Dark Mode
Maven Roasters Coffee Sales Data
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides transactional records from Maven Roasters, a fictional coffee shop based in New York City, operating across three different locations. It is designed for exploring evolving sales trends over time, identifying peak customer traffic days, and examining the performance metrics of various products. The dataset includes details such as transaction dates, timestamps, geographical specifics, and product-level information. It enables users to analyse the frequency of product sales, pinpoint top revenue drivers, and investigate factors contributing to fluctuations in sales volume.
Columns
- transaction_id: A unique numeric identifier for each transaction.
- transaction_date: The date when the transaction occurred, in YYYY-MM-DD format.
- transaction_time: The time of the transaction, in HH:MM:SS format.
- transaction_qty: The numeric quantity of products purchased in a transaction.
- store_id: A unique numeric identifier for each store location.
- store_location: Text detailing the name or description of the store's physical location.
- product_id: A unique numeric identifier for each product sold.
- unit_price: The numeric price of a single unit of the product in the transaction.
- product_category: Text indicating the general category to which the product belongs (e.g., Coffee, Tea, Drinking Chocolate).
- product_type: Text specifying the type or variant of the product (e.g., Gourmet brewed coffee, Brewed Chai tea, Hot chocolate).
- product_detail: Additional textual details about the product (e.g., specific flavour, size, or blend).
Distribution
The dataset is provided as a CSV file,
coffee-shop-sales-revenue.csv, with a size of 13.81 MB. It contains 11 columns and approximately 149,116 records. For some fields like 'transaction_date' and 'transaction_time', the summary data indicates a fixed value of '17Aug25' for minimum, mean, and maximum. Other fields like 'store_location', 'product_category', 'product_type', and 'product_detail' show 100% null values in the provided sample summary, and quantitative fields like 'transaction_qty', 'store_id', and 'unit_price' have 'NaN' for statistical measures.Usage
This dataset is ideal for:
- Exploring evolving sales trends and patterns over time.
- Identifying peak customer traffic days and times.
- Delving into the performance metrics of various products.
- Analysing product sales frequency.
- Pinpointing top revenue drivers within a coffee shop setting.
- Investigating factors contributing to sales volume fluctuations.
- Conducting business intelligence, exploratory data analysis, time series analysis, and geospatial analysis.
Coverage
The dataset covers transactional records from a fictional coffee shop chain, Maven Roasters, operating across three distinct locations in New York City. The time range indicated in the sample data summaries is '17Aug25'. The dataset is expected to be updated annually. Specific demographic scope is not provided beyond the general context of coffee shop customers.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Analysts: To uncover sales trends, product performance, and operational insights.
- Business Intelligence Professionals: For creating dashboards and reports on sales, revenue, and customer behaviour.
- Students and Researchers: For academic projects, case studies, or research in retail analytics, sales forecasting, and consumer patterns.
- Retail Businesses: To gain insights into managing sales data and understanding customer purchase habits.
Dataset Name Suggestions
- Maven Roasters Coffee Sales Data
- NYC Coffee Shop Transaction Records
- Coffee Retail Sales Analytics
- Coffee Shop Daily Sales Data
- Maven Roasters Sales & Revenue
Attributes
Original Data Source: Maven Roasters Coffee Sales Data
QUALITY