A comprehensive dataset covering small business statistics in 2025, including failure rates, growth data, average revenue, number of employees, and market insights.

The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The Los Angeles BusinessSource Centers provide startup ventures and current small business owners various cost effective tools to make their business a success. Through these tools, small businesses can grow and remain competitive within the City of Los Angeles. Startups focuses on owners of businesses with five (5) or fewer employees, one of whom owns the enterprise, and have net operating income of less than Two Hundred Thousand Dollars ($200,000). This focus is particularly important as the majority of the businesses within the City may be categorized as “survivors,” and historically, many such businesses fail in their first two years of operation. The survival and growth of such businesses is still very important to the ongoing economic vitality of the City.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV’s ability to help strategic decision-making in dynamic corporate situations.

Abstract (en): Dun's Review began publishing monthly data on business failures by branch of business during the 1890s. At that time, a business failure was defined as a concern which was involved in a court proceeding or voluntary action which was likely to end in loss to creditors. Liabilities of failed businesses were defined "as all liabilities except long-term publicly-held obligations, chiefly bonds." Dun's published data on failures by branch of business from 1895 through 1935. This dataset reconstructs that series and links it to its successors. The successor series include data on business failures by division of industry, which Dun and Bradstreet's published from 1934 through 1940. This study includes six parts. Part One contains aggregate liabilities in dollars, broken down by branch, month, and year. Part Two contains aggregate numbers of business failures broken down by branch, month, and year. Part Three contains aggregate liability in dollars broken down by division, month, and year. Part Four contains aggregate numbers of business failures broken down by division, month, and year. Part Five contains aggregate liabilities broken down by sector, month, and year. Part Six contains aggregate numbers of business failures broken down by sector, month, and year. Part One and Part Two contain 36 variables and 562 cases. Part Three and Part Four contain 51 variables and 60 cases. Part Five and Part Six contain 6 variables and 562 cases. This study allows for economic analysis of business failures. It is intended to provide a resource on business failure and liabilites from 1895 to 1940. Data originally collected from court filings at municipal, county, state, and United States district court houses throughout the United States from 1895 through 1940. Data published periodically by R. G. Dun and Company, Bradstreet's Company, and their successors through 1940. From their publications, the principal investigators collected, cleaned, compiled, and computerized the current data series. Variables include monthly, unadjusted, liabilities and monthly, unadjusted, number of failures for different branches, sectors, divisons. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Businesses that failed in the United States from 1895 through 1940. Smallest Geographic Unit: United States The data consist of the aggregate number of corporations filing for bankruptcy in various industries each month in the United States and the total liabilities of those corporations. Please refer to the codebook for sampling information in the "Original P.I. Documentation" section. Additional information can be found by visiting the National Bureau of Economic Research (NBER) Web site. For additional information on these datasets please see the National Bureau of Economic Research (NBER) Web site.The dates in the Original P.I. Documentation for Business Failures by Industry in the United States range from 1895 to 1939, however, the data range from 1895 to 1940. The title for ICPSR 34016 has been changed to reflect the data.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

This study is about what matters: predicting when microfinance institutions might fail, especially in places where financial stability is closely linked to economic inclusion. The challenge? Creating something practical and usable. The Adjusted Gross Granular Model (ARGM) model comes here. It combines clever techniques, such as granular computing and machine learning, to handle messy and imbalanced data, ensuring that the model is not just a theoretical concept but a practical tool that can be used in the real world.Data from 56 financial institutions in Peru was analyzed over almost a decade (2014–2023). The results were quite promising. The model detected risks with nearly 90% accuracy in detecting failures and was right more than 95% of the time in identifying safe institutions. But what does this mean in practice? It was tested and flagged six institutions (20% of the total) as high risk. This tool’s impact on emerging markets would be very significant. Financial regulators could act in advance with this model, potentially preventing financial disasters. This is not just a theoretical exercise but a practical solution to a pressing problem in these markets, where every failure has domino effects on small businesses and clients in local communities, who may see their life savings affected and lost due to the failure of these institutions. Ultimately, this research is not just about a machine learning model or using statistics to evaluate results. It is about giving regulators and supervisors of financial institutions a tool they can rely on to help them take action before it is too late when microfinance institutions get into bad financial shape and to make immediate decisions in the event of a possible collapse.

