Welcome to the course! Anurag Gupta and Abhishek Trehan People - - PowerPoint PPT Presentation

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welcome to the course

Welcome to the course! Anurag Gupta and Abhishek Trehan People - - PowerPoint PPT Presentation

Sep 06, 2022 440 likes •704 views

DataCamp Human Resources Analytics: Predicting Employee Churn in R HUMAN RESOURCES ANALYTICS : PREDICTING EMPLOYEE CHURN IN R Welcome to the course! Anurag Gupta and Abhishek Trehan People Analytics Practitioners DataCamp Human Resources

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Welcome to the course!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R

Anurag Gupta and Abhishek Trehan

People Analytics Practitioners

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Understanding employee turnover

Churn refers to the gradual loss of employees over a period of time Churn / Turnover / Attrition are interchangeably used

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Why employee turnover matters?

Employee turnover is the biggest issue facing HR Employee turnover is the highest its been in 10 years Turnover costs way more than you think

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Types of employee turnover

VOLUNTARY TURNOVER When an employee chooses to resign INVOLUNTARY TURNOVER When an organization decides to let go of an employee

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Common reasons for employee turnover

Better opportunity Health Relocation Education Personal reasons etc.

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Hidden reasons of employees turnover

Relationship with manager Percent salary hike Overtime Travel distance Career satisfaction Tenure

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Course overview

Chapter 1: Introduction to employee turnover prediction Chapter 2: Building relationship with data Chapter 3: Building turnover prediction model using logistic regression Chapter 4: Model validation, RoI calculation and retention strategy

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Basic requirements for the course

dplyr: Data wrangling ggplot2: Visualization and exploration

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Know more about turnover

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R

Anurag Gupta

People Analytics Practitioner

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Understanding the data

glimpse(org) Observations: 2,291 Variables: 12 $ emp_id <chr> "E11061", "E1031", "E6213", "E5900", "E3044"... $ status <chr> "Inactive", "Inactive", "Inactive", "Inactiv... $ turnover <int> 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,... $ location <chr> "New York", "New York", "New York", "New Yor... $ level <chr> "Analyst", "Analyst", "Analyst", "Analyst", ... $ date_of_joining <chr> "22-03-2012", "09-03-2012", "06-01-2012", "2... $ last_working_date <chr> "11-09-2014", "05-06-2014", "30-04-2014", "0... $ gender <chr> "Male", "Female", "Female", "Female", "Femal... $ department <chr> "Customer Operations", "Customer Operations"... $ mgr_id <chr> "E1712", "E10524", "E4443", "E3638", "E3312"... $ cutoff_date <chr> "31-12-2014", "31-12-2014", "31-12-2014", "3... $ emp_age <dbl> 22.49, 22.42, 22.24, 22.32, 22.14, 22.67, 22...

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Calculating turnover rate

Turnover rate =

r

Turnover rate = = mean(turnover) where 1 means Inactive ; 0 means Active Total number of employees Number of employees who left Count of all 1’s + Count of all 0’s Count of all 1’s

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Count Active and Inactive employees

# Count Active and Inactive employees

rg %>%

count(status) # A tibble: 2 x 2 status n <fct> <int> 1 Active 1881 2 Inactive 410

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Calculate turnover rate

# Calculate average turnover rate

rg %>%

summarize(turnover_rate = mean(turnover)) turnover_rate 1 0.1789612

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Calculate turnover rate at each level

df_level <- org %>% group_by(level) %>% summarize(turnover_level = mean(turnover)) df_level # A tibble: 7 x 2 level turnover_level <fct> <dbl> 1 Analyst 0.215 2 Assistant Manager 0.0365 3 Director 0 4 Manager 0.0435 5 Senior Manager 0 6 Specialist 0.149 7 Vice President 0

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Visualize the turnover trends using ggplot

# Visualize the results library(ggplot2) ggplot(df_level, aes(x = level, y = turnover_level)) + geom_col()

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Talent segments

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R

Anurag Gupta

People Analytics Practitioner

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Identifying the talent segments

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Identifying the talent segments

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Filtering the dataset

# Select the employees at Analyst and Specialist level

rg2 <- org %>%

filter(level %in% c("Analyst", "Specialist"))

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

HR data sources across employee life cycle (ELC)

Employee Life Cycle HR Data Source Talent Acquisition Taleo, ADP Employee Info SAP HR, Workday, SuccessFactors Learning & Development SkillSoft, Cornerstone Rewards & Recognition OC Tanner, Kwench Onboarding, Engagement and Exit Surveys SurveyMonkey, SurveyGizmo

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HR data architecture

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Merge datasets using left_join()

glimpse(df1) Observations: 2 Variables: 2 $ emp_id <int> 1, 2 $ level <fct> Analyst, Analyst glimpse(df2) Observations: 2 Variables: 2 $ emp_id <int> 1, 2, 3 $ location <fct> NYC, Atlanta, NYC df3 <- left_join(df1, df2, by = "emp_id") glimpse(df3) Observations: 2 Variables: 3 $ emp_id <int> 1, 2 $ level <fct> Analyst, Analyst $ location <fct> NYC, Atlanta

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DataCamp Human Resources Analytics: Predicting Employee Churn in R

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN R