Data-Driven Strategy for Coffee Shop Performance

Table of Contents

• Overview
• Project Objectives
• Dataset
• Analysis Approach
• Key Insights
• Tools and Technologies
• Business Value
• How to Use
• License

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Overview

This repository presents a structured data analysis project designed to transform raw coffee shop sales data into actionable business insights. The project applies SQL queries to answer critical business questions, identify customer behavior patterns, and support data-driven decision-making.

The analysis highlights product performance, customer spending habits, seasonal demand, and operational efficiency opportunities. The final outcome is a strategy that enables the coffee shop to optimize staffing, inventory, and marketing while improving overall profitability.

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Project Objectives

• Analyze sales distribution across products, categories, and price levels.
• Identify temporal patterns such as peak hours, weekdays vs weekends, and seasonal variations.
• Assess revenue contribution by product and detect underperforming items.
• Translate data insights into actionable business recommendations.

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Dataset

• Source: Coffee shop transactional data.
• Records: ~3,500 rows.
• Features: 11 variables, including product, revenue, price category, date, season, and time.
• Assumptions: Week starts on Monday (European market context). Seasonal classification follows meteorological standards.

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Analysis Approach

The analysis is conducted through SQL queries organized into the following areas:

1. Product Performance

   • Contribution of each drink to total revenue.
   • Identification of the top three best-selling products.
   • Classification of products as hot or cold.

2. Customer Spending

   • Calculation of average daily sales.
   • Evaluation of sales distribution across low, medium, and high price categories.

3. Time and Seasonality

   • Monthly and seasonal sales performance.
   • Comparison of weekday vs weekend sales.
   • Identification of peak sales hours.

4. Recommendations

   • Operational improvements for staffing.
   • Seasonal inventory planning.
   • Promotion of high-performing products.
   • Strategy for underperforming products.
   • Payment method optimization.

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Key Insights

• Sales peak between 09:00 and 12:00.
• Strongest performance occurs in Spring, Autumn, and Winter.
• High-priced drinks contribute significantly to revenue.
• Weekdays provide steady revenue, while weekends show distinct customer behavior.
• Certain products underperform and require repositioning or removal.
• Payment methods influence average customer spending.

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Tools and Technologies

• SQL: Data querying and analysis.
• Jupyter Notebook: Documentation and workflow structure.
• Python (optional): For visualization and complementary analysis.

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Business Value

This project enables coffee shop management to:

• Align staffing with demand patterns.
• Optimize inventory according to seasonal and daily trends.
• Increase profitability by focusing on high-performing products.
• Improve customer experience with faster service and flexible payment methods.
• Design marketing strategies tailored to customer preferences and behaviors.

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How to Use

1. Clone the repository:

   git clone https://github.com/your-username/coffee-shop-performance.git
   

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License

This project is licensed under the MIT License. See the LICENSE file for details.