The Global Entrepreneurship Monitor (GEM) was designed to capture various aspects of firm creation and entrepreneurship across countries. The data have been collected over a number of years (1998-2003) and include responses from 4,685 experts in over 38 countries and three subnational regions. This study seeks to measure the national attributes considered critical for new firm births and small firm growth. The dataset is a harmonized file capturing the results from all of the surveys. The expert, or key informant, questionnaire was improved and adjusted each year to increase the reliability of multi-item indices and provide for the addition of new dimensions. For each version of the questionnaire, respondents completed 70-80 standardized items that were the basis for 12-15 multi-item indices. Respondents were initially asked a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, and whether they thought it was a good time to do so. Respondents were also asked about the process of starting up a new business; whether they had done anything to start a new business in the past 12 months; whether they would own all, part, or none of the new business; how many people would be involved with the new business; what sort of business they were starting; and what they would sell. In addition, respondents identified the total start-up costs, the various sources of the start-up money, and why they were involved in the start-up. Respondents then answered a set of questions to assess the national conditions influencing entrepreneurial activity in their own country. In this respect, respondents provided their opinions on business and entrepreneurial education, the integration of new technology in businesses, the availability of financial support through government policies and programs, the availability of subcontractors, yearly changes in the economic market, and the physical infrastructure in their country. Views were also elicited from respondents about their national cultures in regard to entrepreneurial efforts and opportunities, attitudes towards entrepreneurs in general, women entrepreneurs and the resources available to them, and citizens' knowledge and experience with new businesses. They also gave their views on the Intellectual Property Rights (IPR) legislation and its enforcement in their respective countries. Respondents were then queried on the technological strengths of their country by ranking the top five sectors in which there has been development of the greatest number of technology-intensive start-up companies in the past ten years. Finally, respondents were asked the same general questions as those used in the GLOBAL ENTREPRENEURSHIP MONITOR (GEM): ADULT POPULATION SURVEY DATA SET, 1998-2003 (ICPSR 20320) in order to ascertain whether the opinions and behaviors of the current "expert" respondents differ from those of the general population. These questions included whether they were starting a new business, if there were opportunities for new businesses, funding sources for a new business, skills required to start a new business, shutting down a business, and whether a fear of failure was preventing the start of a new business. The dataset also contains variables that describe the respondent's gender, age, educational attainment, labor force status, the entrepreneurial areas in which they feel they have strong expertise, and the month and year the survey was conducted.

Annual data on births, deaths and survival of businesses in the UK, by geographical area and Standard Industrial Classification 2007: SIC 2007 groups.

SBA 7(a) and 504 loan data reports for loans approved since FY1991.

Bankruptcies in the United States increased to 23309 Companies in the first quarter of 2025 from 23107 Companies in the fourth quarter of 2024. This dataset provides - United States Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

With competition getting more stiffer in telecom direct-to-home operators, complaint management process will lead to the key outcome on business survival and growth.

As per the prevailing process and business setups, to cut down on cost companies prefer outsourcing of call centre operations and complaint management process.

Process Steps for handling customer complaints

Customer logs complaint from various modes like call at the call centre, visit retailer/company showroom/ website site/ App/ Email on customer care/ Social media (Facebook / Twitter) All major operators in business give complete attention and alertness on each and every customer complaint and after customer complaint getting logged in the system , same flows to back end team through CRM workflows Dedicated Service recovery teams made available in backend or service agencies Each and every case got assigned to the backend/service team for a customer visit and complaint closure There are broadly two types of transactions for complaints (FTR) First-time resolution and (NFTR) Non-first time resolution. For FTR cases, front end team like a call centre or showroom executive do the required troubleshooting and give resolution to customer and case closed as per customer satisfaction In NFTR cases, backend operation team aligned and visit done at customer premises and closure done by rectifying hardware, product or Outdoor unit. Some operators give delight code/ Happy code to the customer on logging of NFTR complaints and same code need to be provided to the engineer if complaint got resolved as per customer satisfaction Major Challenges in handling customer complaints

During sudden technical failure or any natural calamity, there will be a high flow of complaints, which takes time to manage and close the complaint to customer expectation. These instances bring challenging time in telecom/ DTH operators as a customer not ready for any delay in resolution As per business requirements, there has been a lot of fresh hiring done for call centre advisors and a lot of efforts being put on their training but due to the initial learning curve, basic mistakes done by new hires leading to irrelevant and wrong complaints being raised in the system. This sometimes leads to delay in resolution and telecom/DTH operator undergo firefighting scenarios Managing social media errors is also one of the challenging tasks, sometimes operator’s reputation goes to stake due to small negligence of any employee or any process failure

content

This dataset consists of almost 2224 rows and 11 columns which belongs to all complaints can be raised by user

Acknowledgements

Reference : https://github.com/Kavyapriyakp/Telecome-Consumer-Complaints-Data-Analytics-PYTHON

