Success.ai’s B2B Contact Data for Human Resources Professionals Worldwide empowers businesses to connect with HR leaders across the globe. With access to over 170 million verified professional profiles, this dataset includes critical contact information for key HR decision-makers in various industries. Whether you’re targeting HR directors, talent acquisition specialists, or employee relations managers, Success.ai ensures accurate and effective outreach.
Why Choose Success.ai’s HR Professionals Data?
Data accuracy is backed by AI validation to ensure 99% reliability.
Global Reach Across HR Functions:
Includes profiles of HR directors, recruiters, payroll specialists, and training managers.
Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.
Continuously Updated Datasets:
Real-time updates provide the latest information about HR professionals in decision-making roles.
Ethical and Compliant:
Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.
Data Highlights: - 170M+ Verified Professional Profiles: Includes HR professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.
Key Features of the Dataset:
Strategic Use Cases:
Build relationships with professionals managing recruitment, payroll, or employee engagement.
Corporate Training and Development:
Reach training managers to promote learning solutions, workshops, and skill-building programs.
Showcase personalized employee development initiatives.
Targeted Marketing Campaigns:
Design campaigns to promote HR-focused tools, resources, or consultancy services.
Leverage verified contact data for higher engagement and conversions.
HR Tech Solutions:
Present HR software, automation tools, or cloud solutions to relevant decision-makers.
Target professionals managing HR digital transformation.
Why Choose Success.ai?
APIs for Enhanced Functionality
Leverage B2B Contact Data for Human Resources Professionals Worldwide to connect with HR leaders and decision-makers in your target market. Success.ai offers verified work emails, phone numbers, and continuously updated profiles to ensure effective outreach and impactful communication.
With AI-validated accuracy and a Best Price Guarantee, Success.ai provides the ultimate solution for accessing and engaging global HR professionals. Contact us now to elevate your business strategy with precise and reliable data!
No one beats us on price. Period.
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United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Men was 86.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Men reached a record high of 100.00000 in January of 2009 and a record low of 49.00000 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on June of 2025.
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United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over was 299.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over reached a record high of 324.00000 in January of 2023 and a record low of 207.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.
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Graph and download economic data for Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over (LEU0254472400A) from 2000 to 2024 about human resources, management, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.
In 2024, the top way in which HR departments used artificial intelligence (AI) in supporting performance management was to assist managers in providing more comprehensive or actionable feedback to their employees. Almost ** percent of HR professionals said this was the case within their department. Bottom of the list was to identify potential areas of bias within performance evaluations, where only **** percent of respondents gave this as their answer.
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United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Women was 213.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Women reached a record high of 245.00000 in January of 2023 and a record low of 137.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.
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Graph and download economic data for Employed full time: Wage and salary workers: Human resources managers occupations: 16 years and over: Women (LEU0254686000A) from 2000 to 2024 about human resources, management, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.
This dataset captures the quarterly investment holdings of institutional investment managers and maps the ownership structure of public firms. These Schedule 13F reports are submitted to the Securities and Exchange Commission quarterly by all institutional investment managers with at least $100 million in assets under management. Most academic research examining the common ownership of corporations and the portfolio holdings of large investment managers is based on proprietary commercial databases. This hinders the replication of prior work due to unequal access to these subscriptions and because the data manipulation steps in commercial databases are often opaque. To overcome these limitations, the presented dataset is created from the original regulatory filings; it is updated daily and includes all information reported by investment managers without alteration. Daily updates: https://dx.doi.org/10.34740/kaggle/ds/2973565
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Abstract This article presents the perception of human resource (HR) top managers at strategic level in Brazilian corporations regarding labor union activity. It is a quantitative study about the perception of 354 experienced HR top managers on the unionization of employees, influence of the union on the organization, recognition of the union for labor negotiations and the existence of an advisory committee (workers’ council) within organizations. The theoretical approach addresses both the fields of study on HR and Industrial Relations (IR). The research shows low unionization and management perceptions that unions have little influence on organizations. On the other hand, the HR managers recognize the union influence for collective bargaining purposes and for defining general terms of employment. These contradictions are related to the fact that collective bargaining is mandatory in Brazilian legislation. In general terms of employment, Brazilian legislation is too rigid, prescriptive and detailed. This strong state of regulation reserves to the union an automatic participation in the negotiation process with employers, although this trade union action is somehow inefficient. In this scenario, the perception of HR managers of a relative union influence, although inefficient, is an expected consequence.
