BUSINESS PROBLEM “Attrition in human resources refers to the gradual loss of employees over time. In general, relatively high attrition is problematic for companies. HR professionals often assume a leadership role in designing company compensation programs, work culture and motivation systems that help the organization retain top employees.”

Our role is to uncover the factors that lead to employee attrition through Exploratory Data Analysis, and explore them by using various classification models to predict if an employee is likely to quit. This could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition.

While this model can be routinely run to identify employees, who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee.

HR ANALYTICS Human resource analytics (HR analytics) is an area in the field of analytics that refers to applying analytic processes to the human resource department of an organization in the hope of improving employee performance and therefore getting a better return on investment. HR analytics does not just deal with gathering data on employee efficiency. Instead, it aims to provide insight into each process by gathering data and then using it to make relevant decisions about how to improve these processes.

DATASET This is a hypothetical dataset created by IBM data scientists. The dataset has (23436R X 37C) that contains numeric and categorical data types describing each employee’s background and characteristics; and labelled (supervised learning) with whether they are still in the company or whether they have gone to work somewhere else. Machine Learning models can help to understand and determine how these factors relate to workforce attrition.

Analysis of ‘Employee Turnover’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/davinwijaya/employee-turnover on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About the dataset:

No, it's not about survive from drowning or something like that (just for illustration).

This Employee Turnover dataset is a real dataset shared from Edward Babushkin's blog used to predict an Employee's risk of quitting (with a Survival Analysis Model). Edward Babushkin explained that "Survival Analysis is one of the most importance but it's not the most popular algorithm to predict employee turnover. Analysts use more familiar algorithms like Logistic Regression but, for example, Pasha Roberts writes: 'Don't use logistic methods to predict attrition!'. I think that we can only apply for a short-term situation like whether the employee has worked more or less than three months. If our goal is to predict individual quitting risks, then the best method is Survival Analysis."

Acknowledgements:

All credit goes to Edward Babushkin for sharing this useful dataset.

Inspirations:

This dataset can be used for predicting Employee Churn / Employee Turnover, Employee Survival Analysis, Uplift Modeling, or even Uplift Survival Analysis.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘IBM HR Analytics Employee Attrition & Performance’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/uniabhi/ibm-hr-analytics-employee-attrition-performance on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists.

Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'

WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best'

--- Original source retains full ownership of the source dataset ---

Description:

This project presents a thorough analysis of IBM's HR data to identify and predict factors contributing to employee turnover. By leveraging Exploratory Data Analysis (EDA), feature engineering, and machine learning models, we aim to uncover actionable insights that can inform strategic HR decisions to enhance employee satisfaction and retention. The analysis covers various aspects, including demographic factors, job satisfaction, performance metrics, and departmental influences. Through meticulous data preprocessing, model training, and evaluation, the project delivers robust predictions and strategic recommendations to mitigate employee attrition, thereby fostering a more engaged and productive workforce.

Key Highlights: - Data Exploration: In-depth analysis of employee demographics, job roles, satisfaction levels, and performance metrics. - Feature Engineering: Creation of meaningful features to enhance model performance. - Model Training & Evaluation: Implementation and comparison of Logistic Regression, Random Forest, and XGBoost models, addressing class imbalance with SMOTE and optimizing model performance through hyperparameter tuning. - Insights & Recommendations: Data-driven strategies to reduce employee turnover and improve organizational stability. - Comprehensive Documentation: Detailed methodologies, visualizations, and interpretations to support findings and recommendations.

This project serves as a valuable portfolio piece and a learning resource for colleagues, demonstrating the application of data science techniques to real-world HR challenges.

Turnover data by fiscal year for the City of Tempe compared to the seven market cities which included Chandler, Gilbert, Glendale, Mesa, Phoenix, Peoria and Scottsdale. There are two totals, one with and one without retires.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.Additional 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 percent of turnover per fiscal yearPublish Frequency:AnnuallyPublish Method: ManualData Dictionary

People HR Analytics Software Market Outlook

The global People HR Analytics Software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a robust CAGR of 17.5% during the forecast period. This impressive growth can be attributed to several factors, including the increasing adoption of data-driven decision-making processes within human resource departments, the integration of advanced analytics technologies, and the rising need for efficient workforce management solutions.

