This dataset includes anonymized information about all of CSBDF's closed loans that were utilized in the lending economic impact analysis for FY21 (July 1, 2020 through June 30, 2021). The data contain anonymized information on all lending transactions during the period, including the socioeconomic characteristics of the recipient small businesses and their owner(s).

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.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

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Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

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Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

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Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

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Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

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Local authorities have received and distributed funding to support small and medium businesses in England during coronavirus. The datasets cover schemes managed by local authorities: Additional Restrictions Support Grant (ARG) Restart Grant - closed June 2021 Local Restrictions Support Grants (LRSG) and Christmas support payments - closed 2021 Small Business Grants Fund (SBGF) - closed August 2020 Retail, Hospitality and Leisure Business Grants Fund (RHLGF) - closed August 2020 Local Authority Discretionary Grants Fund (LADGF) - closed August 2020 The spreadsheets show the total amount of money that each local authority in England: received from central government distributed to SMEs 20 December 2021 update We have published the latest estimates by local authorities for payments made under this grant programme: Additional Restrictions Grants (up to and including 28 November 2021) The number of grants paid out is not necessarily the same as the number of businesses paid. The data has not received full verification.

Replication Materials for "The Effect of Retail Chain Expansions on Local Small Businesses: The Case of Dollar General in Florida" This repository provides the R scripts and supporting materials to replicate the methodology presented in the paper "The Effect of Retail Chain Expansions on Local Small Businesses: The Case of Dollar General in Florida." The study employs difference-in-differences methods and staggered event models to explore the impact of Dollar General store openings on neighboring small firms in Florida.

Data and Replication The analysis is based on the National Establishment Time-Series (NETS) Database, a proprietary product licensed from Walls & Associates. Under our data use agreement, we are prohibited from redistributing the raw dataset.

To ensure methodological transparency and demonstrate the functionality of our code, we have created and included a simulated dataset. While this simulated data will not reproduce the exact numerical findings of the paper, it is designed to mimic the structure of the original data. Its purpose is to allow users to run the provided R scripts for data processing and analysis from start to finish, confirming that the code works as intended.

Researchers who wish to fully replicate the paper's results must license the NETS database directly from the provider and apply our provided scripts.

Find the original dataset here

Pandas EDA with Plotly using this dataset here

Paycheck Protection Program (PPP) Loan Data – Key Aspects

SBA Values Transparency, Protecting Taxpayer Funds, and Protecting Proprietary Information of Small Businesses

In releasing PPP loan data to the public, SBA is maintaining a balance between providing transparency to American taxpayers and protecting small businesses’ confidential business information, such as payroll, and personally identifiable information. Small businesses are the driving force of American economic stability and are essential to America’s economic rebound from the pandemic. SBA is committed to ensuring that any release of PPP loan data does not harm small businesses or their employees.

PPP Is A Delegated Loan Making Process

PPP loans are not made by SBA. PPP loans are made by lending institutions and then guaranteed by SBA. Accordingly, borrowers apply to lenders and self-certify that they are eligible for PPP loans. The self- certification includes a good faith certification that the borrower has economic need requiring the loan and a certification that the borrower has applied the affiliation rules and is a small business, among other certifications The lender then reviews the borrower’s application, and if all the paperwork is in order, approves the loan and submits it to SBA.

PPP Loan Data Is Not Indicative of Loan Forgiveness or Program Compliance

A small business or non-profit organization that is listed in the publicly released data has been approved for a PPP loan by a delegated lender. However, the lender’s approval does not reflect a determination by SBA that the borrower is eligible for a PPP loan or entitled to loan forgiveness. All PPP loans are subject to SBA review and all loans over $2 million will automatically be reviewed. The fact that a borrower is listed in the data as having a PPP loan does not mean that SBA has determined that the borrower complied with program rules or is eligible to receive a PPP loan and loan forgiveness. Further, a small business’s receipt of a PPP loan should not be interpreted as an endorsement of the small business’ business activity or business model.

