NPS survey analysis
In this report, we will be analysing the NPS survey results from 762 employees. We will be identifying trends and insights related to employee satisfaction, likelihood of leaving, and the influence of factors such as department, seniority, and remote work. Let's proceed with the analysis.
Overall NPS: Calculate the average satisfaction score across the company.
Department-wise Analysis: Break down the satisfaction score by department to identify which ones are performing well or poorly.
Seniority Level Analysis: Understand if seniority level affects satisfaction and likelihood to leave.
Remote vs. On-site Analysis: Examine if there's a significant difference in satisfaction between remote and on-site employees.
Full dataset
Filter dataset by department, seniority or being remote
seniority
Senior
department
Development
remote
No
# Filter dataset based on the department and store it in a new DataFrame
second_df = nps_df[nps_df['department'] == department]
second_df = second_df[second_df['seniority_level'] == seniority]
second_df = second_df[second_df['remote'] == remote]
Overall NPS
Calculate the average satisfaction score across the company.
Department-wise analysis
Break down the satisfaction score by department to identify which ones are performing well or poorly.
Seniority level analysis
Examine if there's a significant difference in satisfaction between remote and on-site employees.
Remote vs. on-site analysis
Examine if there's a significant difference in satisfaction between remote and on-site employees.