This statistic depicts the average annual employee turn over rate in the United States in 2016 and 2017, as reported by human resources (HR) professionals. During the 2017 survey, respondents reported an average annual turnover rate of 18 percent.

In 2024, the average staff turnover rate of hospitals in the U.S. stood at **** percent. The percentage of employees leaving hospitals has decreased since the peak of ** percent in 2021. A closer look at turnover reveals that most was among less tenured staff, with the highest rates among certified nursing assistants.

Maintain the state employee turnover rate at or below the annual regional average of surrounding states every year through 2019.

In 2022, the turnover rate for temporary and contract staff was 419 percent, an increase of four percent when compared to the previous year. The turnover rate refers to the percentage of employees in a workforce who leave within a certain time period.

This layer shows figures of quit rates and quit levels by the US, BLS regions, and states. Data is from the Bureau of Labor Statistics (BLS) and was released October and November of 2021. The layer default symbology highlights to September 2021 quit rate in comparison to the national figure of 3.0%.According to the October 2021 News Release by BLS:"The number of quits increased in August to 4.3 million (+242,000). The quits rate increased to a series high of 2.9 percent. Quits increased in accommodation and food services (+157,000); wholesale trade (+26,000); and state and local government education (+25,000). Quits decreased in real estate and rental and leasing (-23,000). The number of quits increased in the South and Midwest regions."In the following November News Release:"In September, quits rates increased in 15 states and decreased in 10 states. The largest increases in quits rates occurred in Hawaii (+3.8 percentage points), Montana (+1.5 points), as well as Nevada and New Hampshire (+1.1 points each). The largest decreases in quits rates occurred in Kentucky (-1.1 percentage points), Iowa (-1.0 point), and South Dakota (-0.7 point). Over the month, the national quits rate increased (+0.1 percentage point)."Quit rates: The quits rate is the number of quits during the entire month as a percent of total employment.Quit levels: Quits are the number of quits during the entire month.State and US figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedRegion figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedThis data was obtained in October and November 2021, and the months of data from BLS are as follows:August 2020September 2020April 2021 (only offered for Regions)May 2021June 2021July 2021August 2021September 2021 (preliminary values)For the full data release, click here.The states (including the District of Columbia) that comprise the regions are: Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and VermontSouth: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West VirginiaMidwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and WisconsinWest: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

According to a survey from 2024, certified nursing assistants (CNA) had a turnover rate of over ** percent, making it the highest among all hospital staff in the United States. The second-highest turnover rates were among patient care technicians (PCT), followed by environmental services staff.

Formal Employment: Turnover Rate: Service: Central West data was reported at 4.490 % in Apr 2019. This records a decrease from the previous number of 4.660 % for Mar 2019. Formal Employment: Turnover Rate: Service: Central West data is updated monthly, averaging 3.610 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 5.090 % in Mar 2016 and a record low of 2.140 % in Dec 2015. Formal Employment: Turnover Rate: Service: Central West data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB100: Formal Employment: Turnover Rate: by Region and State: Service.

Ratio of employees who have been retained by the department against the establishment count.

*This data is no longer being updated. For more information please refer to Workforce statistics at https://www.forgov.qld.gov.au/recruitment-performance-and-career/workforce-planning/workforce-statistics-and-tools/workforce-statistics

Job Openings and Labor Turnover Survey data from the U.S. Bureau of Labor Statistics

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

Project Overview: Predicting Employee Turnover at Sailsfort Motors

Introduction This project aims to analyze the factors contributing to employee turnover at Sailsfort Motors, an automobile company. By leveraging a combination of logistic regression and tree-based models, we will identify key predictors of employee turnover and develop strategies to enhance employee retention.

Objectives

• Predict Turnover: Build models to predict whether an employee will leave the company.
• Identify Key Factors: Determine the most significant factors influencing employee turnover.
• Retention Strategies: Provide actionable insights to improve employee retention.

