Data Source: - Article Title: The Global 2000 - Organisation: Forbes - Authors: Andrea Murphy and Hank Tucker - Original Data Updated on June 08, 2023 - Retrieved from https://www.forbes.com/lists/global2000/?sh=60bb65b85ac0

Web Scraping: - Using the robotstxt package in R studio

Data Overview: - The dataset comprises information on 2000 of the world's largest companies, along with several key determinants of their large-scale operations, including sales, profit, assets, and market values. - Data Cleaning: To ensure uniformity, numerical variables were standardized to million USD dollars.

Row: - 2000 Observations: Data related to the world's largest companies and their financial status across the globe.

Columns: - Rank (Ordinal): The company's ranking in the Forbes Global 2000 list. - Company (Categorical): The name of the company. - Country (Categorical): The country where the company is headquartered. - Sales (Numerical): Annual sales revenue of the company (in million USD). - Profit (Numerical): Annual profit of the company (in million USD). - Asset (Numerical): Total assets of the company (in million USD). - Market Value (Numerical): Market capitalization of the company (in million USD). - Publish Year (Ordinal): 2023 (for potential dataset integration).

Useful Tips - Dynamic Trend Analysis: Consider exploring the dynamic trends of the global market by combining this dataset with historical data spanning various decades, such as this set of available datasets(https://www.kaggle.com/datasets/joebeachcapital/forbes-global-2000). This can offer valuable insights into how the world's largest companies have evolved over time.

Introduction:

This analysis delves into the financial performance of top companies by examining key metrics such as revenue, earnings, market capitalisation, P/E ratio, and dividend yield. By comparing these metrics, we gain a comprehensive understanding of a company's scale, profitability, market value, and growth potential. Through visualisations, the analysis also explores correlations between these metrics and offers insights into country-level performance, highlighting economic dominance across various sectors. This holistic approach provides a multi-dimensional view of global financial powerhouses, investor confidence, and regional economic trends.

Key Metrics Used:

1. Revenue (Trailing Twelve Months - TTM): - Definition: This is the total income generated by a company from its operations in the last twelve months. - Potential Insights: High revenue often indicates market dominance or high sales volume. Comparing revenues can reveal which companies are the largest in terms of business volume.

2. Earnings (TTM): - Definition: This refers to the company's profit after taxes and expenses over the trailing twelve months. - Potential Insights: Companies with high earnings are more efficient at converting revenue into profit, suggesting better profitability or cost management. A comparison of earnings provides insight into profitability rather than just scale.

3. Market Capitalisation (Market Cap): - Definition: Market cap is the total value of a company's outstanding shares of stock, calculated as stock price multiplied by the number of shares. It indicates the company’s size in the stock market. - Potential Insights: High market cap usually indicates investor confidence in the company. Comparing market cap among the top 15 companies reveals their relative size in financial markets.

4. P/E Ratio (TTM): - Definition: Price-to-Earnings (P/E) ratio measures a company's current share price relative to its per-share earnings. - Potential Insights: A high P/E ratio may indicate that investors expect high growth in the future, while a low P/E ratio could imply undervaluation or scepticism about growth. Companies are compared by their growth prospects or current valuation.

5. Dividend Yield (TTM): - Definition: Dividend yield is a financial ratio that shows how much a company pays out in dividends each year relative to its share price. - Potential Insights: High dividend yield may indicate that a company returns more income to shareholders. It’s particularly useful for income-focused investors.

In this combined analysis, we will integrate the observations from the visualisations with the key financial metrics definitions and insights, to offer a comprehensive view of the top companies and country-level analysis across various financial dimensions.

Data Visualisations

Visualisation 1: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2296eddd53ddd4b84346b1ea1324ec0a%2FScreenshot%202024-10-01%2015.16.51.png?generation=1727864461164331&alt=media" alt=""> Visualisation 2: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fb35516c91e54eda75a03ff073e94dd73%2FScreenshot%202024-10-01%2015.17.53.png?generation=1727864511265917&alt=media" alt=""> Visualisation 3: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F506ca2428d34b15cd46e4a31261763d7%2FScreenshot%202024-10-01%2015.18.37.png?generation=1727864562835491&alt=media" alt=""> Visualisation 4: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F41e7a3e28c757239d26226f6a0ccdca9%2FScreenshot%202024-10-01%2015.19.20.png?generation=1727864614352037&alt=media" alt=""> A Markdown document with the R code for the above visualisations. link