Key growth drivers of the People HR Analytics Software market include the escalating demand for data analytics in human resource operations, which enables organizations to effectively manage their workforce and optimize HR outcomes. The adoption of advanced analytics helps organizations to gain deeper insights into employee performance, engagement, and retention, which in turn leads to improved productivity and reduced turnover rates. Additionally, the growing emphasis on employee experience and well-being is compelling organizations to invest in sophisticated HR analytics tools that can provide actionable insights for enhancing employee satisfaction and engagement.

Another significant growth factor is the increasing prevalence of remote and hybrid work models, which has amplified the need for HR analytics solutions that can monitor and manage dispersed workforces. The COVID-19 pandemic has accelerated the adoption of remote working, highlighting the importance of digital tools for workforce management. HR analytics software provides organizations with the capabilities to track employee performance, engagement levels, and productivity, irrespective of their physical location. This shift towards remote working is expected to sustain the demand for HR analytics solutions in the long run.

Moreover, the integration of artificial intelligence (AI) and machine learning (ML) technologies into HR analytics software is driving market growth. These advanced technologies enable predictive analytics, which assists HR professionals in forecasting workforce trends, identifying potential issues, and making proactive decisions. For instance, AI can help in identifying patterns related to employee attrition, allowing organizations to take preemptive actions to retain top talent. Similarly, ML algorithms can analyze large volumes of HR data to uncover insights that can inform talent acquisition, workforce planning, and employee development strategies.

From a regional perspective, North America holds a significant share of the People HR Analytics Software market, driven by the high adoption rate of advanced technologies and the presence of major market players in the region. The region's robust economic environment and emphasis on workforce optimization further contribute to its market dominance. However, Asia Pacific is expected to emerge as the fastest-growing region during the forecast period, fueled by the increasing digital transformation initiatives and the rising adoption of HR analytics solutions by enterprises of all sizes in countries like China, India, and Japan.

Component Analysis

The People HR Analytics Software market is segmented by component into software and services. The software segment dominates the market, driven by the increasing demand for comprehensive HR analytics solutions that offer various functionalities such as talent management, performance tracking, and employee engagement. These software solutions are designed to integrate with existing HR systems, providing a seamless experience for HR professionals to manage and analyze employee data. The continuous advancements in software capabilities, such as the incorporation of AI and ML, are further enhancing the value proposition of HR analytics software.

On the other hand, the services segment, which includes implementation, consulting, and support services, is also witnessing substantial growth. As organizations adopt HR analytics software, there is a growing need for professional services to ensure successful implementation and integration with existing systems. Consulting services are particularly in demand as organizations seek expert guidance on leveraging HR analytics to achieve strategic business objectives. Support services are equally important, providing ongoing assistance to ensure the smooth operation of HR analytics solutions and addressing any technical issues that may arise.

Another critical aspect of the component analysis is the role of cloud-based solutions within the software segment. Cloud-based HR an

Analysis of ‘Employee Attrition Rate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/blurredmachine/hackerearth-employee-attrition on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Content

Employees are the most important part of an organization. Successful employees meet deadlines, make sales, and build the brand through positive customer interactions. Employee attrition is a major cost to an organization and predicting such attritions is the most important requirement of the Human Resources department in many organizations. In this problem, your task is to predict the attrition rate of employees of an organization. The evaluation metric that is used for this problem is the root mean squared error. The formula is as follows: score = 100 * max(0, 1-RMSE(actual, predicted))

Acknowledgements

The data is extracted from an online competition at HackerEarth. HackerEarth holds the complete right of extracting the data and it's features.

--- Original source retains full ownership of the source dataset ---

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?

Does turnover improve performance by allowing firms and employees to optimally match, as outlined by job matching theory? On the other hand, could turnover harm productivity by disrupting team dynamics, as outlined by the Firm Specific Human Capital Model (FSHCM)? I attempt to answer these questions through an analysis of Major League Baseball. For exploring the general relationship between turnover and performance, I regress team turnover rates against their winning percentage using both OLS and quadratic models. For specific theories, I analyze whether positional turnover, inter-league turnover, or the interaction between turnover and ballpark characteristics affect team performance using OLS regression. I attempt to pinpoint precisely how job matching theory and FSHCM could be operating in baseball by analyzing these secondary explanatory variables. I find no evidence to suggest that turnover has a significant effect on team performance over a full season. Rather, roster quality and past winning percentage appear to be better indicators of future winning percentage. However, when looking at the effect of turnover over only half the season, it appears that the best teams from the previous season benefit and the worst teams from the previous season are harmed. I attribute this difference to the ability of better teams to attract better players during the off-season.

