Context

World champion barista James Hoffmann and Cometeer partnered to conduct a first-of-its-kind coffee taste test. Cometeer shipped 5000 coffee kits across America. Kits contained four different coffees - pre-extracted and flash frozen. Tasters melted and diluted the coffee capsules for a largely identical tasting experience. Tasting and ratings were conducted blind [1]. After survey responses were collected (provided data), some attributes of the coffee were revealed.

In October 2023, World champion barista James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Content

Geography: US

Time-period: 2023

Unit of Analysis: The Great American Coffee Taste Test

Variables

• submission_id = Submission ID
• age = What is your age?
• cups = How many cups of coffee do you typically drink per day?
• where_drink = Where do you typically drink coffee?
• brew = How do you brew coffee at home?
• brew_other = How else do you brew coffee at home?
• purchase = On the go, where do you typically purchase coffee?
• purchase_other = Where else do you purchase coffee?
• favorite = What is your favorite coffee drink?
• favorite_specify = Please specify what your favorite coffee drink is
• additions = Do you usually add anything to your coffee?
• additions_other = What else do you add to your coffee?
• dairy = What kind of dairy do you add?
• sweetener = What kind of sugar or sweetener do you add?
• style = Before today's tasting, which of the following best described what kind of coffee you like?
  -**strength** = How strong do you like your coffee?
• roast_level = What roast level of coffee do you prefer?
• caffeine = How much caffeine do you like in your coffee?
• expertise = Lastly, how would you rate your own coffee expertise?
• coffee_a_bitterness = Coffee A - Bitterness
• coffee_a_acidity = Coffee A - Acidity
• coffee_a_personal_preference = Coffee A - Personal Preference
• coffee_a_notes = Coffee A - Notes
• coffee_b_bitterness = Coffee B - Bitterness
• coffee_b_acidity = Coffee B - Acidity
• coffee_b_personal_preference = Coffee B - Personal Preference
• coffee_b_notes = Coffee B - Notes
• coffee_c_bitterness = Coffee C - Bitterness
• coffee_c_acidity = Coffee C - Acidity
• coffee_c_personal_preference = Coffee C - Personal Preference
• coffee_c_notes = Coffee C - Notes
• coffee_d_bitterness = Coffee D - Bitterness
• coffee_d_acidity = Coffee D - Acidity
• coffee_d_personal_preference = Coffee D - Personal Preference
• coffee_d_notes = Coffee D - Notes
• prefer_abc = Between Coffee A, Coffee B, and Coffee C which did you prefer?
• prefer_ad = Between Coffee A and Coffee D, which did you prefer?
• prefer_overall = Lastly, what was your favorite overall coffee?
• wfh = Do you work from home or in person?
• total_spend = In total, how much money do you typically spend on coffee in a month?
• why_drink = Why do you drink coffee?
• why_drink_other = Other reason for drinking coffee
• taste = Do you like the taste of coffee?
• know_source = Do you know where your coffee comes from?
• most_paid = What is the most you've ever paid for a cup of coffee?
• most_willing = What is the most you'd ever be willing to pay for a cup of coffee?
• value_cafe = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• spent_equipment = Approximately how much have you spent on coffee equipment in the past 5 years?
• value_equipment = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• gender = Gender
• gender_specify = Gender (please specify)
• education_level = Education Level
• ethnicity_race = Ethnicity/Race
• ethnicity_race_specify = Ethnicity/Race (please specify)
• employment_status = Employment Status
• number_children = Number of Children
• political_affiliation = Political Affiliation

Acknowledgement

Datasource: The data is collected thorugh a survey called The Great American Coffee Taste Test held by James Haffmann

Inspiration: [Great American Coffee...

Coffee is one of the most widely consumed beverages in the world. In 2023/24, approximately *** million 60 kilogram bags of coffee were consumed worldwide, a slight increase from about *** million bags in the previous year. Coffee Brewing Innovations The coffee industry is regularly coming up with innovative new methods for brewing coffee and serving it. Single cup brewers are a relatively new innovation which offer a quick and mess-free coffee brewing method for when one only wants a single cup of coffee without the hassle of brewing an entire pot. Cold Brew Coffee Cold brew coffee is made by leaving coffee to brew in cold or room temperature water for 12 to 24 hours and results in a mellower, less acidic tasting coffee. It has become a popular menu item at coffee shops and cafes, but it also easy to make at home. The market for cold brew coffee was valued at *** million U.S. dollars in 2017, and is expected to reach a staggering **** billion U.S. dollars in market value by 2023.

Coffee consumption in the United States amounted to over ** million 60-kilogram bags in the 2024/2025 fiscal year. This is a slight increase from the total U.S. coffee consumption in the previous fiscal year. Coffee production The coffee plant has its origins in Ethiopia and is now grown all over the world. Most of the world’s coffee is cultivated in South America, followed by Asia and Oceania. In 2024, over ***million 60-kilogram bags of coffee were produced in South America. The majority of South America’s coffee production is attributed to Brazil. In the 2023/2024 fiscal year, global coffee production reached *** million 60-kilogram bags. Coffee brewing in the United States Americans love their coffee and have dozens of different methods and gadgets for brewing and preparing coffee. A 2025 survey of U.S. consumers found that the most commonly used coffee preparation methods were drip coffee makers and single-cup brewers. However, drip coffee makers have become less popular over time. In 2012, ***percent of coffee drinkers used drip coffee makers, while in 2025 this share had dropped to ***percent.

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, ...

Last month, British YouTuber (and former World Barista Champion) James Hoffman virtually hosted the Great American Coffee Taste Test, during which thousands of people simultaneously blind-tasted the same four coffees. Hoffman has published a video summarizing the results, as well as a spreadsheet of anonymized survey responses from 4,000+ participants. It includes tasters’ demographics, general coffee drinking habits and preferences, assessments of the four coffees, and more CR: https://bit.ly/gacttCSV+