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Coffee Shop Daily Sales Data
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About
This dataset is designed for predicting daily coffee shop revenue, offering detailed insights into various factors that influence daily sales. It includes key operational and environmental variables that provide a clear view of how business activities and external conditions impact sales performance. The dataset is a valuable resource for understanding the relationship between customer behaviour, operational decisions, and revenue generation in the food and beverage industry. Daily revenue, the main target variable, is calculated from customer visits, average spending, marketing expenditure, and staffing levels.
Columns
- Number of Customers Per Day: The total count of customers visiting the coffee shop daily. (Range: 50 - 500 customers).
- Average Order Value (£): The average monetary amount spent by each customer per visit. (Range: £2.50 - £10.00).
- Operating Hours Per Day: The total number of hours the coffee shop is open for business each day. (Range: 6 - 18 hours).
- Number of Employees: The employee count working on a given day, influencing service speed, customer satisfaction, and sales. (Range: 2 - 15 employees).
- Marketing Spend Per Day (£): The amount of money allocated to marketing campaigns or promotions daily. (Range: £10 - £500 per day).
- Location Foot Traffic (people/hour): The number of people passing by the coffee shop per hour, indicating the location's potential to attract customers. (Range: 50 - 1000 people per hour).
- Daily Revenue (£): This is the dependent variable, representing the total revenue generated by the coffee shop each day. (Range: £200 - £10,000 per day).
Distribution
The dataset contains 2,000 rows of data across 7 columns, saved as
coffee_shop_revenue.csv with a size of 66.47 kB. All columns have 2,000 valid entries with no mismatched or missing values. It covers a diverse array of operational scenarios, from smaller neighbourhood coffee shops with limited traffic to larger, high-traffic establishments with substantial marketing budgets.Usage
This dataset offers a broad spectrum of applications, particularly in predictive analytics, business optimisation, and forecasting. It can be used for:
- Predictive Modelling: Developing machine learning models (e.g., regression, decision trees, neural networks) to forecast daily revenue based on operational data.
- Business Strategy Development: Analysing how adjustments in marketing expenditure, staff numbers, or operating hours can optimise revenue and enhance efficiency.
- Customer Insights: Identifying patterns in customer behaviour linked to shop operations and external factors such as foot traffic and marketing initiatives.
- Resource Allocation: Determining optimal staffing levels and marketing budgets based on predicted sales to improve overall profitability.
- Optimise Marketing Campaigns: Evaluating the effectiveness of daily or seasonal marketing campaigns on revenue.
- Staff Scheduling: Predicting busy days to ensure appropriate employee scheduling for maximum efficiency.
- Revenue Forecasting: Providing accurate revenue projections for financial planning and decision-making.
- Operational Efficiency: Discovering the most profitable operating hours and adjusting business hours accordingly.
Coverage
The dataset focuses on daily operational variables and revenue generation for coffee shops within the food and beverage industry. Specific geographic, time range, or demographic scopes are not explicitly detailed within the provided information, but the data is relevant to various operational scenarios found in coffee shops.
License
CC0: Public Domain
Who Can Use It
This dataset is an essential tool for:
- Coffee shop owners, managers, and analysts in the food and beverage industry seeking to refine daily operations and boost profitability.
- Aspiring data scientists and machine learning practitioners interested in applying their skills to real-world business challenges in the food and beverage sector.
- Anyone looking to understand the interplay between customer behaviour, operational choices, and revenue generation in this industry.
Dataset Name Suggestions
- Coffee Shop Daily Sales Data
- Coffee Business Revenue Prediction
- Food & Beverage Daily Performance
- Cafe Sales Forecasting Dataset
Attributes
Original Data Source: Coffee Shop Daily Sales Data
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