As of January 2025, the most profitable industry in the United States was the tobacco industry, with a net profit margin of ***** percent. The profit margin of the entertainment software industry was not too far behind, with a net profit margin of *****.

Between 2019 and 2023, oil and gas explorers and producers logged the highest total revenue worldwide, reaching *** trillion U.S. dollars. Life and health insurance carriers followed behind.

In 2024, the**************************************o posted the highest revenue of any company in the world before taxes, with an income of over *** billion U.S. dollars. ************************************************** rounded out the top five spots in the ranking of most profitable companies. What is net income? Net income, or net profit, which differs slightly from pre-tax income, is the figure that gives the most complete overview of a company’s profitability: It is calculated as the revenue of a company less all operating expenses, debt payments, interest paid, income from subsidiary holdings, taxes, etc. Different industries have different net profit margins. The Apple doesn’t fall far In terms of market value, Microsoft was the largest company in the world in 2024, with Apple following in second. Since the beginning of the new millennium, Apple has reported ever rising amounts of worldwide revenue, with iPhone sales leading the charge.

About Dataset

Context

This dataset contains financial information of 1500 companies across 8 different industries scraped from companiesmarketcap.com on May 2024. It contains information about the company's name, industry, country, employees, marketcap, revenue, earnings, etc.

• The scraped program of this dataset can be found in Industry Market Cap Analysis Github repository.
• Tableau visualization of this dataset can also be found in Industry Market Cap: Visualization

Content

The dataset contains 2 files with the same column names. scraped_company_data.csv file is further transformed and cleaned to produce the finaltransformed_company_data.csvfile.

1. Company: Full name of the company
2. Company Path: Website URL of the company
3. Industry: Associated industry of the company
4. Country: Location of the company
5. Employees: Total number of employees of the company
6. Market Cap: Market capital of the company (as of Mar 2024)
7. Revenue: Company's current revenue (31 Mar 2023 - 31 Mar 2024)
8. Earnings: Company's current earnings (31 Mar 2023 - 31 Mar 2024)
9. Operating Margin: Company's current operating margin (at the end of 2023)
10. Total Assets: Company's total assets (as of Mar 2024)
11. Total Liabilities: Company's total liabilities
12. Total Debt: Company's total debt
13. Net Assets: Company’s net assets
14. PE Ratio: Company's current price-to-earnings ratio (31 Mar 2023 - 31 Mar 2024)
15. PS Ratio: Company's current price-to-sales ratio (31 Mar 2023 - 31 Mar 2024)

Acknowledgements

The website companiesmarketcap.com was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

The dataset can be used to find industries with the highest average market value, most profitable industries, most growth-oriented sectors, etc. More interesting insights can be found in this README file.

The statistic shows the most profitable sectors in Vietnam in 2018. In that year, the banking sector ranked first with an after tax profit of ***** trillion Vietnamese dong (VND), followed by the real estate sector with a profit of ***** trillion VND and the food and beverages sector with ***** trillion VND. The oil sector ranked last with an after tax profit of **** trillion VND.

The Danish company Jobindex A/S was the most profitable company in the information service industry in the Nordic countries as of February 2022, with a net profit of roughly 24.6 million euros. The second highest ranked company was the Swedish company Zimpler AB, generating a net revenue of almost 20 million euros.

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Apple's Historical Financials

Tracking a Decade of Performance

By [source]

About this dataset

This dataset contains the financial performance information for Apple Inc. over the past decade, offering researchers with a comprehensive look at the tech giant’s income and expenses. With data covering key components such as total revenue, operating expenses, gross profit and net income for each year from 2012 to 2021, this invaluable dataset provides an in-depth insight into Apple’s financial standing over the years. Beyond simply examining numerical values, it also offers opportunities to explore trends in financial management strategies and inform potential forecasts of future profit or decline. With detailed insights into a historically successful brand that has become one of today’s most innovative global corporations, this dataset will give users access to actionable knowledge which could be used to make informed business decisions or gain further understanding of corporate functions within Apple Inc

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For more datasets, click here.

Featured Notebooks

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How to use the dataset

This dataset contains detailed financial data from Apple Inc. spanning the past decade, and is a valuable resource for those looking to perform in-depth analysis on the company’s financial performance. To get started using this dataset, here’s what you need to know:

• This dataset provides financial information over a 10 year period including total revenue, operating expenses, gross profit, net income and more.
• All values are provided in numeric form so you can easily analyze trends over time or make comparisons between different metrics.
• The columns show each individual metric as reported for each fiscal year (e.g., Total Revenue).
• Use this data to dive into historical trends and gain insights about Apple's overall financial performance over the years!

