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Coffee Shop Sales Analysis Using Python

Header

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

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

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