decisions regarding marketing campaigns, customer retention, content acquisition, and artist partnerships.
The analysis focuses on answering questions such as:
- Who are the company's most valuable customers?
- Which genres generate the highest revenue and market share?
- Which artists drive the most sales?
- What music preferences exist across different countries?
- How concentrated or diversified is revenue generation?
- Which customer segments should be targeted for retention campaigns?
- MySQL
- MySQL Workbench
- SQL
- CSV Data Sources
- Git & GitHub
The project is built on a relational database consisting of:
- Employee
- Customer
- Invoice
- InvoiceLine
- Track
- Album
- Artist
- Genre
- MediaType
- Playlist
- PlaylistTrack
Identify the most senior employee based on job title hierarchy.
Determine countries generating the highest number of invoices.
Identify the largest individual customer purchases.
Determine top-performing cities and countries based on sales revenue.
Identify customers contributing the highest revenue.
Identify customers interested in Rock music for targeted marketing.
Determine artists with the largest Rock music catalog.
Identify artists generating the highest revenue.
Analyze spending relationships between customers and artists.
Determine regional music preferences using window functions.
Identify highest-spending customers within each country.
Measure how many unique customers each artist attracts.
Identify tracks with the highest purchase volume.
Calculate market share percentages for each music genre.
Evaluate customer loyalty and engagement by genre.
- Total Revenue: $4709.43
- Top Customer: FrantiΕ‘ek WichterlovΓ‘ ($144.54)
- Highest Revenue Country: USA ($1040.49)
- Highest Revenue City: Prague ($273.24)
- Highest Revenue Artist: Queen ($190.08)
- Highest Purchase Volume Artist: Queen (192 Purchases)
- Largest Audience Reach: The Rolling Stones (47 Customers)
- Largest Rock Catalog: Led Zeppelin (114 Rock Tracks)
- Highest Revenue Genre: Rock ($2608.65)
- Largest Market Share: Rock (55.39%)
- Strongest Customer Loyalty: Rock (44.66 Purchases per Customer)
- Top 10 customers contribute only 23.71% of total revenue.
- Revenue is well diversified across the customer base.
- Business risk from customer concentration is relatively low.
Rock music leads in:
- Revenue Generation
- Purchase Volume
- Market Share
- Customer Loyalty
This indicates that Rock music is the primary driver of customer demand.
Queen ranks first in:
- Revenue Generation
- Purchase Volume
This makes Queen a strong candidate for promotional campaigns and exclusive partnerships.
The Rolling Stones attract the largest audience, while Queen generates higher revenue.
This highlights the difference between:
- Audience Reach
- Revenue Contribution
The top 10 customers contribute less than 25% of total revenue.
This suggests:
- Lower dependency on a small customer group
- More stable business performance
Rock and Metal attract a similar number of customers, but Rock customers purchase significantly more tracks.
This demonstrates stronger engagement and repeat purchasing behavior among Rock listeners.
Music_Store_BI_Project
β
βββ Dataset
β βββ album.csv
β βββ artist.csv
β βββ customer.csv
β βββ employee.csv
β βββ genre.csv
β βββ invoice.csv
β βββ invoiceline.csv
β βββ mediatype.csv
β βββ playlist.csv
β βββ playlisttrack.csv
β βββ track.csv
β
βββ SQL
β βββ 01_Table_Creation.sql
β βββ 02_Data_Loading.sql
β βββ 03_Business_Analysis.sql
β
βββ Screenshots
β
βββ README.md
- Interactive Power BI Dashboard
- Customer Segmentation Analysis
- Revenue Forecasting
- Genre Trend Analysis
- Artist Performance Dashboard
Sravan
SQL | Data Analytics | Business Intelligence
Built as part of a hands-on SQL data analytics portfolio project focused on transforming raw transactional data into actionable business insights.