Research Ideas

• Analyzing the financial growth of Apple over the past decade and comparing it to similar companies in order to get a better understanding of how Apple has been able to remain one of the most profitable companies in the world.
• Predicting future revenue, expense, and profit trends for Apple based on variations seen in their historical data.
• Implementing an automated financial modeling system that uses data from this dataset to develop quantitative models that make use of industry-wide averages and more detailed market insights than traditional statistical approaches could offer

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: apple_income_statement.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

In 2023, the profits of the wholesale trade industry amounted to around 290.5 billion U.S. dollars. Corporate profits are defined as the net income of corporations in the National Income and Product Accounts (NIPA). Total corporate profits amounted to 3.37 trillion U.S. dollars in Q1 of 2024.

Scientific research and development (R&D) facilities have enjoyed significant growth over the past five years as the mix of accelerating medical innovation, new global conflicts and push to advance medical treatments provided a diversified demand niche for the industry. Skyrocketing corporate profit, which boosted 6.3% over the past five years, enabled private companies to massively increase their budgets for R&D. New conflicts in the Middle East and Europe generated a wider range of defense capability needs, causing public sector clients to contract R&D companies at a more rapid pace to advance research on weapons systems and military equipment. A robust push toward sustainability across clients’ product stream further advanced new technological research in facets such as biomedical treatments. In light of these trends and an acceleration of technological adoption, revenue spiked at a CAGR of 4.9% to an estimated $320.9 billion over the past five years, including an anticipated 3.1% boost in 2025 alone. The federal government is the largest and most consistent source of revenue, so changes in federal funding levels greatly affect servicers’ performance. Many R&D sites focus on military tech, so the Trump administration's support for defense spending brought on a surge revenue. While the Biden administration originally pushed for lower defense spending, serious conflicts involving the US's allies, namely Ukraine and Israel, have brought military innovation back to the forefront of budget discussions. Although revenue growth was strong, a rebound in wage expenditures following an inflationary spike has caused a slight slowdown in profit growth. Moving forward, scientific R&D companies will continue benefiting from anticipated growth in corporate profit and sector-wide support for new research projects. While still high at 4.3% as of February 2025, the eventual stabilization in interest rates will encourage new investment. The passing of the Inflation Reduction Act in 2022 will benefit research labs studying alternative fuels and clean energy through tax credits that encourage private investment. New technological advances, such as UAVs and EWs, will provide greater need for technically adept R&D companies that can help strengthen military equipment research and development for the future. Additionally, anticipated growth in overall research & development expenditure across the public and private sectors will provide more funding for R&D initiatives, creating a larger field of opportunity for new researchers. Overall, revenue is expected to boost at a CAGR of 3.2% to an estimated $375.7 billion over the next five years.

Movie Gross and Ratings

A Study of the Impact of Movies on Profitability and Popularity

By Yashwanth Sharaff [source]

About this dataset

This dataset of top20 movies offers insights on how the movie industry has evolved over two decades. With data on titles, MPAA ratings, budgets, grosses, release dates and genres this comprehensive dataset allows you to explore the film industry's most popular films and trace patterns in movie profits and ratings across time. Analyze how genre types have resonated with audiences, or take a closer look at the characteristics of movies that were highly rated by viewers. With more than three hundred movies featured in this dataset Movie Profits and Ratings acts as both an exploration into the history of film for novices looking for an introduction to popular films as well as a powerful tool for experienced data scientists interested in trend analysis of film industry data

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Featured Notebooks

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How to use the dataset

This dataset is a great tool for analyzing the gross and ratings of movies released. With this data, we can learn more about the success of a movie. By exploring this dataset, we can answer questions such as which movies have been most profitable or what types of movies had the highest ratings.

Research Ideas

• Creating a tool that easily creates movie trailers without any manual editing and use a prediction algorithm to suggest the best trailer based on previously existing ones of similar genres and rating.
• Analyzing the data to detect trends in ratings and gross/budget over time, allowing businesses to adjust strategies accordingly.
• Developing an application that allows users to easily search for movies by genre, rating, runtime, budget and recommend movies based on their past choices or those with similar ratings from other users

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Movies_gross_rating.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------------| | Title | The title of the movie. (String) | | MPAA Rating | The Motion Picture Association of America (MPAA) rating of the movie. (String) | | Budget | The budget of the movie in US dollars. (Integer) | | Gross | The gross of the movie in US dollars. (Integer) | | Release Date | The date the movie was released. (Date) | | Genre | The genre of the movie. (String) | | Runtime | The length of the movie in minutes. (Integer) | | Rating Count | The number of ratings the movie has received. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Yashwanth Sharaff.

