Only **** percent of businesses in the United States founded between March 2014 to March 2024 were still operating in March 2024. By 2019, around half of such U.S. businesses had still survived.

Success.ai delivers comprehensive access to Small Business Contact Data, tailored to connect you with North American entrepreneurs and small business leaders. Our extensive database includes verified profiles of over 170 million professionals, ensuring direct access to decision-makers in various industries. With AI-validated accuracy, continuously updated datasets, and a focus on compliance, Success.ai empowers businesses to enhance their marketing, sales, and recruitment efforts while staying ahead in a competitive market.

Key Features of Success.ai's Small Business Contact Data:

Extensive Coverage: Access profiles for small business owners and entrepreneurs across the United States, Canada, and Mexico. Our database spans multiple industries, from retail to technology, providing diverse business insights.

Verified Contact Details: Each profile includes work emails, phone numbers, and firmographic data, enabling precise and effective outreach.

Industry-Specific Data: Target key sectors such as e-commerce, professional services, healthcare, manufacturing, and more, with tailored datasets designed to meet your specific business needs.

Real-Time Updates: Continuously updated to maintain a 99% accuracy rate, our data ensures that your campaigns are always backed by the most current information.

Ethical and Compliant: Fully compliant with GDPR and other global data protection regulations, ensuring ethical use of all contact data.

Why Choose Success.ai for Small Business Contact Data?

Best Price Guarantee: Enjoy the most competitive pricing in the market, delivering exceptional value for comprehensive and verified contact data.

AI-Validated Accuracy: Our advanced AI systems meticulously validate every data point to deliver unmatched reliability and precision.

Customizable Data Solutions: From hyper-targeted regional datasets to comprehensive industry-wide insights, we tailor our offerings to meet your exact requirements.

Scalable Access: Whether you're a startup or an enterprise, our solutions are designed to scale with your business needs.

Comprehensive Use Cases for Small Business Contact Data:

1. Targeted Marketing Campaigns:

Refine your marketing strategy by leveraging verified contact details for small business owners. Execute highly personalized email, phone, and multi-channel campaigns with precision.

1. Sales Prospecting:

Identify and connect with decision-makers in key industries. Use detailed profiles to enhance your sales outreach, close deals faster, and build long-term client relationships.

1. Recruitment and Talent Acquisition:

Discover small business leaders and key players in specific industries to strengthen your recruitment pipeline. Access up-to-date profiles for sourcing top talent.

1. Market Research:

Gain insights into small business trends, operational challenges, and industry benchmarks. Leverage this data for competitive analysis and market positioning.

1. Local Business Engagement:

Foster partnerships with small businesses by identifying community leaders and entrepreneurial influencers in your target regions.

APIs to Enhance Your Campaigns:

Enrichment API: Integrate real-time updates into your CRM and marketing systems to maintain accurate and actionable contact data. Perfect for businesses looking to improve lead quality.

Lead Generation API: Maximize your lead generation efforts with access to verified contact details, including emails and phone numbers. Tailored for precise targeting of small business decision-makers.

Tailored Solutions for Diverse Needs:

Marketing Agencies: Create targeted campaigns with verified data for small business owners across diverse sectors.

Sales Teams: Drive revenue growth with detailed profiles and direct access to decision-makers.

Recruiters: Build a talent pipeline with current and verified data on small business leaders and professionals.

Consultants: Provide data-driven recommendations to clients by leveraging detailed small business insights.

What Sets Success.ai Apart?

170M+ Profiles: Access a vast and detailed database of small business owners and entrepreneurs.

Global Standards Compliance: Rest assured knowing all data is ethically sourced and compliant with global privacy regulations.

Flexible Integration: Seamlessly integrate data into your existing workflows with customizable delivery options.

Dedicated Support: Our team of experts is always available to ensure you maximize the value of our solutions.

Empower Your Outreach with Success.ai:

Success.ai’s Small Business Contact Data is your gateway to building meaningful connections with North American entrepreneurs. Whether you're driving targeted marketing campaigns, enhancing sales prospecting, or conducting in-depth market research, our verified datasets provide the tools you need to succeed.