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The HHD survey assesses the role of organizations and line managers in ethnic discrimination in hiring in the Netherlands. The data include, for both line managers and employees, attitudes towards HR and diversity policies, as well as several measures of outgroup attitudes, attitudes toward discrimination and their support for various anti-discrimination policies in hiring to the organizational circumstances under which hiring decisions are made. Furthermore, the survey contains information on the hiring, diversity and HR polices in the organization where the respondent is employed.
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United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over: Men was 2284.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over: Men reached a record high of 2284.00000 in January of 2024 and a record low of 1028.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on June of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over was 1869.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over reached a record high of 1869.00000 in January of 2024 and a record low of 891.00000 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over - last updated from the United States Federal Reserve on June of 2025.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over: Men (LEU0254632600A) from 2000 to 2024 about human resources, management, second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.
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Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Human resources managers occupations: 16 years and over (LEU0254525800A) from 2000 to 2024 about human resources, management, second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.
The HMPPS workforce bulletin provides statistics on staffing levels and staff inflows and outflows in England and Wales. This publication updates statistics on HMPPS staffing levels up to the end of December 2021.
The prison officer and operational support grade (OSGs) recruitment diversity annex provides diversity statistics (ethnicity, disability and gender) at each stage of the recruitment process for Prison Officers and OSGs.
The probation officer recruitment annex provides the difference between required and current staffing levels of Probation Officers, and the number of Trainee Probation Officers and other qualified staff.
This statistics release provides data on COVID-19 amongst staff in HMPPS in England and Wales – on deaths, cases and absence.
The bulletin is released by the Ministry of Justice and produced in accordance with arrangements approved by the UK Statistics Authority.
Pre-release list
HMPPS workforce bulletin is produced and handled by the Ministry of Justice’s (MOJ) analytical professionals and production staff. Pre-release access of up to 24 hours is granted to the following persons:
Ministry of Justice
Lord Chancellor and Secretary of State; Minister of State for Justice; Parliamentary Under Secretary of State; Permanent Secretary; Director, Prison Reform Policy; Director General, Justice Analysis & Offender Policy; Director of Prison Safety & Reform Programme; Director of Analytical Services; Chief Statistician and Deputy Director Justice Statistics Analytical Services; Deputy Director, Head of Data Science & HR Analytical Services; Head of HR Analysis, Reporting and Modelling; Deputy Director, Prison Reform Policy; Deputy Director, Prison and Probation Analytical Services; Deputy Director - Human Resources, Head of Data and Insight; Director of Communications; Prison Officer Recruitment – Head of Data and Insight; Press officers (x9); Private secretaries (x8); Special advisors (x2)
HM Prison and Probation Service (HMPPS)
Chief Executive Officer; Head of CEO’s Office; Head of Executive Management Team; HR Director; Head of HR Reform; Deputy Director of HR Prisons; Deputy Director of HR Probation; Deputy Director of Recruitment and Retention.
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The COVID-19 pandemic has significantly affected the global workforce, presenting unprecedented challenges to managers and practitioners of strategic human resource management. Pandemic-influenced changes in the employment relationship highlighting the need for adaptation in order to facilitate a return to pre-pandemic conditions. Crises such as this can have a detrimental effect on employees’ psychological contract, which in turn can hinder the organization’s ability to thrive in the post-COVID-19 era and impede the development of high commitment levels in the aftermath of the crisis. Emotional intelligence plays an increasingly vital role in effectively navigating the crisis and providing support to employees, while also facilitating the reconstruction of the psychological contract. Therefore, this study aims to explain the role of emotional intelligence of strategic human resource management practitioners on affective organizational commitment and the possible mediating effect of the psychological contract in that relationship. A quantitative study took place in February 2023 among 286 HR directors, HR managers, and HR officers in higher education institutions in Georgia. Partial Least Squares for Structural Equation Modelling was applied for data analysis. The results revealed that the emotional intelligence of strategic human resource management practitioners has a positive impact on the psychological contract and the affective organizational commitment. This study supports the idea that emotional intelligence can transform strategic human resource management practitioners into individuals who engage in people-orientated activities. These activities aim to effectively acquire, utilize, and retain employees within an organization. The study also suggests that emotional intelligence can provide solutions to maintain high employee commitment during times of crisis and in the aftermath of unprecedented situations.