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, ...

As of January 2025, the advertising industry had a net profit margin of ******percent in the United States. The green and renewable energy industry had a net profit margin of ****** percent.

Startups Valued at $1 Billion or More

A Comprehensive Dataset of Successful Startups around the World

About this dataset

This dataset contains a list of startup companies around the world that have been valued at $1 billion or more. This dataset can be used to research the most innovative and successful startups in the world, and to compare and contrast companies in different industries, locations, etc

How to use the dataset

This dataset can be used to research the most innovative and successful startups in the world, and to compare and contrast companies in different industries, locations, etc.

Some possible questions that could be answered using this data include: - Which startups have been valued at $1 billion or more? - Where are these startups located? - What industries do these startups belong to? - Who are the investors in these startups? - What is the last valuation of each startup? - When did each startup join Crunchbase?

Research Ideas

• Identifying the most innovative and successful startups in the world
• Analyzing and comparing startups in different industries, locations, etc.
• Predicting which startups are most likely to achieve unicorn status

Acknowledgements

This dataset was scraped from Crunchbase, a website that provides information on startups and tech companies.

Columns

File: unicorns.csv | Column name | Description | |:-------------------------------|:------------------------------------------------------| | Updated at | The date when the dataset was last updated. (Date) | | Company | The name of the startup company. (Text) | | Crunchbase Url | The URL of the company's Crunchbase profile. (URL) | | Last Valuation (Billion $) | The company's last valuation, in US dollars. (Number) | | Date Joined | The date when the company joined Crunchbase. (Date) | | Year Joined | The year when the company joined Crunchbase. (Number) | | City | The city where the company is located. (Text) | | Country | The country where the company is located. (Text) | | Industry | The industry in which the company operates. (Text) | | Investors | The investors in the company. (Text) | | Company Website | The URL of the company's website. (URL) |

Survey of innovation and business strategy, enterprises with more than one profit centre, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2009 to today.

Colombia EES: Industrial: Effect of Salary Increases to Profitability: Next 12 Mos: More Than the Last 12 Mos data was reported at 17.089 % in Apr 2019. This records a decrease from the previous number of 21.622 % for Mar 2019. Colombia EES: Industrial: Effect of Salary Increases to Profitability: Next 12 Mos: More Than the Last 12 Mos data is updated monthly, averaging 16.770 % from Oct 2005 (Median) to Apr 2019, with 163 observations. The data reached an all-time high of 36.486 % in Dec 2015 and a record low of 8.861 % in Jun 2011. Colombia EES: Industrial: Effect of Salary Increases to Profitability: Next 12 Mos: More Than the Last 12 Mos data remains active status in CEIC and is reported by Bank of the Republic of Colombia. The data is categorized under Global Database’s Colombia – Table CO.S005: Economic Expectation Survey: Industrial Sector.

The Information sector creates and distributes media content to US consumers and businesses. The Information sector responds to trends in household formation, which influences subscription volumes to communications services and advertising expenditure, generating nearly one-fourth of sector revenue. Also, consumer incomes and spending habits influence the extent to which households purchase discretionary entertainment products. The Information sector also sells some products and services directly to businesses and is influenced to a lesser extent by trends in corporate profit and business sentiment. The accelerated pace of digital transformation has fueled industry growth. As remote work and online learning became the norm, demand for robust digital infrastructure and cloud services skyrocketed. This shift wasn't limited to cloud services alone; internet providers flourished, spurred by the advent of 5G technology. Through the end of 2025, sector revenue will expand at a CAGR of 2.4% to reach $2.5 trillion, including a boost of 2.0% in 2025 alone. Although consumer demand for media is generally steady and the Information sector has expanded consistently, revenue flows within the sector are uneven and determined by technology trends. Substantial expansion through the end of 2025 has stemmed from a proliferation of new consumer devices. However, most of the expansion has been concentrated on online publishing and data processing at the expense of more traditional information subsectors. For example, new digital channels have detracted from print advertising expenditures, which have declined during the current period and contributed to the curtailment of print publishing. The expansion of mobile devices and the emergence of online streaming services have made consumers less reliant on traditional communication services, such as wired voice, broadband internet and cable TV. Looking ahead, the information sector is poised for sustained growth over the next five years, fueled by rising consumer spending and private investment. As the economy recovers and interest rates stabilize, disposable incomes are poised to climb, allowing households to avail themselves of more digital subscriptions and services. The rollout of 5G will further augment mobile internet usage, potentially challenging wired broadband alternatives. Traditional media companies will continue to shift their focus to online platforms and streaming services, aiming to retain and expand their audience. Through the end of 2030, the Information sector revenue will strengthen at a CAGR of 2.4% to reach $2.8 trillion.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_1726d53683073b9787882051a650e9b8/view

