Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. 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.

Corporate Profits in the United Kingdom increased to 152043 GBP Million in the first quarter of 2025 from 148024 GBP Million in the fourth quarter of 2024. This dataset provides the latest reported value for - United Kingdom Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides values for CORPORATE PROFITS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Corporate Profits in Canada increased to 142196 CAD Million in the first quarter of 2025 from 137544 CAD Million in the fourth quarter of 2024. This dataset provides - Canada Corporate Profits - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Corporate Profits in Japan decreased to 28469.40 JPY Billion in the first quarter of 2025 from 28691.91 JPY Billion in the fourth quarter of 2024. This dataset provides - Japan Corporate Profits - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Annual and quarterly data for the latest profitability estimates of UK companies.

Corporate Profits in Russia increased to 11590 RUB Billion in May from 9830 RUB Billion in April of 2025. This dataset provides - Russia Corporate Profits- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located. Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44). Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division. Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry. County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around ** billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate **** ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around **** billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Corporate Profits in Denmark increased to 475544 DKK Million in 2021 from 309238 DKK Million in 2020. This dataset provides - Denmark Corporate Profits- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

There are two commonly understood ways in which a company is considered public: first, the company’s securities trade on public markets; and second, the company discloses certain business and financial information regularly to the public.

With the COVID-19 pandemic affecting many aspects of the business, a large portion of companies have proceeded with executive compensation adjustments as a response. Corporate boards running America’s largest public firms are giving top executives outsize compensation packages that have grown much faster than the stock market and the pay of typical workers, college graduates, and even the top 0.1%.
Excessive CEO pay is a major contributor to rising inequality that we could safely do away with. CEOs are getting more because of their power to set pay and because so much of their pay (more than 80%) is stock-related, not because they are increasing their productivity or possess specific, high-demand skills. This escalation of CEO compensation, and of executive compensation more generally, has fueled the growth of top 1.0% and top 0.1% incomes, leaving less of the fruits of economic growth for ordinary workers and widening the gap between very high earners and the bottom 90%. The economy would suffer no harm if CEOs were paid less (or were taxed more).

Content

CEO_compensation_top50_2020.csv contains data about the top 50 paid CEOs in 2020. Parameters include: - Total Granted Compensation (TGC) - Total Realized Compensation (TRC) - Total Shareholder Return (TSR) in % - TSR 1YR growth in % - TGC 1YR growth in % - TRC 1YR growth in %

CEO_largestrevenue_highestpaid_2020-21.csv contains data about the CEO & Employee pay at the largest companies by revenue in 2020/2021, as well as the New York Times published 200 highest-paid CEOs in 2020. Parameters include: - CEO Total Compensation - Median Employee Pay - Pay change over previous year - Fiscal Year Revenue - Revenue change over previous year - CEO to Employee Pay Ratio

Acknowledgements

Source: CGLytics and Equilar

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Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

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Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

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Analyzing Customer Churn and Its Impact on Revenue

Exploring Patterns and Trends

By [source]

About this dataset

This dataset contains customer data from multiple sources that can be used to predict customer churn and analyze its effect on revenue. We'll use this data to gain insights into customer behavior, such as when customers are likely to churn, how their behavior affects revenue and what patterns of behavior can help us better understand customers. This dataset features several different attributes for each customer: their unique identifier, total charges paid over time, contract information and more. Additionally, we can use the predictive analytical models based on this data to identify at-risk customers that may be more likely to churn in the near future. By gaining deep insight into which customers are most likely to leave and why they are leaving, businesses will be better equipped with tools necessary for taking proactive measures against potential revenue losses due to customer churn

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For more datasets, click here.

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How to use the dataset

This dataset is an excellent tool for businesses to understand what factors are associated with customer churn and its impact on revenue. It can provide insights into which customers are most likely to leave, and how companies can prevent them from leaving.

To use this dataset, here are the steps businesses can follow: 1. Understand each of the data points available in the dataset and what they represent - For example, CustomerID is a unique identifier for each customer, Churn indicates if a customer has left the company or not, gender denotes what gender the customer is etc. 2. Analyze any trends or patterns in your data – Look out for correlations between different variables like OnlineSecurity usage and Churn rate or MonthlyCharges and tenure to determine how these variables affect customers’ decisions to stay with a company or leave it etc. 3. Use machine learning models on your dataset – Utilize supervised learning algorithms such as logistic regression on this dataset to determine which variable most closely correlates with loyalty of customers i.e., which variable will decide whether a particular customer will stay with your company or not?
4. Explore various ways of increasing retention rates – Think about ways you could incentivize customers who might be considering leaving their current provider (for example, offer discounts, free trials etc.). You could try different strategies like A/B testing too see which incentive works best for churn prevention/retention rate increase etc. 5.. Test out strategies before implementing them - Once you have decided on incentives that might work well, run small scale tests to check if they generate desired results before investing resources into full rollout programs .The systems based on machine learning algorithms allows you to quickly test assumptions efficiently without large investments in time & money prior committing these changes fully operational processes

Research Ideas

• Using customer data to identify and target customers who are at a high risk of churning to counter this effect with relevant customer service initiatives.
• Analyzing the effects of promotional campaigns and loyalty programs on customer retention rates and overall revenue.
• Machine learning models that predict future chances of customer churn which can be used by businesses to improve strategies for better retention & profitability

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: dataset1.csv | Column name | Description | |:---------------------|:-----------------------------------------------------------------| | CustomerID | Unique identifier for each customer. (Integer) | | Churn | Whether or not the customer has churned. (Boolean) | | gender | Gender of the customer. (String) | | SeniorCitizen | Whether or not the customer is a senior citizen. (Boolean) ...

Analysis of ‘Germany Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/germany-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Profits ($billion), Assets ($billion), technical information and other features such as: - Sales ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Assets ($billion) on Sales ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

Context

Microsoft is an American company that develops and distributes software and services such as: a search engine (Bing), cloud solutions and the computer operating system Windows.

Market cap

Market capitalization of Microsoft (MSFT)

Market cap: $3.085 Trillion USD

As of February 2025 Microsoft has a market cap of $3.085 Trillion USD. This makes Microsoft the world's 2nd most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Microsoft (MSFT)
Revenue in 2024 (TTM): $254.19 Billion USD

According to Microsoft's latest financial reports the company's current revenue (TTM ) is $254.19 Billion USD. In 2023 the company made a revenue of $227.58 Billion USD an increase over the revenue in the year 2022 that were of $204.09 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Microsoft (MSFT)
Earnings in 2024 (TTM): $110.77 Billion USD

According to Microsoft's latest financial reports the company's current earnings are $254.19 Billion USD. In 2023 the company made an earning of $101.21 Billion USD, an increase over its 2022 earnings that were of $82.58 Billion USD. The earnings displayed on this page are the earnings before interest and taxes or simply EBIT.

End of Day market cap according to different sources On Feb 2nd, 2025 the market cap of Microsoft was reported to be:

• $3.085 Trillion USD by Nasdaq

• $3.085 Trillion USD by CompaniesMarketCap

• $3.085 Trillion USD by Yahoo Finance

Content

Geography: USA

Time period: March 1986- February 2025

Unit of analysis: Microsoft Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

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