Data Description The dataset includes the following attributes:

-Satisfaction Level: Employee satisfaction level. -Last Evaluation: Last performance evaluation score. -Number of Projects: Number of projects the employee has worked on. -Average Monthly Hours: Average monthly working hours. -Time Spent at Company: Number of years the employee has been with the company. -Work Accident: Whether the employee has had a work accident (1: Yes, 0: No). -Left: Whether the employee has left the company (1: Yes, 0: No). -Promotion in Last 5 Years: Whether the employee has been promoted in the last five years (1: Yes, 0: No). -Department: Department the employee belongs to. -Salary: Salary level (Low, Medium, High).

Methodology -Data Preprocessing: Clean and preprocess the data to handle missing values, categorical variables, and data normalization. -Exploratory Data Analysis (EDA): Perform EDA to understand the distribution of data and identify patterns and correlations. -Feature Engineering: Create relevant features to enhance model performance.

Model Building: -Logistic Regression: Build a logistic regression model to identify the probability of employee turnover. -Tree-Based Models: Build tree-based models (e.g., Decision Tree, Random Forest) to capture non-linear relationships and interactions between features. -Model Evaluation: Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.

-Insights and Recommendations: Analyze the results to identify key factors leading to employee turnover and provide recommendations to improve retention.

Expected Outcomes -Predictive Models: Accurate models to predict employee turnover. -Key Insights: Identification of the most significant factors contributing to employee turnover. -Retention Strategies: Data-driven recommendations to improve employee satisfaction and retention.

By predicting employee turnover and understanding its driving factors, this project aims to provide valuable insights for Sailsfort Motors to enhance their HR strategies and foster a more stable and satisfied workforce.

By the last business day of April 2025, there were about ******* job separations in the trade, transportation, and utilities industry in the United States. Separations include voluntary quits, involuntary layoffs and discharges, as well as other separations such as retirements. Separations are also referred to as turnover.

Context

These are simulated data based on employee turnover data in a real technology company in India (we refer to this company by a pseudonym, 'TECHCO'). These data can be used to analyze drivers of turnover at TECHCO. The original dataset was analyzed in the paper Machine Learning for Pattern Discovery in Management Research (SSRN version here). This publicly offered dataset is simulated based on the original data for privacy considerations. Along with the accompanying Python Kaggle code and R Kaggle code, this dataset will help readers learn how to implement the ML techniques in the paper. The data and code demonstrate how ML can be useful for discovering nonlinear and interactive patterns between variables that may otherwise have gone unnoticed.

Content

This dataset includes 1,191 entry-level employees that were quasi-randomly deployed to any of TECHCO’s nine geographically dispersed production centers in 2007. The data are structured as a panel with one observation for each month that an individual is employed at the company for up to 40 months. The data include 34,453 observations from 1,191 employees total; The dependent variable, Turnover, indicates whether the employee left or stayed during that time period.

Objectives

The objective in the original paper was to explore patterns in the data that would help us learn more about the drivers of employee turnover. Another objective could be to find the best predictive model to estimate when a specific employee will leave.

In 2024, the average turnover rate for registered nurses that worked in hospitals across the United States stood at **** percent. This was lower than the turnover rate of **** percent in 2022. According to this survey, the percentage of registered nurses (RN) that left hospitals in 2023 ranged from roughly ** percent to nearly ** percent, depending on the discipline. The highest RN turnover was found among Telemetry nurses. On the other hand, RN turnover was the lowest in Pediatrics.

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Workforce Analytics Market Size 2025-2029

The workforce analytics market size is forecast to increase by USD 3.27 billion, at a CAGR of 19.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for efficient workforce management and recruitment. Companies are recognizing the value of leveraging data-driven insights to optimize their workforce, leading to increased adoption of workforce analytics solutions. Another key trend in the market is the growing use of mobile applications for workforce analytics, enabling real-time access to data and analytics from anywhere. However, the market also faces challenges, including the lack of a skilled workforce capable of effectively implementing and utilizing these advanced analytics tools. As the market continues to evolve, companies seeking to capitalize on opportunities and navigate challenges effectively must prioritize investments in workforce analytics solutions and focus on building a skilled workforce to maximize the value of their data.

