Coffee Shop Sales Analysis Using Python

An analytical dashboard showcasing sales performance and customer behavior insights for Maven Roasters, a fictitious coffee shop chain operating in New York City.

---

Table of Contents

• Project Background
• Data Structure
• Insights and Analysis
  • Overview of Findings
  • Detailed Insights
• Recommendations
• How to Use the Dashboard
• Technologies Used
• Setup Instructions
• License

---

Project Background

Maven Roasters' dataset contains 149,117 transaction records, including timestamps, store locations, and product-level details. This project aims to:

• Analyze sales performance across three NYC locations.
• Provide insights into transaction trends, product popularity, and customer preferences.
• Develop actionable strategies to optimize operations and enhance customer satisfaction.

The dashboard visualizes key metrics such as Total Revenue, Order Volume, and Average Order Value (AOV), alongside product and location-based performance analysis.

---

Data Structure

The dataset consists of a single table (sales) with:

• 149,117 records detailing customer transactions.
• Fields including transaction_id, timestamp, store_location, product_name, and order_quantity.

---

Insights and Analysis

Overview of Findings

• Total Revenue: $698K from 149K orders, with an AOV of $4.69.
• Peak Month: June ($166K revenue), attributed to increased summer foot traffic.
• Top Store: Hell's Kitchen ($236K revenue).
• Top Product: Barista Espresso ($91K sales).
• Popular Categories: Coffee led the way with $58K, followed by Tea ($45K) and Bakery items ($22K).

Detailed Insights

1. Sales by Month and Location:

   • June: Highest revenue ($166K).
   • January: Lowest revenue, highlighting potential for seasonal promotions.

2. Product Performance:

   • Barista Espresso: $91K in sales.
   • Brewed Chai Tea: $77K in sales.
   • Coffee Beans had the highest AOV at $22.

3. Customer Behavior:

   • Peak Hour: 10 AM.
   • Peak Days: Sales consistent across weekdays (~21K orders/day).
   • Popular Coffee Type: Gourmet Brewed Coffee (29% of orders).

---

Recommendations

1. Promote High-Value Products:

   • Implement seasonal "Buy One Get One Free" offers for Coffee Beans during colder months to boost home brewing.

2. Boost Sales in Slow Months:

   • Offer reward cards or complimentary drinks during January and February to increase customer engagement.

3. Optimize Operating Hours:

   • Introduce end-of-day discounts during the 8 PM slot to drive sales.

4. Customer Feedback:

   • Collect seasonal surveys to refine services and offerings.

---

Dashboard

---

Technologies Used

• Python: Data analysis and dashboard creation.
• Pandas: Data wrangling and transformation.
• Matplotlib & Seaborn: Data visualization.

---

Setup Instructions

1. Clone this repository:

   git clone https://github.com/meabhaykr/Coffee-Shop-Sales-Analysis-Using-Python.git

2. Navigate to the project folder:

   cd Coffee-Shop-Sales-Analysis-Using-Python

3. Install dependencies:

   pip install -r requirements.txt