In 2024, the branded coffee shop market in the United Kingdom was estimated to have a value of *** billion British pounds. This was an increase of over ** percent from the previous year, signifying a continued recovery from the dramatic decline in market value that occurred in 2020, when the coronavirus (COVID-19) pandemic heavily impacted the UK's coffee shop industry. However, the 2024 figure was around ** percent lower than before the pandemic, when it stood at **** billion British pounds in 2018.

Starbucks is one of the largest brands in the world. In 2025, the global coffee shop chain was valued at approximately 38.76 billion U.S. dollars. This shows a significant decrease compared to the company's previous value of 60.67 billion U.S. dollars in 2024.

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%

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.

Market Size statistics on the Coffee & Snack Shops industry in the US

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Specialty Coffee Shops Market Size 2025-2029

The specialty coffee shops market size is forecast to increase by USD 50.8 billion at a CAGR of 7.1% between 2024 and 2029.

The market is experiencing significant growth due to the increasing consumption of coffee and coffee pods, with a focus on premium and artisanal offerings. This trend is driven by consumers' growing appreciation for the unique flavors and quality of specialty coffee. Additionally, the emphasis on sustainability in the coffee industry is another key driver, as consumers are increasingly conscious of the environmental and ethical implications of their coffee choices. However, the market faces challenges as well. Fluctuating prices of raw coffee beans pose a significant threat to profitability, as specialty coffee shops often source high-quality beans at a premium.
These price fluctuations can make it challenging for businesses to maintain consistent pricing and profit margins. To navigate these challenges, specialty coffee shops must focus on building strong relationships with their suppliers and implementing pricing strategies that allow them to absorb price increases while still offering competitive prices to customers. By staying agile and responsive to market trends, specialty coffee shops can capitalize on the growing demand for premium coffee and differentiate themselves from mass-market competitors.

What will be the Size of the Specialty Coffee Shops Market during the forecast period?

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The specialty coffee shop market continues to evolve, with dynamic shifts in consumer preferences, technology, and business models. The coffee shop community is at the forefront of this transformation, driving innovation in various sectors. Third wave coffee, characterized by its focus on high-quality beans, artisanal roasting, and precise brewing methods, is a prime example of this evolution. Espresso machines and coffee shop technology have advanced significantly, enabling shops to offer a wider range of beverages and enhance the customer experience. Direct trade coffee and coffee blends have gained popularity, allowing shops to source beans directly from farmers and create unique flavor profiles.
Coffee shop events, such as workshops and education sessions, have become essential tools for engaging customers and building brand loyalty. Specialty coffee shops are also embracing technology, with apps and social media marketing enabling seamless ordering and delivery. Sustainability and profitability are key concerns for coffee shop owners. Coffee packaging and roasting techniques have evolved to reduce waste and increase efficiency. Coffee shop pricing strategies are also undergoing changes, with subscription services and alternative brewing methods like pour over and cold brew offering new revenue streams. The coffee shop landscape is characterized by a diverse range of business models, from independent shops to franchise chains.

How is this Specialty Coffee Shops Industry segmented?

The specialty coffee shops industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Independent coffee shops
  Chain coffee shops


Distribution Channel

  Offline
  Online


Age Group

  18-24 Years
  25-39 Years
  40-59 Years
  Above 60


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The independent coffee shops segment is estimated to witness significant growth during the forecast period.

The independent coffee shop market experiences continuous growth due to the increasing preference for specialty coffee and unique shop experiences. This sector encompasses various offerings, including Fair Trade Coffee and Coffee Certifications, which prioritize ethical sourcing and sustainable practices. Coffee Shop Innovation is evident in the use of Coffee Equipment, such as Pour Over devices and Espresso Machines, and Coffee Shop Technology, like Coffee Shop Apps and Social Media Marketing. Single-Origin Coffee and Brewing Methods, like Cold Brew, showcase the diversity of Specialty Coffee Beans. Coffee Accessories, Coffee Shop Decor, and Coffee Education provide customers with immersive experiences.

Coffee Shop Workshops and Coffee Shop Events foster a strong Coffee Shop Community. Third Wave Coffee and Direct Trade Coffee emphasize the importance of transparency and quality. Coffee Blends, Coffee Shop Delivery, and Coffee Shop Pricing cater to the convenience of consumers. Coffee Shop Sustainability and Coffee Shop Profitability ar

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

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 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).

The global cafe chain market is a dynamic and rapidly expanding sector, projected to experience significant growth over the next decade. While precise figures for market size and CAGR are not provided, a reasonable estimate, considering the presence of major global players like Starbucks and McDonald's McCafé, alongside numerous regional chains, would place the 2025 market size in the tens of billions of dollars, with a compound annual growth rate (CAGR) between 4% and 7% throughout the forecast period (2025-2033). This growth is fueled by several key drivers, including rising disposable incomes in emerging markets, increasing consumer preference for premium coffee experiences, and the expanding popularity of specialty coffee drinks beyond traditional espresso-based offerings. The market also sees strong growth in innovative beverage options, convenient mobile ordering and loyalty programs. Furthermore, the trend towards healthier food options within cafe settings is gaining momentum. The increasing prevalence of third-wave coffee shops emphasizing artisanal sourcing and brewing techniques also contributes to market expansion. However, the market faces certain restraints. Fluctuations in coffee bean prices, intense competition among established players and emerging brands, and the rising costs of labor and real estate can affect profitability. Market segmentation reveals a diverse landscape encompassing premium chains, quick-service cafes, and independent coffee shops. Geographic variations in coffee consumption patterns and regulatory environments across different regions also impact market performance. The competitive landscape is characterized by a mix of global giants with extensive brand recognition and a large number of regional and local competitors vying for market share. Successful players are continuously adapting their offerings to cater to evolving consumer preferences, embracing technological advancements, and focusing on providing superior customer experiences.

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.

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).

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.

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 Cafe and Bars Market is Segmented by Service Type (Bars and Pubs, Cafés, Specialty Coffee and Tea Shops, and Juice and Smoothies Bar), Outlet Format (Chained Outlets and Independent Outlets), Location (Leisure, Lodging, Retail, Standalone, and Travel), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD).

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.

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].

The given dataset is a collection of synthetically generated transaction information from a coffee shop. It contains 1000 rows, with each row representing a single transaction made by a customer. The columns in the dataset indicate whether a specific product was purchased or not. The purpose of this dataset is to facilitate the analysis of purchasing patterns, development of marketing strategies, and optimization of product offerings in coffee shops(association such market basket) . Each transaction has a unique ID for easy identification. This dataset can be utilized for various analyses, including market basket analysis, which aims to discover frequent purchase patterns among different products.

If you liked this dataset, please upvote! We'd love to know that you enjoyed it.

If you have any questions, please feel free to comment! We're happy to answer any questions you may have.

Best wishes!☕☕

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.

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