Get started with Success.ai today and unlock the potential of verified Small Business ...

As of 2020, the average startup failure rate in Africa stood at ** percent. However, the rate differed across countries. In Ethiopia and Rwanda, ** percent of the startups ceased operations, while Kenyan startups had a failure rate of ** percent in the same year.

Almost one in five new businesses in the European Union failed in their first year according to the one-year business survival rates in the European Union for 2018. In this year, the country with the highest business survival rate was Greece, which had a one-year survival rate of 96.7 percent, while Lithuania had the lowest at 63.57.

This DataSet to track the latest trends, we’ve compiled small business and startup statistics to better understand what makes a startup tick. If you’re looking to build a startup or just interested in diving into the numbers, check out these informative statistics on success, failure, funding and more before getting started.

Objective The objective of the project is to predict whether a startup which is currently operating turn into a success or a failure. The success of a company is defined as the event that gives the company's founders a large sum of money through the process of M&A (Merger and Acquisition) or an IPO (Initial Public Offering). A company would be considered as failed if it had to be shutdown.

This problem will be solved through a Supervised Machine Learning approach by training a model based on the history of startups which were either acquired or closed. The trained model will then be used to make predictions on startups which are currently operating to determine their success/failure.

Do an EDA and try to predict which startups and in which field achieve great success! https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F9770082%2Fd1cc4e53157d2f3f0a9f661b6f2cd28f%2FGroup%202215.jpg?generation=1674420531095211&alt=media" alt="">

You will have to answer the following questions: - How Many New Businesses Fail ? - How Many New Businesses Secsees ? - Reasons for Failing - How to Avoid Failing And many other questions...

As of 2023, UK business enterprises founded in 2022 had a one-year survival rate of 92.3 percent, compared with 93.4 percent in the previous year. For businesses founded in 2018, just 39.4 percent were still operating in 2022.

Explore the rise and fall of 483 startups with this detailed dataset, sourced from CB Insights' "Startup Failure Post-Mortem" compilation (last updated May 29, 2024). This dataset catalogs failed ventures across diverse industries, offering insights into their names, operational sectors, and years of activity—from founding to shutdown. Spanning over three decades (1992–2024), it captures the volatile world of innovation, from dot-com busts to modern tech flops.

Columns: - Name: The startup’s name. - Sector: Industry classification (all "Information" here, NAICS 51). - Years of Operation: Lifespan in years, with founding and shutdown years (e.g., "3 (2010-2013)"). - What They Did: Brief overview of the startup’s product or service. - How Much They Raised: Funding amount in millions ($M) or tied to parent totals (e.g., "$1.7B (Dropbox)"). - Why They Failed: Reason the startup ceased or faded as a standalone entity. - Takeaway: Key lesson derived from the failure. - Giants: 1 if lost to tech giants (e.g., Google, Amazon), 0 if not. - No Budget: 1 if ran out of cash or was underfunded, 0 if not. - Competition: 1 if outpaced by direct rivals (not just giants), 0 if not. - Poor Market Fit: 1 if product lacked demand or user interest, 0 if not. - Acquisition Stagnation: 1 if stagnated or faded after acquisition, 0 if not. - Platform Dependency: 1 if overly reliant on another platform (e.g., Twitter), 0 if not. - Monetization Failure: 1 if couldn’t turn users into revenue, 0 if not. - Niche Limits: 1 if too niche to scale broadly, 0 if not. - Execution Flaws: 1 if mismanagement or tech failures contributed, 0 if not. - Trend Shifts: 1 if market or user trends shifted away, 0 if not. - Toxicity/Trust Issues: 1 if user toxicity or trust breaches hurt, 0 if not. - Regulatory Pressure: 1 if legal or regulatory issues forced closure, 0 if not. - Overhype: 1 if hype exceeded deliverable results, 0 if not.

Uncover the secrets of business failures with this comprehensive dataset. Data analysts, entrepreneurs, and researchers can: pinpoint failure causes (e.g., 92 cases attributed to 'Giants'), analyze funding's impact on success, and visualize company lifespans. Explore why giants like Google contributed to 57% of failures, or why 29% collapsed after acquisition. Whether you need industry-specific failure counts, lifespan visualizations, or historical trend analysis, this dataset provides valuable post-mortem insights for strategic decision-making.

