Sales-Performance-Analysis

📊 Sales Performance Analysis – End-to-End Data Analyst Project

📝 Project Overview

This project provides a comprehensive sales performance analysis using SQL, Python, Power BI, and professional reporting. It demonstrates the full data analyst workflow: data cleaning, KPI creation, exploratory data analysis (EDA), A/B testing, dashboarding, and business reporting. The objective is to uncover sales trends, evaluate performance vs. targets, and provide actionable insights for better decision-making.

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🎯 Objectives

• Clean and prepare raw sales data for analysis.
• Create KPIs and SQL/T-SQL queries to evaluate performance.
• Perform Exploratory Data Analysis (EDA) with Python.
• Run A/B tests to evaluate business strategies.
• Build interactive dashboards for insights.
• Summarize findings in professional business reports.

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🛠️ Tech Stack

• SQL / T-SQL – Data cleaning, KPI calculations, queries for insights.
• Python (Pandas, Matplotlib, Seaborn, SciPy) – EDA and A/B testing.
• Power BI – Interactive dashboarding and visualization.
• Jupyter Notebook – Analysis documentation.
• MS Word / PDF – Final reports for stakeholders.

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📂 Project Structure

Sales Performance/
│
├── AB Testing/
│   └── ab_test_basic.py                 # A/B testing in Python
│
├── Dashboard/
│   └── Sales_Performance.pbix           # Power BI dashboard
│
├── Data Sets/
│   ├── Sales_Raw.csv                    # Raw dataset
│   ├── Sales_Cleaned.csv                # Cleaned dataset (CSV)
│   ├── Sales_Cleaned.xlsx               # Cleaned dataset (Excel)
│   ├── List of Orders.csv               # Orders data
│   ├── Order Details.csv                # Order details
│   └── Sales_Target.csv                 # Sales targets
│
├── EDA Analysis/
│   └── Sales_EDA.ipynb                  # Jupyter notebook for EDA
│
├── SQL/
│   ├── 01_data_cleaning.sql             # Cleaning scripts
│   ├── 02_data_quality.sql              # Quality checks
│   ├── 03_feature_engineering.sql       # Feature creation
│   ├── 05_kpi_calculations.sql          # KPI monitoring
│   └── ...                              # Other analysis queries
│
├── T-SQL/
│   └── sales_queries_tsql.sql           # SQL Server version queries
│
├── Images/
│   ├── Chart (1-15).png                 # Visualization charts
│
├── Reports/
│   ├── Sales_Performance_Dashboard_Report.pdf  # Dashboard summary
│   └── Sales_Performance_Overall_Report.pdf    # Full project report

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🔎 Key Analyses & Deliverables

1. Data Cleaning & KPI Monitoring (SQL)

• Queries to handle missing values, validate data quality, and calculate monthly KPIs.
• KPI metrics: revenue, profit margin, sales by category, and regional performance.

2. Exploratory Data Analysis (Python)

• Descriptive statistics of sales and profit.
• Regional performance and category-level insights.
• Seasonal trends and profit margin distribution.

3. Dashboarding (Power BI)

• Interactive dashboard tracking:
  • Sales trends
  • Category performance
  • Profit distribution
  • Top customers and states

📸 Preview: Sales Overview Regional Performance Customer Insights Category Performance

4. A/B Testing (Python)

• Designed an experiment to measure promotional impact.
• Evaluated performance differences between control and test groups.

5. Business Reporting

• Final reports created in Word/PDF for management.
• Summarized insights with business recommendations.

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🔗 Links to Visualizations

• Average Variance % by Category
• Category Sales vs Target
• Category Performance
• Customer Insights
• Monthly Profit Margin
• Monthly Sales Trend
• Profit Distribution
• Profit Margin Distribution
• Profit vs Sales
• Regional Performance
• Sales Amount Distribution
• Sales Overview
• Sales & Profit by Category
• Top 10 States by Sales
• Top 10 Sub-Categories by Sales

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📈 Key Insights

• Top 10 states contributed ~60% of total sales.
• Certain product categories underperformed vs. targets by ~15%.
• Seasonal spikes in Q4 highlight the importance of promotions.
• A/B test showed Strategy B improved customer retention by 12%.

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🚀 Getting Started

Clone the Repository

git clone https://github.com/Sohitha-01/Sales-Performance-Analysis.git
cd Sales-Performance-Analysis

Explore the Data

• Navigate to Data Sets/ for raw and cleaned datasets.
• Run notebooks in EDA Analysis/ for exploratory analysis.

Run SQL Queries

• Open scripts in /SQL or /T-SQL in your preferred database environment.

View Dashboards

• Open Dashboard/Sales_Performance.pbix in Power BI.

Visual Reports

• View PNG charts in /Images/.
• Read reports in /Reports/ for summarized business insights.

Run A/B Test

• Navigate to AB Testing/ and run ab_test_basic.py.

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📊 Visualizations

The Images/ folder contains 15 charts covering:

• Sales trends over time
• Profit margin distribution
• Category performance
• Top regions by sales
• Target vs. actual comparisons

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🏆 Why This Project Matters

This project demonstrates the end-to-end skillset of a data analyst: ✔️ SQL/T-SQL for ETL and KPIs
✔️ Python for EDA and A/B testing
✔️ Power BI for interactive dashboards
✔️ Business reports for stakeholder communication

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🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m "Add feature").
4. Push to your branch (git push origin feature-name).
5. Open a Pull Request.

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📜 License

This project is open-source and licensed. Feel free to use it for learning or portfolio purposes.