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.

What is turnover?

Objectives of employee turnover prediction

Importing headcount and turnover data

Exploring the data

What proportion of employees have left?

Which levels have high turnover rate?

Is turnover rate different across locations?

HR data architecture

Filtering the dataset

Combining HR datasets (I)

Combining HR datasets (II)

Master data overview

Introduction

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).

Feature engineering

Derive age difference

Derive job hop index

Derive employee tenure

Compensation

Exploring compensation

Pay Gap

Deriving Compa-ratio

Deriving Compa-level

Information value

Calculating Information Value

Which variables are important?

Feature Engineering

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

Pemecahan data

Membagi data

Mengonfirmasi pemisahan data

Pengantar logistic regression

Bangun model regresi logistik pertama Anda

Bangun model regresi logistik berganda

Menafsirkan tingkat signifikansi

Multikolinieritas

Mendeteksi multikolinearitas

Menangani multikolinearitas

Membangun model final Anda

Membangun model regresi logistik final

Memahami prediksi model

Menafsirkan hasil

Predicting Turnover

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.

Validating logistic regression results

Create a confusion matrix

Accuracy of your model

Designing retention strategy

Calculate turnover risk probability

Creating turnover risk buckets

What would you do?

Return on investment

Create salary hike range

Calculate turnover rate across salary hike range

Calculate ROI

Wrap-up

Model Validation, HR Interventions, and ROI

Employee data

Pertumbuhan organisasi sangat bergantung pada retensi karyawan. Sering kehilangan karyawan berdampak pada moral organisasi, dan merekrut karyawan baru lebih mahal daripada mempertahankan yang sudah ada. Kabar baiknya, organisasi dapat meningkatkan retensi karyawan dengan strategi intervensi berbasis data. Kursus ini berfokus pada akuisisi data dari berbagai sumber SDM, mengeksplorasi dan menurunkan fitur baru, membangun dan memvalidasi model regresi logistik, dan akhirnya menunjukkan cara menghitung ROI untuk strategi retensi yang potensial.

HR Analytics: Exploring Employee Data in R

Pelajari cara memprediksi pergantian karyawan dan merancang strategi retensi dengan R.

Analitik SDM: Memprediksi Pergantian Karyawan dengan R

Prediksi tingkat turnover karyawan dan rancang strategi retensi. 

Menafsirkan hasil

Analitik SDM: Memprediksi Pergantian Karyawan dengan R

Latihan interaktif praktis