Designing retention strategy

HR Analytics: Predicting Employee Churn in R

Anurag Gupta

People Analytics Practitioner

Know who may leave

# Load tidypredict 
library(tidypredict)

# Calculate probability of turnover
emp_risk <- emp_final %>%  
  filter(status == "Active") %>% 
  # Add predictions using the final model
  tidypredict_to_column(final_log)

Know who may leave

# Look at the employee's probability of turnover 
emp_risk %>% 
  select(emp_id, fit) %>% 
  slice_max(n = 5, fit)

# A tibble: 5 x 2
  emp_id   fit
  <chr>  <dbl>
E202    0.9694593            
E6475    0.9814252            
E6574    0.9983320            
E7105    0.9193704            
E9878    0.9371767

Classification of employees in risk buckets

Classification of employees in risk buckets

Classification of employees in risk buckets

Classification of employees in risk buckets

Classification of employees in risk buckets

Classify employees into risk buckets in R

# Create turnover risk buckets
emp_risk_bucket <- emp_risk %>% 
  mutate(risk_bucket = cut(fit, breaks = c(0, 0.5, 0.6, 0.8, 1), 
                           labels = c("no-risk", "low-risk", 
                                      "medium-risk", "high-risk")))

Retention strategy

High Risk

Immediate action planning
Inform reporting manager
Hold one-on-one conversation

Medium Risk

Medium-term action planning
Keep tracking for any behavioral change
Have one-on-one or open house discussion

Retention strategy

Low Risk

Long-term action planning
Keep tracking for any behavioral change
Have open house discussion

No risk

No action required

Let's practice!

HR Analytics: Predicting Employee Churn in R

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