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).

Rekayasa fitur

Turunkan selisih usia

Menurunkan indeks loncat kerja

Menurunkan masa kerja karyawan

Kompensasi

Menjelajahi kompensasi

Kesenjangan Upah

Menurunkan Compa-ratio

Menurunkan Compa-level

Nilai informasi

Menghitung Information Value

Variabel mana yang penting?

Feature Engineering

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

Data splitting

Split the data

Corroborate the splits

Introduction to logistic regression

Build your first logistic regression model

Build a multiple logistic regression model

Interpreting significance levels

Multicollinearity

Detecting multicollinearity

Dealing with multicollinearity

Building your final model

Building final logistic regression model

Understanding the model predictions

Interpret the results

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. 

Menjelajahi kompensasi

Analitik SDM: Memprediksi Pergantian Karyawan dengan R

Latihan interaktif praktis