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.

API Features:

• Real-Time Data Access: Our APIs ensure you can integrate and access the latest company data directly into your systems, providing real-time updates and seamless data flow.
• Scalable Integration: Designed to handle high-volume requests efficiently, our APIs can support extensive data operations, perfect for businesses of all sizes.
• Customizable Data Retrieval: Tailor your data queries to match specific needs, selecting data points that align with your business goals for more targeted insights.

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.
• API Access: Our robust APIs and customizable data solutions provide the flexibility and scalability needed to adapt to changing market conditions and business needs.
• 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...

[THIS DATASET HAS BEEN WITHDRAWN]. 5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [1]. SPI is calculated for different accumulation periods: 1, 3, 6, 9, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. This release supersedes the previous version, doi:10.5285/ed7444fc-8c2a-473e-98cd-e68d3cffa2b0, as it addresses localised issues with the source data (Met Office monthly rainfall grids) for the period 1960 to 2000. NOTE: the difference between this dataset with the previously published dataset 'Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]" (Tanguy et al., 2015 [2]), apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Keller et al., 2015 [3], Tanguy et al., 2014 [4]) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. [2] Tanguy, M.; Hannaford, J.; Barker, L.; Svensson, C.; Kral, F.; Fry, M. (2015). Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]. NERC Environmental Information Data Centre. https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0 [3] Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J.A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., & Dixon, H. (2015). CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use. Copernicus GmbH. https://doi.org/10.5194/essdd-8-83-2015 [4] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2014). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2012) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e Full details about this dataset can be found at https://doi.org/10.5285/1b228b42-42f8-4aee-b964-2c92a21d5556

The statistic shows the growth rate in the real GDP in the United Kingdom from 2019 to 2023, with projections up until 2029. In 2023, the rate of GDP growth in the United Kingdom was at around 0.34 percent compared to the previous year.The economy of the United KingdomGDP is used an indicator as to the shape of a national economy. It is one of the most regularly called upon measurements regarding the economic fitness of a country. GDP is the total market value of all final goods and services that have been produced in a country within a given period of time, usually a year. Inflation adjusted real GDP figures serve as an even more telling indication of a country’s economic state in that they act as a more reliable and clear tool as to a nation’s economic health. The gross domestic product (GDP) growth rate in the United Kingdom has started to level in recent years after taking a huge body blow in the financial collapse of 2008. The UK managed to rise from the state of dark desperation it was in between 2009 and 2010, from -3.97 to 1.8 percent. The country suffered acutely from the collapse of the banking industry, raising a number of questions within the UK with regards to the country’s heavy reliance on revenues coming from London's financial sector, arguably the most important in the world and one of the globe’s financial command centers. Since the collapse of the post-war consensus and the rise of Thatcherism, the United Kingdom has been swept along in a wave of individualism - collective ideals have been abandoned and the mass privatisation of the heavy industries was unveiled - opening them up to market competition and shifting the economic focus to that of service.The Big Bang policy, one of the cornerstones of the Thatcher government programs of reform, involved mass and sudden deregulation of financial markets. This led to huge changes in the way the financial markets in London work, and saw the many old firms being absorbed by big banks. This, one could argue, strengthened the UK financial sector greatly and while frivolous and dangerous practices brought the sector into great disrepute, the city of London alone brings in around one fifth of the countries national income making it a very prominent contributor to wealth in the UK.

During the Second World War, the three Axis powers of Germany, Italy, and Finland mobilized the largest share of their male population. For the Allies, the Soviet Union mobilized the largest share of men, as well as the largest total army of any country, but it was restricted in its ability to mobilize more due to the impact this would have on its economy. Other notable statistics come from the British Empire, where a larger share of men were drafted from Dominions than from the metropole, and there is also a discrepancy between the share of the black and white populations from South Africa.

However, it should be noted that there were many external factors from the war that influenced these figures. For example, gender ratios among the adult populations of many European countries was already skewed due to previous conflicts of the 20th century (namely WWI and the Russian Revolution), whereas the share of the male population eligible to fight in many Asian and African countries was lower than more demographically developed societies, as high child mortality rates meant that the average age of the population was much lower.

Economic development is interregional in nature, with economic growth being determined by physical and technological proximity identified by interregional and national cross-border interactions in trade, investments, and knowledge. This report explains the construction of a system of multiregional input-output tables for the EU28 interlinked with trade in goods and services within the same country as well as with regions in other Member States. Taking transhipment locations into account, trade in goods and services is derived from freight transport data, airline data on flights, and business travel data. The methodology is centred on the probability of trade flows and was developed to fit the information available without pre-imposing any geographical structure on the data.

The Economic Impacts of Brexit on the UK, its Sectors, its Cities and its Regions What are the economic impacts of Brexit on the UK's sectors, regions and cities? The findings from our recent research suggest that the UK's cities and regions which voted for Brexit are also the most economically dependent on EU markets for their prosperity and viability. This is a result of their differing sectoral and trade composition. Different impacts are likely for different sectors, and also different impacts are likely between sectors, and these relationships also differ across the country's regions. Some sectors, some regions and some cities will be more sensitive and susceptible to any changes in UK-EU trade relations which may arise from Brexit than others and their long-run competiveness positions will be less robust and more vulnerable than others. This suggests that these sectoral and regional differences need to be very carefully taken into account in the context of the national UK-EU negotiations in order for the post-Brexit agreements to be politically, socially as well as economically sustainable across the country. This project aims to examine in detail the likely impacts of Brexit on the UK's sectors, regions and cities by using the most detailed regional-national-international trade and competition datasets currently available anywhere in the world (and the people who built these data). These two datasets, are the 2016 WIOD World Input-Output Database and the 2016 UK Interregional Trade Datasets developed respectively by the University of Groningen and by the PBL Netherlands Environmental Assessment Agency. WIOD covers 43 countries, 56 sectors and 15 years of trade-GDP-demand relationships, while the EU Interregional Tables covers 59 sectors and 240 EU regions. The quantitative research will allow us to understand the role in shaping UK regional trade behaviour which is played by global value-chains, whereby goods and services crisscross borders multiple times before being finally consumed by household and firms. The UK is heavily integrated with the rest of the EU via such global value-chains and reshaping the future post-Brexit UK trade arrangements with the EU will also involve reconfiguring these global value-chains. Our data allows us to examine the impacts of different trade scenarios and to map out the sensitivity of UK sectors and regions to different post-Brexit scenarios. Brexit will also reshape the national and international competiveness rankings of the UK regions and again our data allows us to examine the likely long run changes which will arise. At the same time, these changes will also all have profound implications for the design and governance of UK city and regional development policy logic and settings. However, the withdrawal of EU Cohesion Funds, alongside changing UK-EU trade relationships means that both the economic and the public policy environment facing local regions will shift significantly. The ongoing UK devolution agenda at the level of both the three devolved national administrations as well as the English city-regions will be heavily affected by the changing external environment and our project will identify the governance, policy and institutional options which key stakeholders perceive to offer the greatest possibilities for adjusting to the new realities. Our quantitative research will therefore also be undertaken in parallel with qualitative research based on key stakeholder engagement sessions. Participatory workshops with city, regional and national stakeholders will be organised in order to develop alternative post-Brexit scenarios for empirical analysis as perceived by the city and regional as well as national institutions. The mix of quantitative and qualitative approaches will allow us to identity the impacts of Brexit at the crucial meso-levels of the individual sectors, the individual cities and the individual regions.

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides.

The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom.

Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature.

The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in...

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information