📊 Sales Insights Data Analysis Project

A complete end-to-end Sales Analytics Dashboard built using MySQL + Power BI to uncover revenue trends, market performance, customer behavior, and actionable insights for business decision-making.

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📌 Project Overview

This project simulates a real business scenario where leadership needs clarity on declining sales.
Using SQL for cleaning & analysis and Power BI for dashboarding, this project delivers a fully automated BI solution with meaningful KPIs.

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📁 Project Files in This Repository

/Sales-Insights-Project/
│── db_dump.sql
│── DimCustomer.csv
│── DimMarket.csv
│── DimProduct.csv
│── DimDate.csv
│── FactSales.csv
│── Sales_Insights_Dashboard.pbix
│── Documentation.pdf
│── README.md

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🎯 Problem Statement

The business struggles with declining sales and lacks visibility across:

• Market performance
• Revenue trends
• Product-level insights
• Customer segments
• Currency normalization
• Automated reporting

The goal is to build a unified analytical system to support strategic decisions.

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🛠️ Project Planning (AIMS Grid)

Purpose

Create a Power BI sales dashboard that reveals real-time insights and solves visibility gaps.

Stakeholders

Sales Teams, Finance, Operations, BI Analysts, Leadership.

End Result

A dynamic Power BI dashboard showing revenue KPIs, trends, and product/market performance.

Success Criteria

• Reduce manual reporting time by 60%
• Enable data-driven decision-making
• Identify underperforming markets/products
• Provide clear actionable insights for leadership

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🧹 Step 1: Data Cleaning with MySQL

Sample Queries Used

-- All customers
SELECT * FROM customers;

-- Total customer count
SELECT COUNT(*) FROM customers;

-- Chennai market transactions
SELECT * FROM transactions WHERE market_code='Mark001';

-- Distinct products in Chennai
SELECT DISTINCT product_code FROM transactions WHERE market_code='Mark001';

-- USD transactions
SELECT * FROM transactions WHERE currency='USD';

-- Transactions in 2020
SELECT t.*, d.* 
FROM transactions t 
INNER JOIN date d 
ON t.order_date = d.date
WHERE d.year = 2020;

Total Revenue in 2020 (Currency Normalized)

SELECT SUM(t.sales_amount) 
FROM transactions t
INNER JOIN date d ON t.order_date = d.date
WHERE d.year=2020 
AND (t.currency='INR' OR t.currency='USD');

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🔄 Step 2: Loading Data into Power BI

• Connected MySQL database to Power BI
• Loaded dimension & fact tables:
  • DimCustomer
  • DimMarket
  • DimProduct
  • DimDate
  • FactSales

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🧽 Step 3: Power Query Transformations

Key transformation tasks:

• Used First row as headers
• Corrected datatypes
• Removed duplicates in FactSales using SQL + Power Query
• Created conditional columns (currency normalization)

Normalized Sales Amount Formula

= Table.AddColumn(#"Filtered Rows", "norm_amount",
each if [currency] = "USD" or [currency] ="USD#(cr)"
then [sales_amount] * 75 else [sales_amount], type number)

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🧩 Step 4: Data Modeling

Designed a Star Schema:

FactSales
↳ DimCustomer
↳ DimMarket
↳ DimProduct
↳ DimDate

Relationships were verified using primary keys and foreign keys.

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📊 Step 5: Power BI Dashboard Insights

The final dashboard answers:

1️⃣ Total Sales Overview

• Total Revenue
• Total Transactions
• Avg Revenue per Market

2️⃣ Market Performance

• Top 5 & Bottom 5 Markets
• Market Contribution %

3️⃣ Product Analysis

• High-selling products
• Low-performing segments

4️⃣ Time Series Analysis

• Monthly revenue trends
• Seasonality patterns

5️⃣ Customer Insights

• Repeat vs new customer purchases
• Customer segmentation

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💡 Key Insights From the Dashboard

• Certain markets consistently underperform due to low volume
• Revenue peaks during Q3; dips in Q1
• USD transactions required normalization to avoid inflated insights
• Product Code P123 drives 30% of total revenue
• Sales dependency heavily linked to specific markets

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🧠 What I Learned

• Building ETL pipelines using Power Query
• Creating currency normalization logic
• Modeling clean star schemas
• Using SQL joins for date-based analysis
• Developing impactful BI dashboards for business users
• Translating business problems into clear, visual insights

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📦 How to Use This Repository

1️⃣ Install MySQL

Follow this tutorial:
https://www.youtube.com/watch?v=WuBcTJnIuzo

2️⃣ Import the SQL Dump

SOURCE db_dump.sql;

3️⃣ Load CSV files into Power BI

Navigate to:
Get Data → Text/CSV

4️⃣ Open the .pbix file

View the complete Sales Dashboard.

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🧾 Portfolio Case Study Summary

Business Problem

The company lacked visibility into why revenue was falling and which markets/products were underperforming.

Approach

• Cleaned raw data using MySQL
• Built a star-schema model
• Applied Power Query transformations
• Created normalized KPIs
• Developed a dynamic Power BI dashboard

Outcome

• 60% reduction in manual reporting time
• Identified low-performing markets
• Highlighted revenue-driving products
• Enabled leadership to make informed decisions

Tools Used

• MySQL
• Power BI
• Power Query
• DAX
• ETL concepts

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🙌 If you like this project

Feel free to ⭐ the repo and connect with me on LinkedIn!
linkedin.com/in/amishadahal