People Analytics - Part C of D

People Analytics & People Strategy Improvisations – Part C of D

Overview of summary - Averages of variable for Attrition group & Non - Attrition group
0 (Stay)
1 (Attrited)

Notice that apart from Satisfaction and Salary (not present in above table), there is not much difference in the variables for the people who have stayed and for the ones who are attriting. This is the single most reason why Satisfaction is included as Lead Indictor for deriving analysis here.  

Insights from Lead Indicators of Attrited Workforce:

Let’s put few parameters on Distribution plots: (Satisfaction – Evaluation – Average Monthly Hours – Experience – Salary)

The U shape formations above are apparent on why people are leaving and why we don’t want to retain everybody. Some people don’t work well as we can see from their evaluation, but clearly there are also many good workers that leave. Equally, check the graphs below – Tenure spreads and Salary

Let’s as well understand Salary variable for People Staying v.s. Attrition:

·         Majority of employees who left either had low or medium salary.
·         Barely any employees left with high salary
·         Employees with low to medium salaries are at a higher risk of attrition.

How does the Attrition roll up Function wise?

  ·      The Sales, Technical, and Support were the top 3 functions to contribute highest to Attrition
  • The Management & RnD functions share was minimum

Attrition & Project Count
·         More than half of the employees with 2,6, and 7 projects left the company
·       Majority of the employees who did not leave the company had 3,4, and 5 projects.   The sweet spot for number of projects allocated seems to be between 3 - 4, as well resonating the undertone of employees not utilizing their potential to the fullest
·         All of the employees with 7 projects left the company
·         There is an increased risk of Attrition as project count increases

Attrition & Evaluation

·         There is a bimodal distribution for people attriting.
·         Employees with low performance are a higher attrition risk.
·         Employees with high performance are as well, at a high attrition risk
·         The sweet spot for employees that stayed is within 0.6-0.8 evaluation

Attrition & Average Monthly Hours

·         Another bi-modal distribution.
·         Employees who had less hours of work (~150hours or less) are at a higher attrition risk.
·         Employees who had too many hours of work (~250 or more) as well are at a higher attrition risk.
·         Employees who left generally were underworked or overworked.

Attrition & Satisfaction

·        There is a tri-modal distribution for Attriting employees.
·       Employees who had really low satisfaction levels (0.2 or less) are at a higher attrition risk.
·      Similarly, Employees who had low satisfaction levels (0.3~0.5), are at a higher attrition risk.
·      And so are Employees who had really high satisfaction levels (0.7 or more), at a higher attrition risk.

Project Count & Average Monthly Hours

·         As project count increased, so did average monthly hours
·    Notice the fuzziness about the boxplot graph, where there is difference in Average Monthly Hours between people who left and those who stayed.
·    Employees who did not attrite had consistent Average Monthly Hours, despite the increase in projects.
·      In contrast, employees who did have a turnover had an increase in Average Monthly Hours with the increase in projects.
·       As well, employees who left worked more hours than employees who didn't, even with the same project count.

Project Count & Evaluation

·     There is an increase in evaluation for employees who did more projects within the Attriting group.
·         For the Non-Attriting group, employees had a consistent evaluation score despite the increase in project counts.

Satisfaction & Evaluation

·      In this visual, 3 distinct clusters for employees who attrited emerge; and this shall form a part of Primary People Management plan recommended earlier.

Cluster 1 – Top Left (High Performers & Least Satisfied): Satisfaction was below 0.2 and evaluations were greater than 0.75. Which could be a good indication that employees who left the company were good workers but felt horrible at their job. (These as well includes the Overworked Employees)
Cluster 2 – Centre Focused (Under Performers & Moderately Satisfied): Satisfaction between about 0.35~0.45 and evaluations below ~0.58. These are employees who got low evaluations and in a way are an anomaly, as they left the organization. In a place where they could have managed to stay further, they attrited – may be they were wrongly rated and they found a better offer coming by their side. We need more datasets to probe this further.
Cluster 3 – Top Right (High Performers & Satisfied Employee): Satisfaction between 0.7~1.0 and evaluations were greater than 0.8. Which could mean that employees in this cluster were "ideal". They are satisfied with the company and were evaluated highly for their performance.
      Check out all the parts of People Analytics:
  • Part A - Overview & Macro People Strategy Inferences
  • Part B - General Idea of Working Environment in the Org
  • Part C - General Reasons of Attrition
  • Part D -  People Strategy Improvisations