☕ Coffee Sales Analysis Project

Welcome to the Coffee Shop Sales Analysis Project!

This repository showcases a comprehensive Coffee Shop Sales Dashboard built using Power BI for visualization and SQL for data extraction and transformation. The project dives deep into sales performance, providing actionable insights for data-driven decisions.

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📂 Project Overview

This project focuses on analyzing coffee shop sales data to uncover patterns, identify trends, and provide insights for business optimization.

🔑 Key Objectives

• Analyze overall sales performance, including total sales, orders, and quantities sold.
• Explore sales trends over time, including weekday vs. weekend performance.
• Identify the best-performing products and top-selling store locations.
• Visualize peak sales hours and days using advanced Power BI visualizations. 🛠️ Tools & Techniques

1. Power BI

• Created interactive dashboards with slicers, heatmaps, and KPIs.
• Visualized sales performance across different dimensions like time, product category, and location.
• Used DAX for calculations like Month-over-Month (MoM) growth.

2. SQL

• Extracted and transformed data from raw transactional tables.
• Performed complex aggregations, joins, and trend analysis.
• Calculated MoM growth, daily/hourly trends, and top-performing entities using advanced SQL queries.

📊 Dashboard Features

1. KPI Metrics

• Total Sales: $82K
• Total Orders: 17,314
• Total Quantity Sold: 24,870

2. Sales Trends

• Month-over-Month growth rates for sales and orders.
• Daily average sales trends over the selected period.

3. Product Category Insights

• Top-performing categories like Coffee ($31K) and Tea ($22K).
• Breakdown of individual product performance (e.g., Barista Espresso: $10.46K).

4. Store Performance

• Best-performing store locations such as Lower Manhattan ($26.54K) and Astoria ($27.31K).

5. Time-Based Analysis

• Hourly and daily peak sales visualized using heatmaps. Comparison of weekday vs. weekend performance (Weekdays: 71.64%, Weekends: 28.36%).

📝 SQL Contributions

• Monthly Sales Growth Calculation
• Top-Selling Products by Category
• Hourly Peak Sales

🎯 What I Learned

• Leveraged SQL for data wrangling, trend analysis, and advanced calculations.
• Designed visually appealing and user-friendly Power BI dashboards.
• Understood the importance of connecting business requirements with data insights.

🚀 How to Use This Repository

1. Clone the repository to access SQL queries and Power BI files.
2. Follow the setup guide to load sample data and replicate the dashboard.
3. Customize and adapt the dashboard to your data for similar analysis.

📂 Repository Contents

• SQL Queries: Contains all SQL scripts used for data preparation and analysis.
• Power BI Dashboard: Power BI file (.pbix) showcasing the final interactive dashboard.
• Sample Data: A subset of the sales dataset (anonymized).