Source: CB Insights - Startup Failure Post-Mortem

License: This dataset is derived from publicly available data on CB Insights’ site; please review their terms for usage. Compiled and structured for research purposes.

Let’s learn from the ashes of ambition—happy analyzing!

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

Success.ai’s Startup Data for Global Tech Startups offers a comprehensive and reliable dataset tailored for businesses, investors, and organizations seeking to connect with tech startups worldwide. Covering emerging companies in software, AI, fintech, health tech, and other innovation-driven industries, this dataset provides detailed funding insights, firmographic data, and verified contact details for decision-makers.

With access to continuously updated, AI-validated data from over 700 million global profiles, Success.ai ensures your outreach, partnership development, and investment strategies are powered by accuracy and relevance. Backed by our Best Price Guarantee, this solution is designed to help you thrive in the competitive global startup ecosystem.

Why Choose Success.ai’s Startup Data?

1. Verified Contact Data for Precision Outreach

   • Access verified contact details, including work emails and phone numbers, for startup founders, CEOs, and key decision-makers.
   • AI-driven validation ensures 99% accuracy, reducing errors and improving engagement outcomes.

2. Comprehensive Global Coverage

   • Includes tech startups from major hubs such as Silicon Valley, Europe’s fintech capitals, Asia’s innovation centers, and beyond.
   • Gain insights into startup operations, growth trajectories, and funding patterns across diverse regions.

3. Continuously Updated Datasets

   • Real-time updates capture changes in startup funding rounds, leadership roles, and business expansions.
   • Stay informed on emerging opportunities in the fast-evolving global startup landscape.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access comprehensive startup data for tech companies and professionals worldwide.
• Funding Insights: Understand investment rounds, funding amounts, and venture capital backing for tech startups.
• Firmographic Data: Gain details on company size, industry focus, business locations, and market presence.
• Decision-maker Profiles: Connect with founders, executives, and leadership teams driving innovation in global startups.

Key Features of the Dataset:

1. Comprehensive Startup Profiles

   • Identify and connect with tech startups specializing in AI, SaaS, fintech, health tech, e-commerce, and more.
   • Target decision-makers responsible for product innovation, market expansion, and fundraising strategies.

2. Advanced Filters for Precision Campaigns

   • Filter startups by funding stage (seed, Series A, Series B+), industry focus, or geographic location.
   • Tailor campaigns to align with specific business needs, such as partnership opportunities or technology solutions.

3. Regional and Industry-specific Insights

   • Leverage data on startup trends, funding activity, and market demands to refine strategies.
   • Align outreach efforts with high-growth sectors and regional opportunities in tech innovation.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Investor Relations and Partnership Development

   • Build relationships with startups seeking venture capital, strategic investments, or market partnerships.
   • Foster collaborations that enhance innovation, accelerate growth, or enter new markets.

2. Marketing Campaigns and Outreach

   • Promote technology solutions, consulting services, or operational tools tailored to the unique needs of startups.
   • Use verified contact data for targeted multi-channel outreach, including email, phone, and LinkedIn.

3. Market Research and Competitive Analysis

   • Analyze global startup trends, funding dynamics, and innovation hotspots to refine product development and marketing strategies.
   • Benchmark against competitors to identify high-demand solutions and underserved markets.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in tech startups seeking candidates for engineering, product management, or growth roles.
   • Provide workforce optimization platforms or talent development solutions tailored to startups.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality startup data at competitive prices, ensuring strong ROI for your marketing, sales, and investment initiatives.

2. Seamless Integration

   • Integrate verified startup data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost engagement rates in startup-focused campaigns.

4. Customizabl...

According to the survey carried out among start-up owners, the main reasons why their businesses did not work out was a lack of financing, with nearly **** of the start-ups giving this as the main reason for their business failure. Moreover, the COVID-19 pandemic played a role in one third of business failures. There is rarely one reason behind a company going bankrupt, it is rather a mixture of several issues, as reflected in the many reasons stated by the respondents.