1. Revenue (Trailing Twelve Months - TTM)

• Definition: The total income generated from a company’s operations over the last 12 months.
• Insights from Charts:
  • Revenue vs Earnings (Visualisation 2): Companies like Saudi Aramco are massive outliers with high revenues and even higher earnings, indicating impressive profitability despite their revenue volume.
  • Top 10 Countries by Average Revenue (Visualisation 3): China, South Korea, and Japan are at the top, with companies generating significant business volumes.
  • Analysis: High revenue companies like Walmart dominate the market but may not always convert this into proportional earnings or market cap growth. This could be due to operational costs or sector-specific challenges (retail margins being lower than tech).

2. Earnings (TTM)

• Definition: Profits...

With a market capitalization of 3.12 trillion U.S. dollars as of May 2024, Microsoft was the world’s largest company that year. Rounding out the top five were some of the world’s most recognizable brands: Apple, NVIDIA, Google’s parent company Alphabet, and Amazon. Saudi Aramco led the ranking of the world's most profitable companies in 2023, with a pre-tax income of nearly 250 billion U.S. dollars. How are market value and market capitalization determined? Market value and market capitalization are two terms frequently used – and confused - when discussing the profitability and viability of companies. Strictly speaking, market capitalization (or market cap) is the worth of a company based on the total value of all their shares; an important metric when determining the comparative value of companies for trading opportunities. Accordingly, many stock exchanges such as the New York or London Stock Exchange release market capitalization data on their listed companies. On the other hand, market value technically refers to what a company is worth in a much broader context. It is determined by multiple factors, including profitability, corporate debt, and the market environment as a whole. In this sense it aims to estimate the overall value of a company, with share price only being one element. Market value is therefore useful for determining whether a company’s shares are over- or undervalued, and in arriving at a price if the company is to be sold. Such valuations are generally made on a case-by-case basis though, and not regularly reported. For this reason, market capitalization is often reported as market value. What are the top companies in the world? The answer to this question depends on the metric used. Although the largest company by market capitalization, Microsoft's global revenue did not manage to crack the top 20 companies. Rather, American multinational retailer Walmart was ranked as the largest company in the world by revenue. Walmart also had the highest number of employees in the world.

🌍 Worldwide B2B Company Dataset | 65M+ Verified Records | Firmographics & API Access Power your sales, marketing, and investment strategies with the most comprehensive global B2B company data—verified, AI-driven, and updated bi-weekly.

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United States Realized Sales Revenue Growth data was reported at 3.228 % in Apr 2025. This records an increase from the previous number of 2.407 % for Mar 2025. United States Realized Sales Revenue Growth data is updated monthly, averaging 4.450 % from Sep 2016 (Median) to Apr 2025, with 104 observations. The data reached an all-time high of 13.646 % in Jun 2021 and a record low of -9.952 % in Jun 2020. United States Realized Sales Revenue Growth data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S015: Business Uncertainty Index.

United States Business Expectations: Sales Revenue Growth: Unsmoothed data was reported at 0.025 % in Sep 2020. This records an increase from the previous number of 0.010 % for Aug 2020. United States Business Expectations: Sales Revenue Growth: Unsmoothed data is updated monthly, averaging 0.042 % from Sep 2016 (Median) to Sep 2020, with 49 observations. The data reached an all-time high of 0.060 % in Jul 2018 and a record low of -0.028 % in Apr 2020. United States Business Expectations: Sales Revenue Growth: Unsmoothed data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.

China Internet: Sales Revenue: Year to Date: Data Center data was reported at 10,700.000 RMB mn in Sep 2018. This records an increase from the previous number of 8,880.000 RMB mn for Aug 2018. China Internet: Sales Revenue: Year to Date: Data Center data is updated monthly, averaging 6,250.000 RMB mn from Jun 2017 (Median) to Sep 2018, with 15 observations. The data reached an all-time high of 11,900.000 RMB mn in Dec 2017 and a record low of 1,760.000 RMB mn in Feb 2018. China Internet: Sales Revenue: Year to Date: Data Center data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Internet Business.