Differentiation processes of the beneficiaries of agrarian reform after allocation of land in Pelarco, (Chile). Topics: type of land acquisition; time spent farming the land; length of time spent as a farmer; work carried out on the old property; membership and leadership position in organizations; interest in additional land acquisition; preferred land ownership; willingness to lease land owned; reasons for success and failure of agricultural activities; personal ideas of agricultural success; financial prerequisites for personal work migration; knowledge of the Colbun-Machicura project; knowledge of the country´s foreign debt; preference for a high level of competence vs. the economic necessity of working in filling a teaching position; assumed amount of salary of a teacher or college instructor; type of house construction; condition of house; size of house; location of kitchen; number of rooms and beds in total and per family member; possession of furniture; possession of a car; number of children; childrens´ average age and education level; amount of subsidy; social insurance contributions and social insurance institution; living in the house mentioned as well as structural changes made; construction of barns; value of fencing; ownership of capital; ownership of machines and animals at the time of allocation. Additionally encoded were: identification of interviewer and place of interview. In addition the data set contains the encoding of open questions about technical production knowledge and ability, the willingness to improve on this knowledge as well as analogous questions on knowledge and ability in the area of business management and the willingness to expand on this knowledge. The answers were each assigned points from 1 (bad) to 10 (good). Differenzierungsprozesse von Nutznießern der Agrarreform nach der Landzuteilung in Pelarco (Chile). Themen: Art des Landerwerbs; Dauer der Landbewirtschaftung; Dauer der Tätigkeit als Bauer; verrichtete Tätigkeit auf dem alten Grundstück; Mitgliedschaft und führende Position in Organisationen; Interesse an zusätzlichem Landerwerb; präferierter Landbesitz; Bereitschaft zur Verpachtung des eigenen Landes; Gründe für Erfolg und Mißerfolg landwirtschaftlicher Aktivitäten; eigene Vorstellung vom landwirtschaftlichem Erfolg; finanzielle Voraussetzungen für eigene Arbeitsmigration; Kenntnis des Projekts Colbun-Machicura; Kenntnis der Auslandsverschuldung des Landes; Präferenz für höhere Kompetenz vs. der ökonomischen Notwendigkeit zu arbeiten bei der Besetzung einer Lehrerstelle; vermutete Höhe des Lohns eines Lehrers und Hochschullehrers; Art der Hauskonstruktion; Zustand des Hauses; Hausgröße; Örtlichkeit der Küche; Anzahl der Räume und Betten insgesamt und je Familienmitglied; Möbelbesitz; Fahrzeugbesitz; Kinderzahl; durchschnittliches Alter und durchschnittlicher Bildungsgrad der Kinder; Höhe der Subventionen; Sozialversicherungsbeitrag und Sozialversicherungsinstitution; Wohnen im zugesprochenen Haus sowie erfolgte bauliche Veränderungen; Errichtung von Scheunen; Wert der Umzäunung; Kapitalbesitz; Besitz von Maschinen und Tieren zum Zeitpunkt der Besitzzuweisung. Zusätzlich verkodet wurde: Intervieweridentifikation und Ort des Interviews. Der Datensatz enthält zudem die Verkodung offener Fragen zum produktionstechnischen Wissen und Können, der Bereitschaft dieses Wissen zu verbessern sowie analog dazu Fragen zum Wissen und Können im Bereich der Betriebsführung und der Bereitschaft dieses Wissen zu erweitern. Die Antworten wurden jeweils mit Punkten zwischen 1 (schlecht) und 10 (gut) bewertet. Systematic random sample based on the property tax register.

The Department of Jobs and Small Business publishes quarterly reports on a range of job seeker compliance data.

Quarterly compliance data has been available on the department's website since 2006. The data relates to those job seekers on activity tested income support payments for the relevant quarter, and many of the indicators are broken down by categories such as age, gender, payment type, indigeneity and employment service programme - i.e. jobactive, Disability Employment Services (DES) and the Community Development Programme (CDP).

The data includes a range of statistics on job seeker attendance at appointments with employment services providers, income support payment suspensions, the number and type of non-compliance reported and the number of participation failures and financial penalties applied.

Bankruptcies in Australia decreased to 1303 Companies in June from 1308 Companies in May of 2025. This dataset provides - Australia Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Research into the Barriers to Take-up and Use of Business Support, 2011 study was a one-off survey concerned with the barriers to the take up of formal external assistance, the reasons for such barriers and whether there is evidence of market failure, and the extent of latent demand for business support services. The research differentiates between: (i) non users of external assistance; (ii) users of private sector external assistance such as from accountants, solicitors, consultants, and trade associations; and (iii) users of public sector business assistance such as from Business Link, UK Trade and Investment, and local authorities.

The research was based on a CATI telephone survey of the owner-managers of Small and Medium-sized Enterprises (SMEs) in England undertaken in March 2011.

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Training and test classification evaluation metrics with SMOTE.