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.

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

Brazil Formal Employment: Turnover Rate: Metropolitan: São Paulo data was reported at 4.390 % in Apr 2019. This records an increase from the previous number of 4.150 % for Mar 2019. Brazil Formal Employment: Turnover Rate: Metropolitan: São Paulo data is updated monthly, averaging 3.630 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 4.410 % in Mar 2016 and a record low of 2.010 % in Dec 2003. Brazil Formal Employment: Turnover Rate: Metropolitan: São Paulo 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.GBB093: Formal Employment: Turnover Rate: by Region and State.

Voluntary employee turnover in business service centers in Poland in 2024 was nearly *** percent. The highest turnover was recorded in 2022.

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.

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.

Employee Attrition and Factors

Examining Performance, Financials, and Job Role for Impact on Retention

By [source]

About this dataset

This dataset offers a comprehensive and varied analysis of an organization's employees, focusing on areas such as employee attrition, personal and job-related factors, and financials. Included are numerous parameters such as Age, Gender, Marital Status, Business Travel Frequency, Daily Rate of Pay, Departmental Information such as Distance From Home Office or Education Level Obtained by the employee in question. Also included is a variant series of parameters related to the job being performed such as Job Involvement (level), Job Level (relative to similar roles within the same organization), Job Role specifically meant for that individual(function/task), total working hours in a week/month/year be it overtime or standard hours for a given role. Furthermore detailed aspects include Percent Salary Hike during their tenure with the company from promotion or otherwise , Performance Rating based on specific criteria established by leadership , Relationship Satisfaction among peers at workplace but also taking into account outside family members that can influence stress levels in varying capacities ,Monthly Income considered at its starting point once hired then compared against their monthly payrate with overtime hours included if applicable along with Number Companies Worked before if any. Lastly the Retirement Status commonly known as Attrition is highlighted; covering whether there was an intent to stay with one employer through retirement age or if attrition took place for reasons beyond ones control earlier than expected . Through this dataset you can get an insight into various major aspect regarding today's workforce management philosphies which have changed drastically over time due to advancements in technology

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How to use the dataset

• Understand the variables that make up this dataset. The dataset includes several personal and job-related variables such as Age, Gender, Marital Status, Business Travel, Daily Rate, Department, Distance From Home, Education, Education Field, Employee Count, Employee Number, Environment Satisfaction Hoursly Rate and so on. Knowing what each variable is individuallly will help when exploring employee attrition as a whole.
• Analyze the data for patterns as well as outliers or anomalies either at an individual level or across all of the data points together. Identifying these patterns or discrepancies can offer insight into factors that are related to employee attrition.
• Visualize the data using charts and graphs to allow for easy understanding of which relationships might be causing higher levels of employees leaving the organization over time dimensions like age or job role can be key factors in employee attrition rates visually displaying how they relate to one another can provide clarity into what needs to change within an organization in order to reduce attrition rates
• Explore relationships between pairs of variables through correlation analysis correlations are measures of how strongly two variables are related when looking at employment retention it’s important to analyze correlations at both an individual level and for all variables together showing which pairings have more influence than others when it comes to influencing employee decisions
  5 Use descriptive analytics methods such as scatter plots histograms boxplots etc with aggregated values from each field like average age average monthly income etc These analytics help gain a deeper understanding about where changes need to be made internally
  6 Utilize predictive analytics with more advanced techniques such as regressions clustering decision trees in order identify trendsfrom past data points then build models on those insights from different perspectives helping further prepare organizations against potential high levelsinvolving employees departing ?

Research Ideas

• Identifying performance profiles of employees at risk for attrition through predictive analytics and using this insight to create personalized development plans or retention strategies.
• Using the data to assess the impact of different financial incentives or variations in job role/structure on employee attitudes, satisfaction and ultimately attrition rates.
• Analyzing different age groups' responses to various perks or turnover patterns in order to understand how organizations can better engage different demographic segments

Acknowledgements

If you use this dataset in your research, pl...