This dataset includes 2,100 entries of student-led entrepreneurship projects sourced from 40 academic institutions between 2019 and 2023. It was developed to aid in predictive modeling of the success rate of college startup initiatives using deep learning and machine learning approaches.

The dataset captures both structural and strategic elements that influence startup outcomes — such as funding, innovation, team dynamics, and support systems like mentorship and incubation. Each project is labeled as either successful (1) or not successful (0) based on a calculated success metric derived from multiple weighted inputs.

This data can be used to train classification models, perform feature analysis, and build intelligent recommendation systems to support innovation incubators, educational policymakers, and student entrepreneurs.

The current dataset primarily captures internal project-specific factors such as team experience, innovation score, funding, mentorship, and incubation support. However, it does not include broader environmental variables, such as macroeconomic indicators (e.g., industry growth rates, regional investment trends) or regional factors (e.g., resource availability in large vs. small cities). These external factors can significantly influence startup success. To enhance the dataset’s robustness, future work can integrate supplementary environmental variables using publicly available data sources, such as regional economic indicators, startup density, proximity to innovation hubs, and local infrastructure quality. Incorporating these variables will enable the predictive model to account for both internal and external determinants of success, thereby improving its accuracy, generalizability, and practical applicability for diverse institutional and regional contexts.

Key Features Feature Name Description project_id Unique identifier for each project institution_name Name of the college or university institution_type Type of institution (Public, Private, Technical, Non-technical) project_domain Startup domain (e.g., HealthTech, EdTech, AgriTech) team_size Number of students in the team avg_team_experience Average prior experience of the team members (in years) innovation_score Normalized score reflecting novelty and originality of the project funding_amount_usd Initial funding received by the project in USD mentorship_support Whether the team received mentorship (1 = Yes, 0 = No) incubation_support Whether the project was incubated (1 = Yes, 0 = No) market_readiness_level Readiness scale from idea (1) to market-ready (5) competition_awards Number of awards won in competitions business_model_score Score representing clarity and scalability of the business model (0 to 1) technology_maturity Maturity level of the tech used (1 = prototype, 5 = production ready) year Year the project was submitted success_label Target variable: 1 = Successful, 0 = Not successful

The paper deals with the potential relationship between higher education and entrepreneurial activities. Universities and other higher education institutions could be seen as boosting entrepreneurship in the region. University graduates could be more often involved in starting up a new business and the university itself could commercialize their innovations by creating academic spin-off companies. The paper aims to examine the potential effect of higher education on the probability of starting a business as well as its further success. Based on the data for 40 EU and non-EU countries, retrieved from a Eurobarometer survey, we conducted probit and IV probit regressions. These have tested the assumed relationship between higher education and entrepreneurial activities. Our results strongly suggest that higher education can often be very beneficial for starting up a new business and this seems to be one of the factors determining the success of new businesses. Furthermore, those respondents who attended courses related to entrepreneurship appear to be more active in starting-up a business and this seems to be also positively correlated with the company's future success. Interestingly, university graduates from Brazil, Portugal and India in particular, tend to appreciate the role that their universities have played in acquiring the skills to enable them to run a business.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

Find detailed Small Business Contact Data and company information for small businesses globally with Success.ai. Includes firmographic data, employee counts, and decision-maker profiles. Best price guaranteed.

Historical Dataset of H S For Business And Economic Success is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2010-2023),Distribution of Students By Grade Trends,Hispanic Student Percentage Comparison Over Years (2010-2023),Black Student Percentage Comparison Over Years (2010-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2010-2023),Free Lunch Eligibility Comparison Over Years (2010-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2010-2023)

In 2020, approximately **** percent of newly established businesses in South Korea failed to continue operations after five years. This was highest among arts, sports and recreation-related services as well as accommodation and food services. The 5-year survival rate stood at **** percent, which was notably lower than the OECD average of **** percent.

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.

As of June 2024, around ** percent of marketing professionals surveyed worldwide rated their data-driven strategies somewhat successful. Approximately ** percent considered them very successful, and **** percent as unsuccessful. According to the same study, targeting segmented audiences and real-time decision-making were among the top challenges for executing a data-driven strategy.