China E-commerce: Sales Revenue: Year to Date: Business to Business data was reported at 16.800 RMB bn in Jun 2017. This records a decrease from the previous number of 26.000 RMB bn for Dec 2016. China E-commerce: Sales Revenue: Year to Date: Business to Business data is updated quarterly, averaging 11.500 RMB bn from Jun 2010 (Median) to Jun 2017, with 19 observations. The data reached an all-time high of 26.000 RMB bn in Dec 2016 and a record low of 2.960 RMB bn in Mar 2011. China E-commerce: Sales Revenue: Year to Date: Business to Business data remains active status in CEIC and is reported by China e-business Research Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICG: E-commerce: Business Sales Revenue.

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

Books Sales and Ratings

Books Dataset: Analyzing Sales, Ratings, and Genres

By Josh Murrey [source]

About this dataset

The Books Dataset: Sales, Ratings, and Publication provides comprehensive information on various aspects of books, including their publishing year, author details, ratings given by readers, sales performance data, and genre classification. The dataset consists of several key columns that capture important attributes related to each book.

The Publishing Year column indicates the year in which each book was published. This information helps in understanding the chronological distribution of books in the dataset.

The Book Name column contains the titles of the books. Each book has a unique name that distinguishes it from others in the dataset.

The Author column specifies the name(s) of the author(s) responsible for creating each book. This information is crucial for understanding different authors' contributions and analyzing their impact on sales and ratings.

The language_code column represents a specific code assigned to indicate the language in which each book is written. This code serves as a reference point for language-based analysis within the dataset.

Each author's rating is captured in the Author_Rating column. This rating is based on their previous works and serves as an indicator of their reputation or acclaim among readers.

The average rating given by readers for each book is recorded in the Book_average_rating column. This value reflects how well-received a particular book is by its audience.

The number of ratings given to each book by readers can be found in the Book_ratings_count column. This metric helps gauge reader engagement and provides insights into popular or widely-discussed books within this dataset.

Books are classified into different genres or categories which are mentioned under the genre column. Genre classification allows for analyzing trends across specific literary genres or identifying patterns related to certain types of books.

Sales-related data includes both gross sales revenue (gross sales) generated by each book and publisher revenue (publisher revenue) earned from these sales transactions. These numeric values provide insights into financial performance aspects associated with the book market.

The sale price column denotes the specific price at which each book is sold. This information helps evaluate pricing strategies and their potential impact on sales figures.

Sales performance is further quantified through the sales rank column, which assigns a numerical rank to each book based on its sales performance. This ranking system aids in identifying high-performing books within the dataset.

Lastly, the units sold column captures the number of units of each book that have been sold. This data highlights popular books based on reader demand and serves as a crucial measure of commercial success within the dataset.

Overall, this expansive and comprehensive Books Dataset

How to use the dataset

Introduction:

• Getting Familiar with the Columns: The dataset contains multiple columns that provide different kinds of information:

• Book Name: The title of each book.

• Author: The name of the author who wrote the book.

• language_code: The code representing the language in which the book is written.

• Author_Rating: The rating assigned to the author based on their previous works.

• Book_average_rating: The average rating given to the book by readers.

• Book_ratings_count: The number of ratings given to the book by readers.

• genre: The genre or category to which the book belongs.

• gross sales: The total sales revenue generated by each book.

• publisher revenue: The revenue earned by publishers from selling each book.

• sale price: The price at which each copy of a book is sold.

• sales rank: A numeric value indicating a book's rank based on its sales performance in comparison to other books within its category (genre).

• units sold : Total number of copies sold for each specific title.

• Understanding Numeric and Textual Data: Numeric columns in this dataset include Publishing Year, Author_Rating, Book_average_rating, Book_ratings_count,gross sales,publisher revenue,sale price,sales rank and units sold; these provide quantitative insights that can be used for statistical analysis and comparisons.

Additionally,the columns 'Author','Book Name',and 'genre' contain textual data that provides descriptive elements such as authors' names and categorization genres.

• Exploring Relationships Between Data Points: By combining different co...

Our Business Listings Data dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For market and business analysis

Our Business Listings Data gives information about millions of companies, allowing you to find your competitors and see their weak and strong points.

