Global Coffee Market Size Volume Per Capita by Country, 2023 Discover more data with ReportLinker!

Dataset Overview

This dataset contains 2,000 rows of data from coffee shops, offering detailed insights into factors that influence daily revenue. It includes key operational and environmental variables that provide a comprehensive view of how business activities and external conditions affect sales performance. Designed for use in predictive analytics and business optimization, this dataset is a valuable resource for anyone looking to understand the relationship between customer behavior, operational decisions, and revenue generation in the food and beverage industry.

Columns & Variables

The dataset features a variety of columns that capture the operational details of coffee shops, including customer activity, store operations, and external factors such as marketing spend and location foot traffic.

1. Number of Customers Per Day

   • The total number of customers visiting the coffee shop on any given day.
   • Range: 50 - 500 customers.

2. Average Order Value ($)

   • The average dollar amount spent by each customer during their visit.
   • Range: $2.50 - $10.00.

3. Operating Hours Per Day

   • The total number of hours the coffee shop is open for business each day.
   • Range: 6 - 18 hours.

4. Number of Employees

   • The number of employees working on a given day. This can influence service speed, customer satisfaction, and ultimately, sales.
   • Range: 2 - 15 employees.

5. Marketing Spend Per Day ($)

   • The amount of money spent on marketing campaigns or promotions on any given day.
   • Range: $10 - $500 per day.

6. Location Foot Traffic (people/hour)

   • The number of people passing by the coffee shop per hour, a variable indicative of the shop's location and its potential to attract customers.
   • Range: 50 - 1000 people per hour.

Target Variable

• Daily Revenue ($)
  • This is the dependent variable representing the total revenue generated by the coffee shop each day.
  • It is calculated as a combination of customer visits, average spending, and other operational factors like marketing spend and staff availability.
  • Range: $200 - $10,000 per day.

Data Distribution & Insights

The dataset spans a wide variety of operational scenarios, from small neighborhood coffee shops with limited traffic to larger, high-traffic locations with extensive marketing budgets. This variety allows for exploring different predictive modeling strategies. Key insights that can be derived from the data include:

• The effect of marketing spend on daily revenue.
• The correlation between customer count and daily sales.
• The relationship between staffing levels and revenue generation.
• The influence of foot traffic and operating hours on customer behavior.

Use Cases & Applications

The dataset offers a wide range of applications, especially in predictive analytics, business optimization, and forecasting:

• Predictive Modeling: Use machine learning models such as regression, decision trees, or neural networks to predict daily revenue based on operational data.
• Business Strategy Development: Analyze how changes in marketing spend, staff numbers, or operating hours can optimize revenue and improve efficiency.
• Customer Insights: Identify patterns in customer behavior related to shop operations and external factors like foot traffic and marketing campaigns.
• Resource Allocation: Determine optimal staffing levels and marketing budgets based on predicted sales, improving overall profitability.

Real-World Applications in the Food & Beverage Industry

For coffee shop owners, managers, and analysts in the food and beverage industry, this dataset provides an essential tool for refining daily operations and boosting profitability. Insights gained from this data can help:

• Optimize Marketing Campaigns: Evaluate the effectiveness of daily or seasonal marketing campaigns on revenue.
• Staff Scheduling: Predict busy days and ensure that the right number of employees are scheduled to maximize efficiency.
• Revenue Forecasting: Provide accurate revenue projections that can assist with financial planning and decision-making.
• Operational Efficiency: Discover the most profitable operating hours and adjust business hours accordingly.

This dataset is also ideal for aspiring data scientists and machine learning practitioners looking to apply their skills to real-world business problems in the food and beverage sector.

Conclusion

The Coffee Shop Revenue Prediction Dataset is a versatile and comprehensive resource for understanding the dynamics of daily sales performance in coffee shops. With a focus on key operational factors, it is perfect for building predictive models, ...

Forecast: Coffee Market Size Volume Per Capita in Italy 2022 - 2026 Discover more data with ReportLinker!