Turnover data by fiscal year for the City of Tempe compared to the seven market cities, which include Chandler, Gilbert, Glendale, Mesa, Phoenix, Peoria, and Scottsdale. There are two totals, one with and one without retirees.Please note that the Valley Benchmark Cities’ annual average is unavailable for FY 2020/2021 due to a gap in data collection during that year. Please note that corrections were made to the data, including historic data, due to additional review and research on the data on 10/2/2024.This page provides data for the Employee Turnover performance measure.The performance measure dashboard is available at 5.07 Employee Turnover.Data DictionaryAdditional InformationSource: Department ReportsContact: Lawrence La VictoireContact E-Mail: lawrence_lavictoire@tempe.govData Source Type: ExcelPreparation Method: Extracted from PeopleSoft, and requested data from other cities is entered manually into a spreadsheet, and calculations are conducted to determine the percent of turnover per fiscal yearPublish Frequency: AnnuallyPublish Method: Manual

A time series of staff turnover rates, broken down by provider type. Staff turnover rates are the number of staff who left employment during the period expressed as a percentage of the total number of staff employed at the start of the period.

Turnover among U.S. childcare workers was about 65% higher than turnover in the median occupation in 2022, which creates challenges for the broader workforce, according to a new report from the Federal Reserve Bank of Cleveland.

The Synthetic Employee Attrition Dataset is a simulated dataset designed for the analysis and prediction of employee attrition. It contains detailed information about various aspects of an employee's profile, including demographics, job-related features, and personal circumstances.

The dataset comprises 74,498 samples, split into training and testing sets to facilitate model development and evaluation. Each record includes a unique Employee ID and features that influence employee attrition. The goal is to understand the factors contributing to attrition and develop predictive models to identify at-risk employees.

This dataset is ideal for HR analytics, machine learning model development, and demonstrating advanced data analysis techniques. It provides a comprehensive and realistic view of the factors affecting employee retention, making it a valuable resource for researchers and practitioners in the field of human resources and organizational development.

FEATURES:

Employee ID: A unique identifier assigned to each employee. Age: The age of the employee, ranging from 18 to 60 years. Gender: The gender of the employee Years at Company: The number of years the employee has been working at the company. Monthly Income: The monthly salary of the employee, in dollars. Job Role: The department or role the employee works in, encoded into categories such as Finance, Healthcare, Technology, Education, and Media. Work-Life Balance: The employee's perceived balance between work and personal life, (Poor, Below Average, Good, Excellent) Job Satisfaction: The employee's satisfaction with their job: (Very Low, Low, Medium, High) Performance Rating: The employee's performance rating: (Low, Below Average, Average, High) Number of Promotions: The total number of promotions the employee has received. Distance from Home: The distance between the employee's home and workplace, in miles. Education Level: The highest education level attained by the employee: (High School, Associate Degree, Bachelor’s Degree, Master’s Degree, PhD) Marital Status: The marital status of the employee: (Divorced, Married, Single) Job Level: The job level of the employee: (Entry, Mid, Senior) Company Size: The size of the company the employee works for: (Small,Medium,Large) Company Tenure: The total number of years the employee has been working in the industry. Remote Work: Whether the employee works remotely: (Yes or No) Leadership Opportunities: Whether the employee has leadership opportunities: (Yes or No) Innovation Opportunities: Whether the employee has opportunities for innovation: (Yes or No) Company Reputation: The employee's perception of the company's reputation: (Very Poor, Poor,Good, Excellent) Employee Recognition: The level of recognition the employee receives:(Very Low, Low, Medium, High)

Attrition: Whether the employee has left the company, encoded as 0 (stayed) and 1 (Left).

The wholesale and retail trade sector in the United Kingdom had a combined turnover of almost *** trillion British pounds at the start of 2025, more than double that of the manufacturing sector, the sector with the second-highest turnover at more than ****billion pounds.

The Employee Attrition Prediction Dataset contains data for 10,000 employees, designed for predictive modeling and analysis of employee attrition. It includes a variety of demographic, job-related, and performance metrics to help understand the factors contributing to employee turnover.

Key Features:

Employee_ID: Unique identifier for each employee. Age: Age of the employee. Gender: Gender of the employee. Marital_Status: Marital status of the employee (Single, Married, Divorced). Department: Department the employee works in (e.g., HR, IT, Sales, Marketing). Job_Role: Specific role within the department (e.g., Manager, Analyst). Job_Level: Level in the organizational hierarchy. Monthly_Income: Monthly salary of the employee. Hourly_Rate: Rate per hour for hourly employees. Years_at_Company: Number of years the employee has been with the company. Years_in_Current_Role: Number of years the employee has been in their current role. Years_Since_Last_Promotion: Time since the employee’s last promotion. Work_Life_Balance: Rating of work-life balance. Job_Satisfaction: Rating of job satisfaction (1-5 scale). Performance_Rating: Performance rating (1-5 scale). Training_Hours_Last_Year: Number of training hours completed in the past year. Overtime: Whether the employee works overtime (Yes/No). Project_Count: Number of projects managed by the employee. Average_Hours_Worked_Per_Week: Average working hours per week. Absenteeism: Number of days the employee was absent in the past year. Work_Environment_Satisfaction: Rating of work environment satisfaction. Relationship_with_Manager: Rating of the relationship with the manager. Job_Involvement: Rating of job involvement. Distance_From_Home: Distance from home to the workplace (in kilometers). Number_of_Companies_Worked: Total number of companies the employee has worked for. Attrition: The target column (Yes/No) indicating whether the employee left the company.