Use cases

1. Pinpoint your competitors
2. Learn about your competitors' size, headcount, and revenue
3. Prepare a data-driven plan for the next quarter

For Investors

We recommend Business Listings Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Business Listings Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase
4. Identify and predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Business Listings Data saves time your employees would otherwise use it to manually find potential clients and choose the best prospects.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

Oracle’s cloud services and license support division is the company’s most profitable business segment, bringing in over ** billion U.S. dollars in its 2025 fiscal year. In that year, Oracle brought in annual revenue of close to ** billion U.S. dollars, its highest revenue figure to date. Oracle Corporation Oracle was founded by Larry Ellison in 1977 as a tech company primarily focused on relational databases. Today, Oracle ranks among the largest companies in the world in terms of market value and serves as the world’s most popular database management system provider. Oracle’s success is not only reflected in its booming sales figures, but also in its growing number of employees: between fiscal year 2008 and 2021, Oracle’s total employee number has grown substantially, increasing from around ****** to *******. Database market The global database market reached a size of ** billion U.S. dollars in 2020. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

Our Web Scraping dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies from around the globe, this scraped data enables you to filter potential clients based on specific criteria and hasten the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
5. Group your leads in segments for more accurate targeting

For market and business analysis

Our Web Scraping Data on companies gives information about millions of businesses, allowing you to evaluate your competitors.

Use cases

1. Know your competitors
2. See your competitors' size, headcount, and revenue
3. Come up with a data-driven strategy for the next quarter

For Investors

We recommend Web Scraping Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Web Scraping Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies looking for the next investment
3. Check if a startup is about to reach its maturity
4. Predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Web Scraping Data saves time your employees would otherwise use it to find potential clients and choose the best prospects manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
3. Based on the revenue, determine which companies are ready to convert
4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data was reported at 25.400 % in Jun 2017. This records an increase from the previous number of 18.180 % for Dec 2016. China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data is updated quarterly, averaging 25.650 % from Dec 2010 (Median) to Jun 2017, with 14 observations. The data reached an all-time high of 36.000 % in Dec 2011 and a record low of -13.700 % in Dec 2015. China E-commerce: Sales Revenue: YoY: Year to Date: Business to Business data remains active status in CEIC and is reported by China e-business Research Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICG: E-commerce: Business Sales Revenue.

Amazon is one of the most recognisable brands in the world, and the third largest by revenue. It was the fourth tech company to reach a $1 trillion market cap, and a market leader in e-commerce,...

United States Business Expectations: Sales Revenue Growth: Smoothed data was reported at 0.021 % in Sep 2020. This records an increase from the previous number of 0.014 % for Aug 2020. United States Business Expectations: Sales Revenue Growth: Smoothed data is updated monthly, averaging 0.035 % from Jan 2015 (Median) to Sep 2020, with 69 observations. The data reached an all-time high of 0.052 % in Jul 2018 and a record low of -0.010 % in May 2020. United States Business Expectations: Sales Revenue Growth: Smoothed data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.

In 2024, Amazon's total consolidated net sales revenue amounted to *** billion U.S. dollars, *** billion U.S. dollars of which were generated through international revenue channels. North America was the biggest operations segment, accumulating nearly *** billion U.S. dollars in net sales during the year. Sales activities Amazon appeals because it sells a wide range of products. Its departments include beauty, clothing, electronics, games and even wine, along with digital products and subscription services. In 2022, Amazon's largest revenue segment was online retail product sales with roughly *** billion U.S. dollars in global net sales. Retail third-party seller services ranked second with nearly *** billion U.S. dollars in sales. A weak spot Faster and more efficient delivery services come with a price. Data from the company's financial reports showed that Amazon's worldwide shipping costs amounted to a staggering **** billion U.S. dollars, up from **** billion U.S. dollars in 2021. Amazon's annual fulfillment expenses have also risen steadily, from **** billion U.S. dollars in 2021 to over ** billion U.S. dollars in 2022.

In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

United States Business Uncertainty: Sales Revenue Growth: Unsmoothed data was reported at 0.045 % in Sep 2020. This records a decrease from the previous number of 0.052 % for Aug 2020. United States Business Uncertainty: Sales Revenue Growth: Unsmoothed data is updated monthly, averaging 0.028 % from Sep 2016 (Median) to Sep 2020, with 49 observations. The data reached an all-time high of 0.059 % in Mar 2020 and a record low of 0.019 % in Jan 2020. United States Business Uncertainty: Sales Revenue Growth: Unsmoothed data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.