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.

1) Data Introduction • 'Employee dataset: Employee data' aggregates comprehensive information encompassing various aspects of employee data such as training, surveys, performance, recruitment, and attendance. This rich dataset is designed to support in-depth analysis and research in human resources.

2) Data Utilization (1) Employee dataset: Employee data has characteristics that: • The dataset includes extensive details on employee demographics, job roles, performance ratings, and other HR-related metrics. • It is an invaluable resource for modeling and predicting employee behavior and outcomes based on historical data. (2) Employee dataset: Employee data can be used to: • Human Resources Management: Assists HR professionals in making informed decisions regarding recruitment, training, and employee retention strategies. • Predictive Analysis: Enables companies to forecast trends in employee turnover and performance, aiding in proactive management and planning.

Source: 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 percent of turnover per fiscal yearPublish Frequency:AnnuallyPublish Method: ManualData Dictionary

Analysis of ‘IBM Employee Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rohitsahoo/employee on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Watson Analytics Sample Data

Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists

Content

This the data about the employee on various factor influencing the attrition from the company. Predict the Attrition of an employee based on the various factor given

Acknowledgements

IBM Blog on employee attrition

Inspiration

Predict the Attrition of an employee based on the various factor given

About Dataset

Education 1. 'Below College' 2. 'College' 3. 'Bachelor' 4. 'Master' 5. 'Doctor'

EnvironmentSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

JobInvolvement 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

JobSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

PerformanceRating 1. 'Low' 2. 'Good' 3. 'Excellent' 4. 'Outstanding'

RelationshipSatisfaction 1. 'Low' 2. 'Medium' 3. 'High' 4. 'Very High'

WorkLifeBalance 1. 'Bad' 2. 'Good' 3. 'Better' 4. 'Best'

--- Original source retains full ownership of the source dataset ---

Snapshot img

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,

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.

Forecast: Turnover Per Employee of Technical Testing and Analysis in Italy 2024 - 2028 Discover more data with ReportLinker!

Forecast: Turnover Per Employee of Technical Testing and Analysis in Germany 2024 - 2028 Discover more data with ReportLinker!

Forecast: Turnover Per Employee of Technical Testing and Analysis in France 2024 - 2028 Discover more data with ReportLinker!

Characteristics of employee turnover in Sri Lankan startups.

Background: Globally, firms face a crucial problem with employee retention since it has a big impact on their sustainability, profitability, and productivity. The goal of this study is to propose management interventions that may be implemented to improve employee retention of medical representatives by assessing the Two-factor variables that affect it using Partial Least Squared Structual Equation Modeling. Methods: As part of the research approach, a thorough analysis of existing literatures and studies on employee retention, including scholarly journals was conducted. In the context of investigated study, the findings indicate that employee retention is significantly and positively influenced by factors such as compensation and benefits and promotion. The mediation effect of Organizational Citizenship Behavior to the relationship of compensation and benefits and employee retention; and promotion and employee retention is found not significant. Findings: The results of the study also showed that Leader-Member Exchange positively and significantly moderates the relationship between coworker relations and employee retention; and working conditions and employee retention. Conclusion: The study concludes that employee retention is a complex problem that needs a comprehensive approach to effectively solve. Although other Two-factor variables did not demonstrate a strong correlation with employee retention, this does not imply that they are not meaningful. It is possible that in the circumstances of the study under investigation, promotions, pay, and benefits are more strongly associated with employee retention. To learn more about this connection, future research may examine what constitutes high-quality LMX and how organizations might promote a favorable culture between leaders and employees. Novelty/Originality of this Study: This study contributes to understanding the specific factors influencing employee retention in the context of medical representatives, with an emphasis on the moderating role of Leader-Member Exchange and its effect on coworker relations and working conditions.