The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.
National
Schools, teachers, students, public officials
Sample survey data [ssd]
The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location.
For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions.
For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.
Overall, we draw a sample of 300 public schools from each of the regions of Ethiopia. As a comparison to the total number of schools in Ethiopia, this consistutes an approximately 1% sample. Because of the large size of the country, and because there can be very large distances between Woredas within the same region, we chose a cluster sampling approach. In this approach, 100 Woredas were chosen with probability proportional to 4th grade size. Then within each Woreda two rural and one urban school were chosen with probability proportional to 4th grade size.
Because of conflict in the Tigray region, an initial set of 12 schools that were selected had to be trimmed to 6 schools in Tigray. These six schools were then distributed to other regions in Ethiopia.
Computer Assisted Personal Interview [capi]
The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.
More information pertaining to each of the three instruments can be found below:
School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.
Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey questions.
Survey of Public Officials: The Survey of Public Officials collects information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey is a streamlined and education-focused version of the civil-servant surveys that the Bureaucracy Lab (a joint initiative of the Governance Global Practice and the Development Impact Evaluation unit of the World Bank) has implemented in several countries. The survey includes questions about technical and leadership skills, work environment, stakeholder engagement, impartial decision-making, and attitudes and behaviors. The survey takes 30-45 minutes per public official and is used to interview Ministry of Education officials working at the central, regional, and district levels in each country.
The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level.
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This study employed a qualitative method to achieve the research objectives. This study included 20 semi-structured interviews with 20 individual HR managers and operational (non-HR) managers/supervisors from four organisations in the IT industry in Australia. Primary data for this study were collected through interviews with participants. This research also included secondary data came from policy documents on workforce analytics and remote working.
The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.
National
Schools, teachers, students, public officials
Sample survey data [ssd]
The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.
The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.
EGRA Details:
"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.
To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)
The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.
As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.
In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."
Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.
The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.
Computer Assisted Personal Interview [capi]
The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.
More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.
Success.ai’s B2B Contact Data for Human Resources Professionals Worldwide empowers businesses to connect with HR leaders across the globe. With access to over 170 million verified professional profiles, this dataset includes critical contact information for key HR decision-makers in various industries. Whether you’re targeting HR directors, talent acquisition specialists, or employee relations managers, Success.ai ensures accurate and effective outreach.
Why Choose Success.ai’s HR Professionals Data?
Data accuracy is backed by AI validation to ensure 99% reliability.
Global Reach Across HR Functions:
Includes profiles of HR directors, recruiters, payroll specialists, and training managers.
Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.
Continuously Updated Datasets:
Real-time updates provide the latest information about HR professionals in decision-making roles.
Ethical and Compliant:
Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.
Data Highlights: - 170M+ Verified Professional Profiles: Includes HR professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.
Key Features of the Dataset:
Strategic Use Cases:
Build relationships with professionals managing recruitment, payroll, or employee engagement.
Corporate Training and Development:
Reach training managers to promote learning solutions, workshops, and skill-building programs.
Showcase personalized employee development initiatives.
Targeted Marketing Campaigns:
Design campaigns to promote HR-focused tools, resources, or consultancy services.
Leverage verified contact data for higher engagement and conversions.
HR Tech Solutions:
Present HR software, automation tools, or cloud solutions to relevant decision-makers.
Target professionals managing HR digital transformation.
Why Choose Success.ai?
APIs for Enhanced Functionality
Leverage B2B Contact Data for Human Resources Professionals Worldwide to connect with HR leaders and decision-makers in your target market. Success.ai offers verified work emails, phone numbers, and continuously updated profiles to ensure effective outreach and impactful communication.
With AI-validated accuracy and a Best Price Guarantee, Success.ai provides the ultimate solution for accessing and engaging global HR professionals. Contact us now to elevate your business strategy with precise and reliable data!
No one beats us on price. Period.