Cancelled Loans Do Not Appear In The PPP Loan Data

The public PPP data includes only active loans. Loans that were cancelled for any reason are not included in the public data release.

PPP Loan Demographic Data Is Voluntarily Submitted

PPP loan data reflects the information borrowers provided to their lenders in applying for PPP loans. SBA can make no representations about the accuracy or completeness of any information that borrowers provided to their lenders. Not all borrowers provided all information. For example, approximately 75% of all PPP loans did not include any demographic information because that information was not provided by the borrowers. SBA is working to collect more demographic information from borrowers to better understand which small businesses are benefiting from PPP loans. The loan forgiveness application expressly requests demographic information for borrowers.

The list tracks the number of businesses that NYC Business Acceleration has assisted in opening and how many jobs were created by those businesses. This data is up to date as of the date reflected in the "About" tab of this dataset.

The Office of Advocacy’s Small Business Profiles for the States and Territories are an annual analysis of each state’s small business activities. They provide information on small business employment, industry composition, small business borrowing, exporting, and survival rates, as well as business owner demographics. The profiles provide information for the 50 states, the District of Columbia, the U.S. territories, and the United States. Detailed historical data may be found in the Small Business Economy.

An Analysis of Engineering-as-Marketing Tools

Strategies for Expanding Business Reach

By Ian Greenleigh [source]

About this dataset

The engineering-as-marketing tools available today allow startups to maximize and take advantage of the engineering talents they possess. By creating useful tools such as calculators, widgets and microsites, businesses can get in front of potential customers and lead them to their products or services.

This dataset provides a comprehensive list of companies who are using engineering as a marketing strategy and the respective tools these companies have created for it. For each company you get information about their name, product/service, tool name, what the tool does and a URL for further information about it. Additionally there is an extra notes field providing more details about each company’s market habit or any other additional facts that could be relevant in understanding better the use cases these companies are leading with this new way of doing marketing through engineering driven strategies.

With this data you will be able to take a closer look at how effectively this strategy is working while being able to compare different approaches taken inside each industry vertical in order to maximize conversions among leads generated by all these amazing pieces work made possible by software engineers everywhere devoted every day making our lives easier constantly!

More Datasets

For more datasets, click here.

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How to use the dataset

Analyzing this data allows users to gain insights into how successful companies are using engineering-as-marketing techniques to generate leads and expand their customer base. It also provides a valuable resource for other organizations wanting to learn more about how other organizations have achieved success with such practices.

This dataset can be used in many ways such as:

• Analyzing different trends in which engineering-as-marketing techniques are being used across multiple industries
• Examining whether certain techniques lead to higher lead generation or increased customer base

• Comparing effectiveness between companies using different types of tools etc.

  To get started with this dataset, simply load it up into some kind of data analysis software package that supports csv file processing capabilities such as Tableau or R Studio. Then define each column appropriately by adding appropriate labels onto them so that they can be understood easily when looked at from a first glance perspective by yourself or other members on your team who are looking over your datasets before any analyses start happening on those files within your chosen data analysis software package . Now you should be all set up for analyzing this dataset!

Research Ideas

• Leveraging this data to understand the effectiveness of engineering-as-marketing for various companies.
• Creating a sentiment analysis of customers’ responses to engineering-as-marketing tools in order to determine which tools are most popular and successful.
• Analyzing what types of engineering-as-marketing tools have been most successful with specific customer segments, to inform future product development and marketing tactics

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: Engineering as Marketing.csv | Column name | Description | |:-------------------|:-------------------------------------------------------------------| | Company name | The name of the company. (String) | | What co does | A brief description of what the company does. (String) | | Tool name | The name of the engineering-as-marketing tool. (String) | | What tool does | A brief description of what the tool does. (String) | | URL | The URL of the engineering-as-marketing tool. (String) | | Notes | Additional notes about the engineering-as-marketing tool. (String) |

