https://github.com/kaushikrohida/coffee-shop-dashbaord
Building an interactive dashboard to explore and analyze coffee shop sales
https://github.com/kaushikrohida/coffee-shop-dashbaord
excel forecasting time-series
Last synced: 2 months ago
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Building an interactive dashboard to explore and analyze coffee shop sales
- Host: GitHub
- URL: https://github.com/kaushikrohida/coffee-shop-dashbaord
- Owner: kaushikrohida
- Created: 2024-10-27T02:38:44.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-10-27T20:23:34.000Z (7 months ago)
- Last Synced: 2025-01-19T07:43:46.728Z (4 months ago)
- Topics: excel, forecasting, time-series
- Homepage:
- Size: 23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Coffee Shop Sales Dashboard
## Overview
This repository contains an interactive dashboard project developed to analyze sales and transaction data for a coffee shop chain. The dashboard provides comprehensive insights into revenue trends, product performance, and sales across various locations, enabling data-driven decision-making for business optimization.
## Table of Contents
1. [Project Description](#project-description)
2. [Data Source](#data-source)
3. [Dashboard Features](#dashboard-features)
4. [Getting Started](#getting-started)
5. [Usage](#usage)
6. [Data Summary](#data-summary)
7. [Future Enhancements](#future-enhancements)
8. [Conclusion](#conclusion)
## Project Description
The Coffee Shop Sales Dashboard is designed to visualize and analyze transaction data from multiple coffee shop locations. It aims to provide stakeholders with actionable insights to improve sales strategies, optimize inventory, and enhance overall business performance.
## Data Source
The primary data source for this project is the `Coffee Shop Sales.xlsx` Excel file, which contains two sheets:
1. **Dashboards Sheet**: Contains summarized data for creating high-level visuals, including:
- Total revenue by month
- Aggregated metrics for quick insights
2. **Transactions Sheet**: Detailed transactional data for each sale, including:
- `transaction_id`: Unique identifier for each transaction
- `transaction_date`: Date and time of the transaction
- `store_location`: Location of the coffee shop
- `product_category`: Category of the product sold (e.g., Coffee, Tea, Drinking Chocolate)
- `revenue`: Revenue generated from the transaction
## Dashboard Features
1. **Monthly Revenue Analysis**
- Visualizes total revenue for each month
- Identifies trends and seasonal variations in coffee shop sales
2. **Product Category Performance**
- Breaks down sales by product category
- Highlights top-performing products and areas for improvement
3. **Store Performance Comparison**
- Compares sales performance across different coffee shop locations
- Provides insights into geographic sales patterns and local market preferences
4. **Time-Based Sales Trends**
- Analyzes sales based on the day of the week and hour of the day
- Identifies peak sales periods to optimize staffing and inventory management
5. **Interactive Filters**
- Allows users to drill down into specific date ranges, locations, or product categories
- Enables customized views for targeted analysis
## Getting Started
To get started with this project:
1. Clone the repository:
```
git clone https://github.com/kaushikrohida/coffee-shop-dashboard.git
```
2. Ensure you have the necessary tools installed:
- Microsoft Excel (for data exploration)
- A data visualization tool such as Tableau, Power BI, or Python with libraries like Plotly for creating interactive dashboards
3. Open the `Coffee Shop Sales.xlsx` file to explore the raw data
4. Use your preferred data visualization tool to connect to the Excel file and create the dashboard based on the features outlined above
## Usage
1. **Data Exploration**:
- Open the `Coffee Shop Sales.xlsx` file
- Review the "Dashboards" sheet for summarized data
- Explore the "Transactions" sheet for detailed sales information
2. **Creating Visualizations**:
- Connect your data visualization tool to the Excel file
- Use the fields provided in the Transactions sheet to create the required visualizations
- Implement interactive elements such as date filters and store selectors
3. **Analysis**:
- Use the dashboard to identify trends in monthly revenue
- Analyze product category performance to inform inventory decisions
- Compare store performances to understand successful strategies
- Examine time-based trends to optimize operational hours and staffing
## Data Summary
### Sample Transaction Data
| transaction_id | transaction_date | store_location | product_category | revenue |
|----------------|------------------|-----------------|------------------|---------|
| 1 | 2023-01-01 | Lower Manhattan | Coffee | $6.00 |
| 2 | 2023-01-01 | Lower Manhattan | Tea | $6.20 |
### Monthly Revenue Data (Sample)
| Month | Revenue |
|-------|---------|
| Jan | $26,543 |
| Feb | $25,320 |
## Future Enhancements
1. **Predictive Analysis**:
- Implement machine learning models to forecast future sales trends
- Develop algorithms for demand prediction to optimize inventory levels
2. **Customer Insights**:
- Integrate customer-level data to analyze behavior patterns
- Implement a loyalty program analysis to track customer retention and lifetime value
3. **Inventory Optimization**:
- Develop a real-time inventory tracking system
- Create alerts for low stock items based on sales velocity
4. **Mobile Application**:
- Develop a mobile version of the dashboard for on-the-go access
- Implement push notifications for important metrics and alerts
5. **Geospatial Analysis**:
- Incorporate maps to visualize sales distribution across different regions
- Analyze the impact of local events or weather on sales performance
## Conclusion
The Coffee Shop Sales Dashboard project provides a powerful tool for analyzing and visualizing sales data from multiple coffee shop locations. By leveraging this interactive dashboard, stakeholders can gain valuable insights into revenue trends, product performance, and store-specific patterns. This data-driven approach enables more informed decision-making, helping to optimize inventory management, staffing, and overall business strategies.
As the project evolves, the planned future enhancements will further increase its capabilities, potentially incorporating predictive analytics, customer insights, and advanced inventory management features. We encourage users to explore the dashboard, contribute to its development, and utilize the insights gained to drive the success of their coffee shop operations.