🎼 Digital_Music_Store_Analysis_Project

Transformed raw music store data into an interactive Power BI dashboard using SQL for analysis.

Project Overview

This project performs a comprehensive analysis of a digital music store's database. By applying SQL for complex data extraction and Power BI for interactive visualization, I transformed raw transactional data into actionable business insights. The goal was to identify top-performing regions, loyal customers, organizational hierarchy, and genre-specific trends to drive marketing and operational strategies.

🧰 Tools used

1. SQL (PostgreSQL): The language used for querying the data.
2. pgAdmin4: Used to "talk" to the database and get specific answers.
3. Power BI: Used to create the interactive dashboard.

❓ The Problem

The music store had a lot of sales data but didn’t know how to use it to grow. They were struggling to identify their best customers, which countries were buying the most music, and which genres were actually making the most money.

• Wrong Marketing: They were sending the same emails to everyone. For example, if a customer only buys Jazz, sending them Rock promotions is a waste of effort.
• Hidden Spenders: They couldn't identify who their biggest spenders were.
• Outdated Focus: They were promoting artists that no one listens to anymore while ignoring the genres that are actually trending.

ℹ️ Data Source

I used a digital music database called the Chinook dataset which mimics a real store like iTunes. It contains 11 relational tables including information on artists, songs, customer purchases, and employee details.

🧠 Analysis Logic

My approach was to use Segmentation to find the right groups, then determine the best Channel (such as email) to reach them with content they actually care about.

• Data Extraction (SQL): I wrote optimized SQL queries to "talk" to the database and extract specific answers, such as identifying who spent the most money and popular genres.
• Data Modeling (Power BI): I cleaned the data and established relationships between tables in Power BI so they could "work together."

🎯 Key Insights

• Market Leadership: Identified the countries with the highest customer volume, helping the store decide where to grow their business next.
• Customer Behavior: Found that a small percentage of "Best Customers" contribute to a significant portion of revenue. This highlights that a rewards program would be a great idea.
• Content Strategy: The analysis of the top 10 Rock bands, which account for 55.39% of total track sales. This provides the store a clear plan for which artists to promote next.

✔️ Recommendations

• Reward Big Spenders: Create a special "VIP Club" for the top customers identified to keep them coming back.
• Target Specific Cities: Instead of advertising everywhere, the store should focus its marketing budget on the top 5 cities identified in the report. Host promotional music festivals or 'Rock Nights' in cities like Prague or London, which showed the highest density of Rock music listeners.
• Focus on Rock Music: Since the data shows Rock is the top seller, the store should sign new contracts or deals with the top 10 Rock bands as this genre makes the most money across all global markets.

🏁 Conclusion

This project demonstrates how to turn raw data into data-driven business decisions. By combining SQL and Power BI together, I developed an interactive dashboard that allows stakeholders to explore sales trends and customer behavior to see what is really driving the business forward.