What will be the Size of the Workforce Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing importance of data-driven decision making in various sectors. Cost optimization, data visualization, and data warehousing are integral components of workforce analytics, enabling organizations to gain valuable insights from their workforce data. Process automation and employee development are also key areas of focus, as they help streamline operations and enhance employee skills. Performance management and organizational network analysis provide valuable insights into employee productivity and team dynamics. ETL processes and risk management ensure data accuracy and security, while recruitment optimization and career pathing facilitate effective talent acquisition and retention. Predictive modeling and sentiment analysis aid in anticipating workforce trends and employee sentiment, respectively. Data security and strategic workforce planning are essential for mitigating risks and ensuring long-term success. Machine learning and natural language processing are advanced technologies that are increasingly being adopted for data analysis and processing. Workforce analytics encompasses a range of applications, from compensation analysis and employee satisfaction to diversity and inclusion and leadership development. These areas are interconnected and evolve continuously, with new technologies and trends shaping the market landscape. The ongoing integration of these applications into comprehensive workforce analytics solutions enables organizations to optimize their workforce and gain a competitive edge.

How is this Workforce Analytics Industry segmented?

The workforce analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userRetailBFSITelecom and ITHealthcareOthersApplicationLarge enterprisesSmall and medium sized enterpriseDeploymentCloudOn-premiseServiceConsulting ServicesSystem IntegrationManaged ServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.In today's dynamic business environment, retail organizations face increasing pressure to optimize their workforce to stay competitive. The retail industry's growth is driven by factors such as changing market economics, rising competition from e-commerce, and evolving customer demands. To meet these challenges, retailers are investing in their workforce, recognizing its crucial role in driving business success. Workforce optimization strategies encompass various approaches, including 360-degree feedback, organizational network analysis, and social network analysis, to enhance employee performance and engagement. Headcount planning, aided by cloud computing, enables retailers to manage their workforce effectively and adapt to seasonal fluctuations. Regression analysis, statistical analysis, and time series analysis help retailers identify trends and make data-driven decisions. Strategic workforce planning, succession planning, and talent acquisition are essential components of a robust workforce strategy. Employee development, cost optimization, data cleaning, and natural language processing are critical for maintaining a skilled and productive workforce. Data mining, ETL processes, data warehousing, and business intelligence provide valuable insights into workforce performance and trends. Retention strategies,

Formal Employment: Turnover Rate: Service: South: Santa Catarina data was reported at 5.600 % in Apr 2019. This records a decrease from the previous number of 6.000 % for Mar 2019. Formal Employment: Turnover Rate: Service: South: Santa Catarina data is updated monthly, averaging 4.460 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 6.440 % in Feb 2016 and a record low of 3.180 % in Dec 2015. Formal Employment: Turnover Rate: Service: South: Santa Catarina data remains active status in CEIC and is reported by Ministry of Labor and Social Security. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBB100: Formal Employment: Turnover Rate: by Region and State: Service.

In September 2024, the hiring rate in the United States was at 3.5 percent for total nonfarm industries. The seasonally adjusted total separations rate was at 3.3 percent. The data are seasonally adjusted. The separations figure includes voluntary quits, involuntary layoffs and discharges, and other separations, including retirements. Total separations is also referred to as turnover.

The employee attrition rate of professional services organizations worldwide ********* overall between 2013 and 2023, despite some fluctuations. During the 2023 survey, respondents reported an average employee attrition rate of **** percent.

In 2023, employee attrition rates decreased in the Americas and EMEA regions, however increased in the ACAP region. The Americas showed a decrease of 1.2 percent, with the ACAP region demonstrating a 3.3 percent increase. Relatively, however, these percentages were some of the best recorded between 2015 and 2023.

Graph and download economic data for Labor Turnover, Total Separation Rate, Manufacturing for United States (M0854BUSM497NNBR) from Jan 1930 to Oct 1968 about separations, labor, manufacturing, rate, and USA.