Acknowledgements

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

This dataset represents private non-retail shipping facilities.This dataset represents private non-retail shipping facilities. The Private Non-Retail Shipping layer contains motor carrier freight terminals for some of largest trucking companies in the U.S. (as measured by revenue reported by the Bureau of Transportation Statistics). Entities from the following companies are included: United Parcel Service (UPS), Yellow Freight System, Schneider, Roadway Express , FedEx , Con-Way Transportation , ABF Freight System, DHL, Corporate headquarters and other locations that do not actually handle freight are not included. No data could be obtained for Consolidated Freightways, Ryder Integrated Logistics, RPS and J.B. Hunt Transport Services. Facilities associated with these companies are not included in this dataset even though these companies have more annual revenue that some of the companies that are represented in this dataset. Many smaller, but still significant, companies have been excluded due to resource constraints. Examples include United Van Lines, Overnite Transportation, and American Freightways. Rail, air, sea, pipeline and inter-modal terminals are not included unless they are operated by one of the companies represented in this dataset. Facilities associated with the United States Postal Service (USPS) are not included. This dataset does not include retail shipping stores, drop boxes, and transportation operations that are not operated for hire. No entities located in American Samoa, The Northern Mariana Islands or the Virgin Islands are included in this dataset. The companies represented in this dataset are involved in the parcel delivery / courier service business or the freight service provider business. Parcel delivery / courier services deliver parcels, packages and other small shipments that typically weight less than 100 pounds according to the Bureau of Transportation Statistics. A freight service provider can include motor carriers, for-hire carriers and freight forwarders. According to the United Shippers Corporation, a motor carrier is defined as a company that provides truck transportation. A for-hire carrier is defined as a company that provides truck transportation of cargo belonging to others and is paid for doing so. A freight forwarder is defined as a company that arranges for truck transportation of cargo belonging to others, utilizing for-hire carriers to provide the actual truck transportation. The forwarder does assume the responsibility for the cargo from origin to destination and usually does take possession of the cargo at some point during the transportation. Forwarders typically assemble and consolidate less-than-truckload shipments into truckload shipments at origin and disassemble and deliver less-than-truckload shipments at destination. The intention of TGS is to include only those entities that meet the above definition. TGS was not able to contact all of the entities in this layer to verify that they met this definition, therefore some entities may be included in this layer that do not meet the definition. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute the oldest record dates from 09/08/2006 and the newest record dates from 10/03/2006.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

Description of the data in this data set: structure of the survey and pre-defined answers (if any) 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed} 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question) 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility} 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 8. How would you assess the value of the following data categories? 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)} 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)} 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company} 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”} 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

Format of the file .xls, .csv (for the first spreadsheet only), .odt

Licenses or restrictions CC-BY

Luxembourg number dataset is a popular platform for cell phone number lists. Many companies in Luxembourg use our phone number library for promotions. Our services have many advantages. Firstly, you will receive our products within 24 hours after confirming your order and payment. Secondly, our phone number list works on all devices, like smartphones, computers, and tablets. Thirdly, our packages are affordable and fit every budget. Moreover, our Luxembourg number dataset also has a filter option. This allows you to find specific numbers based on your needs. You will also receive a free updated telemarketing list six months after purchase. Our database complies with GDPR and provides over 95% accuracy. If there are any errors, we will fix them for free. This ensures you have accurate and current phone numbers, improving your telemarketing efforts. Luxembourg phone data helps you easily contact people or businesses in Luxembourg. Our system is user-friendly and saves time. It also provides additional details like location, age, and gender. We offer a “Do Not Call” list to avoid legal issues in SMS marketing. You can get both a call list and an SMS marketing list in one package. Also, List to Data helps businesses find the right telephone numbers quickly, which makes the process even easier. In addition, our Luxembourg phone data contains both B2B and B2C phone numbers, which support the growth of your business. You can get our customer-friendly after-sales service. We also provide excellent customer service 24/7. If you have any questions or problems, please call us anytime. We are always here to assist you in any situation. Luxembourg phone number list is a valuable tool. It helps you connect with people in Luxembourg. The list includes phone numbers that help companies reach new customers. With name, age, and contact information, it is perfect for marketing. So, use it for promotions, updates, or feedback. This phone number list is available at a reasonable price. So, buy this mobile phone number list at a low price and get huge benefits. Moreover, our Luxembourg phone number list offers good value for your money. Since they update and ensure its accuracy, it helps you get the best results. Moreover, telemarketing saves money and grows your brand. Our cell phone list increases sales. Therefore, you will get great returns on marketing.