Welcome to the "Worldwide Coffee Habits" dataset, a comprehensive resource that uncovers global coffee consumption trends. This dataset provides a detailed view of coffee consumption patterns across various countries, offering valuable insights into how coffee is enjoyed around the world.

Whether you're a data scientist, market analyst, researcher, or coffee enthusiast, this dataset enables in-depth analysis of coffee preferences, pricing trends, and economic influences on coffee consumption.

📌 Key Features: 🌍 Country-Specific Data: Country 🏳️: The name of the country where the data was collected. Year 📅: The recorded year (2000–2023), showing historical coffee trends. ☕ Coffee Consumption Insights: Coffee Consumption (kg per capita per year) 📊: The average amount of coffee consumed per person annually. Type of Coffee Consumed 🍵: The most popular coffee types in each country (e.g., Espresso, Latte, Cappuccino, Americano, Mocha). 💰 Economic Aspects of Coffee: Average Coffee Price (USD per kg) 💵: The average price per kilogram in US dollars, reflecting economic factors influencing coffee consumption. Population (millions) 👥: The estimated population of each country, providing context for per capita coffee consumption. 📈 Use Cases & Applications: 🔹 Analyzing global coffee consumption trends over time 🔹 Exploring regional coffee preferences and cultural influences 🔹 Understanding the impact of coffee prices on consumption 🔹 Comparing coffee habits across different population sizes 🔹 Identifying market opportunities for coffee businesses and suppliers

⚠️ Important Note: This dataset is synthetically generated for educational and analytical purposes. It does not contain real-world data but is designed to simulate realistic coffee consumption trends.

Content

This dataset contains detailed sales records for different coffee products across various locations and time periods. It includes product information such as coffee type, size, and price, along with customer purchase quantities and total revenue generated. Each entry represents an individual transaction, allowing analysis of sales trends, product performance, and customer purchasing behavior. The dataset is structured and clean, making it suitable for reporting, dashboards, machine learning models, and business insights.

context

The dataset is intended to assist in business decision-making within the retail coffee sector. Store owners, analysts, and other stakeholders can use it to learn which products sell best, how pricing impacts demand, and how sales fluctuate by location and season. Businesses can use this data to estimate future sales, optimize inventory, modify marketing tactics, and spot growth prospects. Both learning data analysis and practical commercial applications can benefit from the dataset.

Forecast: Coffee Market Size Volume Per Capita in Philippines 2022 - 2026 Discover more data with ReportLinker!

This dataset outlines the various occasions on which consumers purchase coffee out-of-home in the UK. It categorises consumption by purpose or context—such as on-the-go, breakfast, social, work-related, and treat—providing valuable insight into consumer behaviour and usage trends within the coffee shop market.

In a bustling and competitive coffee market, understanding sales performance and profit margins is crucial for any coffee chain to stay ahead. Our dataset captures a snapshot of transactions from a fictional coffee chain operating across various markets in the United States. With a diverse product line ranging from classic blends to herbal teas, the chain targets different segments of the market, each with its unique characteristics and preferences.

As the chain expands its operations, managers and analysts seek insights into sales trends, profitability, and market dynamics to optimize their strategies and drive growth. The dataset includes detailed records of sales, costs, and profit margins, allowing stakeholders to track performance at both the product and market levels. Additionally, information on marketing efforts provides context for understanding how promotional activities influence sales and profitability.

By analyzing this dataset, the coffee chain can identify high-performing products, assess the effectiveness of marketing campaigns, and uncover opportunities for improvement. Insights gleaned from the data empower decision-makers to allocate resources strategically, refine product offerings, and tailor marketing initiatives to specific market segments.

Ultimately, leveraging data-driven insights enables the coffee chain to enhance customer satisfaction, increase profitability, and solidify its position in the competitive coffee industry landscape.