Dynamic Ticket Pricing Solutions Market Outlook

According to our latest research, the global dynamic ticket pricing solutions market size reached USD 1.42 billion in 2024, driven by surging adoption across live events, sports, and transportation sectors. The market is expected to grow at a robust CAGR of 15.8% during the forecast period, reaching an estimated USD 5.31 billion by 2033. This remarkable growth is primarily attributed to the increasing need for maximizing revenue and optimizing seat occupancy through advanced pricing algorithms and real-time data analytics.

One of the primary growth factors fueling the dynamic ticket pricing solutions market is the increasing digital transformation across the events and transportation industries. Organizations are leveraging sophisticated pricing engines powered by artificial intelligence and machine learning to analyze real-time factors such as demand surges, competitor pricing, and historical sales data. This enables event organizers and ticketing agencies to adjust ticket prices dynamically, ensuring optimal revenue generation and higher fill rates. The proliferation of mobile ticketing platforms and the integration of dynamic pricing modules within these systems further accelerate market adoption, as end-users seek seamless, data-driven solutions to respond to rapidly changing market conditions.

Another significant driver is the rising consumer acceptance of variable pricing models, especially among younger, tech-savvy audiences who expect transparency and value for money. Dynamic ticket pricing solutions allow organizations to offer competitive pricing during off-peak periods and capitalize on premium pricing during high-demand events. This approach not only maximizes profits but also democratizes access, enabling a broader segment of the population to attend events at affordable rates. The growing popularity of live entertainment, sports events, and experiential travel experiences is pushing venues and organizers to adopt dynamic pricing strategies to stay competitive and relevant in a crowded marketplace.

Market growth is also propelled by the increasing complexity and scale of events, which demand sophisticated, automated pricing tools. Large-scale concerts, sports tournaments, and theme parks require real-time, granular control over pricing to manage thousands of seats or entry tickets efficiently. Dynamic ticket pricing solutions offer centralized dashboards, predictive analytics, and scenario planning features that empower decision-makers to simulate various pricing strategies and select the most profitable options. As the industry continues to globalize and face fluctuating consumer behaviors, the need for agile, scalable pricing solutions is expected to intensify, further driving market expansion.

Dynamic Pricing Software plays a pivotal role in the evolution of dynamic ticket pricing solutions. As organizations strive to optimize revenue and enhance customer experience, the integration of advanced software platforms becomes indispensable. These platforms are equipped with sophisticated algorithms that analyze vast datasets in real-time, enabling precise pricing adjustments that reflect current market conditions. By leveraging dynamic pricing software, businesses can seamlessly adapt to fluctuations in demand, ensuring that they remain competitive and responsive to consumer needs. This adaptability not only maximizes profitability but also fosters a more engaging and personalized customer journey.

From a regional perspective, North America currently dominates the dynamic ticket pricing solutions market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, is at the forefront due to its mature live events and sports industries, as well as widespread adoption of advanced digital technologies. However, emerging markets in Asia Pacific and Latin America are witnessing rapid uptake, fueled by increasing urbanization, rising disposable incomes, and growing investments in entertainment infrastructure. As global travel and tourism rebound post-pandemic, regions such as the Middle East & Africa are also expected to experience accelerated adoption of dynamic pricing solutions.

Introduction

Food Truck Statistics: Food trucks are one of the most profitable businesses in the food industry. However, as these Food Truck Statistics indicate, they may shut down after three years of operations if no proper strategies are followed. These on-the-go businesses can be set up in any part of the world. Anything can be cooked in these food trucks to meet the demands of the consumers.

However, recent Food truck numbers show that the demand for vegan and vegetarian food is rising; therefore, offering substitutes is always an improved strategy to continue the business.