Annual statistics on the value of turnover from services provided by the UK service economy.

Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Salvador data was reported at 1.600 % in Apr 2019. This records a decrease from the previous number of 2.830 % for Mar 2019. Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Salvador data is updated monthly, averaging 1.860 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 5.990 % in Jan 2016 and a record low of 0.290 % in Dec 2007. Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Salvador 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.GBB094: Formal Employment: Turnover Rate: by Region and State: Agricultural.

Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Rio de Janeiro data was reported at 4.940 % in Apr 2019. This records an increase from the previous number of 4.820 % for Mar 2019. Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Rio de Janeiro data is updated monthly, averaging 3.870 % from Feb 2003 (Median) to Apr 2019, with 195 observations. The data reached an all-time high of 10.430 % in Jun 2016 and a record low of 1.490 % in Dec 2003. Brazil Formal Employment: Turnover Rate: Agricultural: Metropolitan: Rio de Janeiro 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.GBB094: Formal Employment: Turnover Rate: by Region and State: Agricultural.

In 2023, the attrition rate was the highest among employees working in ******************. It was followed by life sciences and consumer products sectors.

Almost 20 percent of learning and development (L&D) professionals in the United States declared retaining their staff for an average of 5 to 10 years, as of 2019. The average staff retention rate for 6 percent of L&D professionals ranged from six months to one year during the same period.

Dataset Description: The dataset contains information about employees from an organization, including their performance, workplace behavior, and other key factors that may influence employee attrition. It is used to predict whether an employee will leave the company based on several relevant attributes. The features in this dataset provide valuable insights into the workforce, allowing for predictive modeling to understand the likelihood of employee turnover Features Explanation:

1. Satisfaction Level: Description: This represents the employee’s self-reported job satisfaction level. Value Range: A float between 0 and 1, where 0 indicates very low satisfaction, and 1 indicates very high satisfaction.

2. Last Performance Rating: Description: The most recent performance evaluation score of the employee. Value Range: A float between 0 and 1, where 0 represents the lowest performance, and 1 represents the highest.

3. Number of Projects: Description: The total number of projects the employee has worked on during their time at the company. Value Range: An integer, with higher numbers indicating more projects handled by the employee.

4. Average Monthly Hours: Description: The average number of hours the employee works in a month. Value Range: A continuous integer value, reflecting work hours per month.

5. Years at Company: Description: The number of years the employee has worked at the company. Value Range: A continuous float, with higher values indicating longer tenure.

6. Had Work Accident: Description:** Indicates whether the employee has had a workplace accident. Value Range: A binary value (0 or 1), where 0 means no accident and 1 means the employee had at least one accident.

7. Promoted in Last 5 Years: Description: Reflects whether the employee received a promotion in the last five years. Value Range:A binary value (0 or 1), where 0 indicates no promotion, and 1 indicates the employee was promoted.

8. Department: Description: The department in which the employee works. Value Range: Encoded as an integer, where different numbers correspond to different departments: 0: Sales 1: Support 2: Technical 3: HR 4: Accounting 5: Management 6: IT 7: Marketing 8: Research and Development (RandD) 9: Product Management

9. Salary: Description: The salary level of the employee. Value Range: Encoded as an integer: 0: Low salary 1: Medium salary 2: High salary

10. Will Left or Not (Target Feature): Description: Indicates whether the employee has left the company. Value Range: A binary value (0 or 1), where 0 means the employee stayed, and 1 means the employee left.

This statistic shows the share of average share of staff turnover among Indian companies, by industries in the fiscal year 2018, based on an online survey across ** sectors. The staff turnover in the retail industry was the highest with about **** percent, while it was the lowest for automotive with close to ***** percent during the survey period.