Area Code: The area code associated with the data entry. COGS: Cost of goods sold. Difference Between Actual and Target Profit: The variance between the actual profit and the target profit. Date: The date of the data entry (in a specific format). Inventory: The amount of inventory. Margin: The profit margin. Market Size: The size of the market (e.g., major market). Market: The market segment. Marketing: Marketing-related data. 记录数: Number of records or entries. Product Line: The line of products. Product Type: The type of product. Product: The specific product. Profit: The profit generated. Sales: The amount of sales. State: The state associated with the data entry. Target COGS: The target cost of goods sold. Target Margin: The target profit margin. Target Profit: The targeted profit. Target Sales: The targeted sales amount. Total Expenses: The total expenses incurred.

Brazil Agricultural Production: Coffee Beans data was reported at 3,405.267 Ton th in 2023. This records an increase from the previous number of 3,179.176 Ton th for 2022. Brazil Agricultural Production: Coffee Beans data is updated yearly, averaging 2,767.659 Ton th from Dec 1974 (Median) to 2023, with 50 observations. The data reached an all-time high of 4,405.416 Ton th in 1987 and a record low of 751.969 Ton th in 1976. Brazil Agricultural Production: Coffee Beans data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIB001: Agricultural Production.

This dataset captures daily sales transactions from a coffee shop in Cape Town over March 2024. It includes transaction timestamps, payment types (card/cash), coffee product names, and revenue per transaction.

The dataset is designed to help explore customer habits and business performance — perfect for time series analysis, data visualization, or beginner-friendly data analytics projects.

🧰 Columns Description Column: What it means date Transaction date (YYYY/MM/DD) datetime: Exact timestamp of the transaction cash_type: Payment method (card or cash) card: Anonymized customer ID (card-based loyalty) money: Amount spent per transaction (in South African Rand) coffee_name: Type of coffee purchased

📊 Possible analyses Trend of transactions and sales by day of the week (Monday–Sunday)

Revenue distribution by coffee type

Payment method preference (card vs. cash)

Average daily transactions and average daily sales

Peak times of the day: morning, afternoon, evening

🎨 Visualization preview The attached Power BI dashboard shows:

Total sales, total transactions, and average transaction value

Revenue by coffee type

Double-axis trend line showing daily sales and transactions

Sales split by payment type

🌍 Why this dataset? Coffee data is relatable, seasonal, and perfect to practice:

Time-based grouping (weekdays, months, times of day)

KPI design and visualization

Building dashboards with clear business insights

This dataset compares the price of coffee across leading sandwich and bakery chains in the UK. It highlights pricing differences between operators, providing insight into competitive positioning and pricing strategy within the out-of-home coffee market.

This dataset contains detailed sales transactions from a coffee shop, providing insights into customer purchasing behavior, revenue trends, and product popularity. It is ideal for sales forecasting, demand analysis, and business intelligence applications.

Forecast: Instant Coffee Market Size Value in Denmark 2022 - 2026 Discover more data with ReportLinker!

About dataset:

The Coffee Sales Insights Dataset provides comprehesive sales data aimed at uncovering key business insights and trends within the coffee market. It captures useful details about product performance, customer behaviour, and store-level operations, helping busineses make data-driven decisions. By analyzing this dataset, you can explore how different coffee products perform across time, identify peak sales periods, understand customer preferences, and evaluate the impact of pricing and promotions on revenue. It’s ideal for sales analytics, market strategy, and forecasting future demand.

Better to analyze:

1. Top-performing coffee products and categories
2. Seasonal and regional sales patterns
3. Customer buying behavior and preferences
4. Insights on revenue growth and pricing strategies
5. Forecasts for upcoming sales trends

columns: It includes column like hours of day , cash_ type , money , coffee name , time of day , weekday , month name , weekday sort , month sort , date etc

The Coffee Sales Data dataset provides valuable insights into the performance of a coffee chain across various locations.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13090611%2F0f25997ba9b63b33c901471cb874aa83%2Fcisco_blog_canada_coffee.png?generation=1696189849175111&alt=media" alt=""> Key Attributes:

1.Area Code: A unique identifier for different geographical areas or regions where the coffee chain operates.

2.COGS (Cost of Goods Sold): The total cost incurred by the coffee chain in producing or purchasing the products it sells.