More than 200 million businesses use Facebook globally. The goal of Meta’s quarterly Small Business Surveys is to learn about the unique perspectives, challenges and opportunities of small and medium-sized businesses (SMBs).

The Future of Business (FoB) Survey is conducted biannually in partnership with the World Bank and the Organisation for Economic Cooperation and Development (OECD) across nearly 100 countries. The target population consists of SMEs that have an active Facebook Business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. Meta also conducts the Global State of Small Business (GSoSB) Survey bi-annually in partnership with various academic partners across approximately 30 countries. Similarly to the FoB Survey, the target population is active Facebook Page Administrators, but also includes the general population of Facebook users.

Survey questions for all surveys cover a range of topics depending on the survey wave such as business characteristics, challenges, financials and strategy in addition to custom modules related to regulation, gender inequity, access to finance, digital technologies, reduction in revenues, business closures, international trade, inflation, reduction of employees and challenges/needs of the business.

Aggregated country level data for each survey wave is available to the public on HDX and controlled access microdata is available to Data for Good at Meta partners. Please visit https://dataforgood.facebook.com/dfg/tools/future-of-business-survey to apply for access to microdata or contact dataforgood@fb.com for any questions.

Overview This dataset contains information about the largest companies in the United States by revenue. It includes key attributes such as company name, industry, annual revenue, profit, number of employees, and the state where the company is headquartered. The dataset provides valuable insights into the financial and operational aspects of these major corporations.

Columns Rank: Ranking of the company based on its annual revenue. Name: Name of the company. Industry: Industry in which the company operates. Revenue: Annual revenue of the company in millions of dollars. Profit: Annual profit of the company in millions of dollars. Employees: Number of employees working for the company. State: State where the company’s headquarters are located. Key Insights Revenue Distribution: Significant variation in revenue among the top companies, with some generating much higher revenues. Profit Margins: Wide variation in profit margins, indicating different levels of profitability across industries. Employee Numbers: Disparity in the number of employees, reflecting differences in business models and operational scales. Geographic Spread: Companies are headquartered in various states, with certain states having a higher concentration of large companies. Potential Uses Industry Analysis: Understand trends and performance in different industries. Economic Research: Analyze the economic impact of these large companies. Business Strategy: Inform business strategies and market analysis. Educational Purposes: Use as a case study for business and economic courses. Future Work In-Depth Industry Analysis: Explore specific industries to identify trends and outliers. Time-Series Analysis: Analyze trends over time if historical data becomes available. Comparative Analysis: Compare with similar datasets from other countries. Advanced Visualization: Create interactive dashboards for better data presentation. This dataset is a valuable resource for anyone interested in the financial and operational characteristics of the largest companies in the United States.

Women's Business Centers (WBCs) represent a national network of nearly 100 educational centers throughout the United States and its territories, which are designed to assist women in starting and growing small businesses. WBCs seek to "level the playing field" for women entrepreneurs, who still face unique obstacles in the business world. SBA’s Office of Women’s Business Ownership (OWBO) oversees the WBC network, which provides entrepreneurs (especially women who are economically or socially disadvantaged) comprehensive training and counseling on a variety of topics in several languages