Interactive Course

Human Resources Analytics in R: Predicting Employee Churn

Predict employee turnover and design retention strategies.

  • 4 hours
  • 14 Videos
  • 50 Exercises
  • 1,943 Participants
  • 4,000 XP

Loved by learners at thousands of top companies:

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Course Description

Organizational growth largely depends on staff retention. Losing employees frequently impacts the morale of the organization and hiring new employees is more expensive than retaining existing ones. Good news is that organizations can increase employee retention using data-driven intervention strategies. This course focuses on data acquisition from multiple HR sources, exploring and deriving new features, building and validating a logistic regression model, and finally, show how to calculate ROI for a potential retention strategy.

  1. 1

    Introduction

    Free

    This chapter begins with a general introduction to employee churn/turnover and reasons for turnover as shared by employees. You will learn how to calculate turnover rate and explore turnover rate across different dimensions. You will also identify talent segments for your analysis and bring together relevant data from multiple HR data sources to derive more useful insights.

  2. Predicting Turnover

    In this chapter, you will build a logistic regression model to predict turnover by taking into account multicollinearity among variables.

  3. Feature Engineering

    In this chapter, you will create new variables from existing data to explain employee turnover. You will analyze compensation data and create compa-ratio to measure pay equity of all employees. To identify the most important variables influencing turnover, you will use the concept of Information Value (IV).

  4. Model Validation, HR Interventions, and ROI

    In this chapter, you will calculate the accuracy of your model and categorize employees into specific risk buckets. You will then formulate an intervention strategy and calculate the ROI for this strategy.

  1. 1

    Introduction

    Free

    This chapter begins with a general introduction to employee churn/turnover and reasons for turnover as shared by employees. You will learn how to calculate turnover rate and explore turnover rate across different dimensions. You will also identify talent segments for your analysis and bring together relevant data from multiple HR data sources to derive more useful insights.

  2. Feature Engineering

    In this chapter, you will create new variables from existing data to explain employee turnover. You will analyze compensation data and create compa-ratio to measure pay equity of all employees. To identify the most important variables influencing turnover, you will use the concept of Information Value (IV).

  3. Predicting Turnover

    In this chapter, you will build a logistic regression model to predict turnover by taking into account multicollinearity among variables.

  4. Model Validation, HR Interventions, and ROI

    In this chapter, you will calculate the accuracy of your model and categorize employees into specific risk buckets. You will then formulate an intervention strategy and calculate the ROI for this strategy.

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Abhishek Trehan
Abhishek Trehan

People Analytics Practitioner

Abhishek is an SHRM Certified Professional, a CAP® (Certified Analytics Professional from INFORMS), and a Six Sigma Black Belt. Has worked and held various roles in fortune 500 companies including setting up HR Analytics and Digital HR COE. He has been a visiting faculty to Indian Institute of Management and is a speaker on HR Analytics.

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Anurag Gupta
Anurag Gupta

People Analytics Practitioner

Anurag holds post graduate degree in Human Resources Management from XLRI, Jamshedpur. He has several years of experience in setting up People Analytics function for global MNCs and believes that for HR to get a seat at the C-suite table, they should incorporate data driven decisions across the employee lifecycle.

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Collaborators
  • Richie Cotton

    Richie Cotton

  • Sumedh Panchadhar

    Sumedh Panchadhar

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