3.Difference between Actual and Target Profit: This attribute indicates how well the company performed in terms of profit compared to its target. It reflects the financial performance against predefined goals.

4.Date: The date of sales transactions, which allows for time-based analysis of sales trends and patterns.

5.Inventory Margin: The difference between the cost of maintaining inventory and the revenue generated from selling those inventory items.

6.Margin: The profit margin, which is the percentage of profit earned from sales. It's a critical financial metric.

7.Market Size: Information about the size of the market in each area, helping to understand the potential customer base and market dynamics.

8.Profit: financial gain achieved by the company after deducting the cost of goods sold (COGS) and other expenses from the revenue generated through sales.

9.Sales: represent the revenue generated from the coffee chain's products, reflecting its financial performance and customer demand.

Forecast: Coffee Market Size Volume in Thailand 2022 - 2026 Discover more data with ReportLinker!

Data from online surveys with coffee consumers in Brazil, the Democratic Republic of Congo, and Switzerland, and with coffee roasters in Switzerland. The goal of the surveys was to understand what characteristics of coffee are important to consumers and roasters, how they consume coffee, and what sustainability means to them.The data was collected in 2022 and 2023. The online survey with Brazilian consumers was conducted at the Festa do Café in Poço Fundo from 20 to 22 October 2023 and at the Semana International do Café in Belo Horizonte on 10 November 2023. The online survey with Congolese consumers was shared in WhatsApp Groups of coffee specialists and enthusiasts from April to June 2023. The online survey with Swiss consumers was conducted at the Swiss Coffee Festival in Zurich from 7 to 9 September 2022. The online survey with Swiss roasters was sent by e-mail to all Swiss roasters (171) from April to May 2023.Informed consent was obtained from all individual participants and the study was approved by the ethics commission of the home institution.

Description: This dataset provides daily price records for three key agricultural commodities: coffee, wheat, and corn, spanning five decades from 1973 to 2023. The dataset is a valuable resource for researchers, analysts, and enthusiasts interested in understanding the historical price trends of these essential commodities in the global market.

Columns: - Date: The date of the price record in yyyy-mm-dd format. - Coffee (USD): Daily prices of coffee in US dollars. - Wheat (USD): Daily prices of wheat in US dollars. - Corn (USD): Daily prices of corn in US dollars.

Data Source: The dataset is compiled from reliable sources and represents a comprehensive record of daily commodity prices, making it an ideal tool for studying the dynamics of these agricultural markets over the past fifty years.

Use Cases: - Analyze long-term price trends and patterns for coffee, wheat, and corn. - Create predictive models for commodity price forecasting. - Investigate the impact of various economic and environmental factors on commodity prices. - Explore correlations between commodity prices and global events.

Acknowledgments: We would like to express our gratitude to the data sources that have contributed to the compilation of this dataset, making it freely available for research and analysis.

Note: Please cite this dataset appropriately if you use it in your research or analysis.

Start exploring the world of agricultural commodity prices by downloading this dataset today!

Forecast: Coffee Market Size Volume in the US 2022 - 2026 Discover more data with ReportLinker!

Here are a few use cases for this project:

1. Quality Control in Coffee Production: This model could be used in coffee factories to automatically sort and separate the coffee beans into different categories (sour, broken, fade, black). It could help enhance the speed and efficiency of quality control processes.

2. Agricultural Research and Coffee Grade Classification: Researchers could use the model to study and classify different types of coffee beans. The model would help in identifying and studying the properties of different coffee beans leading to more effective and efficient agriculture processes.

3. Educational Tool for Coffee Lovers and Baristas: The model could be used as an educational tool to teach about the different types of coffee beans, their characteristics, and how these affect flavor.

4. Coffee Shop Use: Coffee shops could use the model to ensure the quality of the beans they purchase from suppliers. By identifying any inferior beans (such as broken or black ones), they can ensure the high standard of their coffee drinks.

5. Online Coffee Marketplaces: E-commerce platforms that sell coffee beans could integrate this model to their user interface to allow their suppliers to easily categorize their beans and thus increase product transparency for customers.