Access 28M verified Company Data profiles with Company Funding Data and complete business location data, including small business contact data. Our data provides real-time, AI-validated, and compliant datasets with global coverage, including company funding information - Best Price Guaranteed.

1. Introduction The growth of startups across the globe has brought immense innovation, but also a high rate of failure. Understanding the factors that contribute to the survival or failure of startups is critical for investors, entrepreneurs, and policymakers. This project focuses on analyzing startup failure data using machine learning techniques to predict whether a startup will survive or not, based on historical and operational attributes. The dataset used in this analysis is titled "Startup Failures", which provides information on startups including their founding year, end year (if applicable), total lifespan, and operational status (survived or failed). The primary objective of this study is to apply machine learning models to classify startups into survival categories and to compare the performance of different algorithms based on their accuracy and practical utility. Three models are employed for this task: Decision Tree, Random Forest, and KMeans Clustering. The first two are supervised classification models, while the third is an unsupervised learning method. The performance of these models is evaluated using appropriate metrics such as accuracy for the supervised models and Adjusted Rand Index for clustering.

2. Dataset Description The dataset used in this project contains information on 815 businesses, capturing important details related to their sector and operational history. It provides a mix of categorical and numerical attributes that allow for exploratory data analysis and model training. Key Features: • Name: The name of the startup or business (used for identification only) • Sector: The industry or sector to which the business belongs (e.g., FinTech, E-Commerce, Education) • Operation Data: Status of the business (e.g., “Operating”, “Closed”) • Years of Operation: The total duration for which the business has been active • Start Year: The year the business was established • End Year: The year the business ceased operations (if applicable) • Lifespan: The overall time span of the business from start to end This raw dataset was directly used to apply both supervised learning models (Decision Tree and Random Forest) and an unsupervised learning model (KMeans) to identify potential patterns in startup longevity and failure. No additional variables or feature engineering was performed, keeping the dataset close to its original form. Column description: • Name: The name of the startup or business (used for identification only) • Sector: The industry or sector to which the business belongs (e.g., FinTech, E-Commerce, Education) • Operation Data: Status of the business (e.g., “Operating”, “Closed”) • Years of Operation: The total duration for which the business has been active • Start Year: The year the business was established • End Year: The year the business ceased operations (if applicable) • Lifespan: The overall time span of the business from start to end

3. Model Building In this study, three machine learning models were developed to analyze startup survival outcomes: Decision Tree, Random Forest, and KMeans Clustering. The first two are supervised classification models, while the third is an unsupervised clustering method. Model Summary: Decision Tree The Decision Tree classifier was trained on labeled data using startup attributes to predict whether a business survived or failed. This model splits the data based on feature values, building a tree-like structure that is easy to interpret. • Training Method: Supervised • Input Features: Years of Operation, Start Year, End Year, Lifespan • Target Variable: Survival Category • Performance Metric: Accuracy • Train-Test Split: 70:30

This model predicts the Survival Category (Short, Medium, Long) based on input features (e.g., Years.of.Operation). Tree Structure: • The root node splits on Years.of.Operation < 7. • If yes:  Further splits at Years.of.Operation < 4:  If yes → Predicts Short (pure class).  If no → Predicts Medium (pure class). • If no → Predicts Long (pure class). This tree shows a very clean separation of classes based on Years.of.Operation. Confusion Matrix & Statistics:

Accuracy: 100% Kappa: 1.0 → Perfect agreement between prediction and actual. Sensitivity, Specificity, PPV, NPV, Balanced Accuracy: All 1.0 for every class. P-Value [Acc > NIR] < 2.2e-16: Strong statistical evidence that the model performs better than random guessing. Interpretation: • The Decision Tree performed exceptionally well, classifying all test samples perfectly. • Feature Years.of.Operation is highly predictive of survival category. • This model is easy to interpret, visually intuitive, and practically effective. Practical Utility: • Can be used in decision-making systems where understanding the operational duration helps in predicting survival outcomes. • Ideal for business or policy applications needing transparent model...