sreejabethu / starbucks-sales-data-analysis

This project analyzes simulated sales data from various Starbucks locations to uncover insights into customer behavior, product performance, and sales trends. The analysis includes data cleaning, exploratory data analysis (EDA), and visualization to identify patterns that can help optimize operations, enhance customer experience, & guide marketing

Starbucks Sales Data Analysis

Overview

This project focuses on analyzing sales data from Starbucks to gain insights into customer behavior, product performance, and sales trends across various locations. The analysis aims to help Starbucks optimize its operations, tailor marketing strategies, and enhance the overall customer experience.

Table of Contents

• Project Structure
• Data
• Installation
• Usage
• Analysis
• Results
• Contributing
• License

Project Structure

The project directory is structured as follows:

Starbucks-Sales-Analysis/ │ ├── data/ │ └── starbucks_sales_data.csv # The dataset used for analysis │ ├── notebooks/ │ └── Starbucks_Sales_Analysis.ipynb # Jupyter Notebook with analysis │ ├── src/ │ ├── data_cleaning.py # Data cleaning scripts │ ├── eda.py # Exploratory data analysis scripts │ ├── visualization.py # Visualization scripts │ ├── requirements.txt # Python dependencies ├── README.md # Project documentation └── .gitignore # Files and directories to ignore in Git

Data

The dataset starbucks_sales_data.csv contains simulated sales records from various Starbucks locations, capturing details such as:

• transaction_id: Unique identifier for each transaction
• transaction_date: Date of the transaction
• store_location: Starbucks store location
• product_category: Category of the product sold (e.g., Beverages, Food)
• item_name: Name of the item sold
• quantity_sold: Quantity of the item sold
• transaction_amount: Total amount for the transaction
• customer_id: Unique identifier for the customer

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/Starbucks-Sales-Analysis.git
   cd Starbucks-Sales-Analysis
   

2.Create a virtual environment (optional but recommended): python -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate

3.Install the required dependencies: pip install -r requirements.txt

Usage Running the Analysis The analysis can be run using the Jupyter Notebook provided in the notebooks directory. To start the notebook: jupyter notebook notebooks/Starbucks_Sales_Analysis.ipynb

Alternatively, you can run the analysis scripts from the src directory:

python src/data_cleaning.py python src/eda.py python src/visualization.py

Analyzing Sales Data The analysis includes:

Sales Trends: Understand sales over time, by day of the week, and across different store locations.
Customer Segmentation: Segment customers based on their purchasing behavior and average spending.
Product Performance: Evaluate the performance of different product categories and individual items.
Revenue Analysis: Calculate key metrics such as average transaction value (ATV) and sales per store.

Results

The key findings and insights from the analysis include:

Peak Sales Periods: Identified the days and times when sales are highest.
Top Performing Products: Discovered which products are driving the most revenue.
Customer Insights: Segmented customers based on visit frequency and spending habits, allowing for targeted marketing strategies.

These insights can help Starbucks optimize its operations, improve customer experience, and enhance marketing efforts.