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Project Overview
Project Overview
Coffee Shop End-to-End Analytics
This project analyzes sales transactions from a coffee shop to identify revenue drivers, customer demand patterns, and product category performance.
The goal was to transform raw transactional data into actionable business insights that support data-driven decisions for pricing, product mix, and operations.
Dataset
Dataset
Transactional sales dataset including:
• Order Date & Time
• Product Category
• Product Name
• Quantity Sold
• Revenue & Cost
The dataset represents multiple days of coffee shop transactions used to simulate real business performance analysis.
Tools Used
Tools Used
SQL – Data extraction and transformation
Python – Exploratory Data Analysis (EDA)
Excel – Data cleaning and validation
Power BI – Interactive dashboard and visualization
Analysis Approach
The dataset was cleaned and prepared using Excel and SQL.
Exploratory data analysis was performed in Python to identify patterns in sales, product performance, and demand timing.
Key business metrics such as revenue, profit, order volume, and average order value were calculated.
Finally, an interactive Power BI dashboard was developed to visualize trends and support business decision-making.
Key Business Questions
Key Business Questions
• Which product categories generate the highest revenue?
• What time of day drives the most orders?
• What products have the highest profit margins?
• What is the average order value?
Key Insights
Key Insights
• Peak sales occur during morning hours indicating strong breakfast demand
• Coffee beverages contribute the majority of total revenue
• Some high-volume items have lower margins suggesting pricing optimization opportunities
• Order patterns show strong weekday morning spikes
Business Impact
Business Impact
These insights can help a coffee shop:
• Optimize menu pricing
• Improve inventory planning
• Schedule staff during peak demand hours
• Focus promotions on high-margin items
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