As of September 2024, there were ***** café and coffee shop businesses in the United Kingdom. Meanwhile, the market size of the industry stood at *** billion British pounds in the same period.

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, ...

The Coffee Market Report is Segmented by Product Type (Whole-Bean, Ground Coffee, and More), Distribution Channel (On-Trade and Off-Trade), Coffee Species (Arabica, Robusta and More), Origin (Single Origin/Specialty and Mixed), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD) and Volume (Tons).

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 ranks the top 10 UK coffee shop, café, and dessert parlour brands based on forecasted outlet numbers as of December 2025. It provides a snapshot of market presence by brand and highlights the leading players in the out-of-home coffee and dessert sectors.

The market size of the coffee and snack shop sector in the United States totaled **** billion U.S. dollars as of April 2025. Meanwhile, the number of businesses reached nearly ****** and employment reached over *******.

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.

This dataset showcases the top 10 branded coffee and sandwich shop chains in the UK ranked by forecasted net outlet growth between December 2024 and December 2025. It includes both absolute and percentage growth figures, providing insight into the fastest-growing brands within the sector.

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

Number of Businesses statistics on the Coffee & Snack Shops industry in the US

The Australia Coffee Market Report is Segmented by Product Type (Whole Bean, Ground Coffee, Instant Coffee, Coffee Pods and Capsules, and More), Flavor (Plain, Flavored), Category Type (Conventional Coffee, Specialty Coffee), Bean Type (Arabica, Robusta, Others), Distribution Channel (On-Trade, Off-Trade), and Geography (NSW, Victoria, Queensland, Rest of Australia). Market Forecasts are Provided in Terms of Value (USD).

This dataset is ideal for exploring the evolving sales trends over time, identifying peak customer traffic days, and delving into the performance metrics of various products. The dataset comprises transactional records from Maven Roasters, a fictional NYC-based coffee shop operating across three distinct locations. It encompasses comprehensive details such as transaction dates, timestamps, geographical specifics, and product-level information. Researchers can analyze the frequency of product sales, pinpoint top revenue drivers, and investigate factors contributing to fluctuations in sales volume.

Reference :

Maven Analytics. (n.d.). Maven Analytics | Data analytics online training for Excel, Power BI, SQL, Tableau, Python and more. [online] Available at: https://mavenanalytics.io [Accessed 6 Dec. 2023].

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.

Comprehensive dataset containing 145,629 verified Coffee shop businesses in United States with complete contact information, ratings, reviews, and location data.

****Attribute information:****

Transaction ID: Numerical (Unique identifier for each transaction) Transaction Date: Date (Date of the transaction) Transaction Time: Time (Time of the transaction) Store Number: Numerical (Identifier for the store location) Store Location: Text (Location of the store) Unit Number: Numerical (Unit number within the store) Product Category: Text (Category of the product) Product Type: Text (Type of product within the category) Product Name: Text (Specific name of the product) Price: Numerical (Price of the product) Month: Numerical (Month of the transaction) Day: Numerical (Day of the month) Weekday: Text (Day of the week) Hour: Numerical (Hour of the day)

By understanding these attributes and their characteristics, you can effectively explore the dataset and derive meaningful insights.

The US Coffee Market Report is Segmented by Product Type (Whole Bean, Ground Coffee, Instant Coffee, and Coffee Pods and Capsules), Type (Conventional and Specialty), Packaging Type (Flexible, Rigid, and Single-Serve), Distribution Channel (On-Trade and Off-Trade Channel) and Geography (California, Texas, Florida, and More). The Market Forecasts are Provided in Terms of Value (USD).

In 2025, ** percent of survey respondents in the United States stated that they drank coffee within the last day. About ** percent of U.S. respondents had drunk espresso-based beverages instead. Coffee brands in the U.S. In 2020, Folgers produced over ************* U.S. dollars’ worth of sales in the United States, making it the leading brand of regular ground coffee by a significant margin. Total sales numbers generated by private coffee labels amounted to some *** million U.S. dollars. Folgers Coffee was first introduced in 1850, and by 2020, had the largest ground coffee market share in the United States. The coffee giant was followed by other well-known brands, such as Maxwell House, Starbucks, and Dunkin’ Donuts. Arabica vs. Robusta In the commercial coffee industry, there are two main types of coffee species: Arabica and Robusta. Coffee beans of the Arabica variety are slightly larger, produce a smooth and aromatic taste, and are the most commonly produced coffee bean variety: in 2023/24, just over ** million bags (60 kilograms each) of Arabica coffee were produced worldwide. Robusta beans are generally smaller and rounder, cheaper to cultivate, and taste quite bitter. Just over ** million bags of this coffee type were produced during the same marketing year.

The global coffee market size was USD 97.71 billion in 2024 & is projected to grow from USD 102.98 billion in 2025 to USD 156.85 billion by 2033.
Report Scope:

This statistic shows the domestic coffee market demand in China in 2015 with the forecasts up until 2025. According to preliminary data, the market size of coffee consumption in China was around ** billion yuan in 2015 and it is forecasted to grow to approximately ************ yuan by 2025.

Global coffee shops market size valued USD 85.75 Billion in 2024 and Is Expected To Reach USD 145.96 Billion by the end of 2034, CAGR of 4.75%