Today we are going to present some insights related to employee’s working the satisfaction using Advanced Analytics tools and techniques. As a source for this study, we make use of the data made available on this link by the data scientist Ludovic Benistant who made important anonymizations. Some pictures have Brazilian Portuguese words, sorry about that! Let’s go!

Research Questions

Following the DCIM (Data Culture Introduction Methodology) methodology to guide this research, we came up the following questions:

  • What factors have the greatest influence on employee satisfaction?
  • What are the main satisfaction scenarios that exist?
  • What are the main patterns associated with key satisfaction scenarios?
  • What factors influence professionals to leave?

Data Characteristics

In total, 14,999 employees were evaluated, considering the following variables already sanitized by our scripts:

  • Employee satisfaction level (0 to 10) – Probably filled out by the employee;
  • Last evaluation (0 to 10) – Probably filled in by a manager;
  • Number of projects (2 to 7) – Number of projects in which the employee acted;
  • Average monthly hours (96 to 310);
  • Time spent at the company (2 to 10) – How long the person already worked in the company;
  • Whether they have had an accident at work – (Yes = 1 / No = 0);
  • Whether they have had a promotion in the last 5 years (Yes = 1 / No = 0);
  • Salary Range (Low = 1, Medium = 2, High = 3); Note: Actual values were not made available.
  • Left the company (Yes = 1 / No = 0).

Number of people per department


per-departament

Frequency Analysis / Distribution of Satisfaction

overal-satisfaction-level

The highest concentration of satisfaction is within the range of 7 to 9, and there are few people with satisfaction scores between 1.5 and 3.0.

Results

Ranking of Influence Factors in Work Satisfaction

By processing this dataset on VORTX Big Data algorithm

  1. Average monthly hours (50)
  2. Time spent at the company (21)
  3. Number of projects (20)
  4. Salary Range (13)
  5. Left the company (10)
  6. Whether they have had a promotion in the last 5 years (9)
  7. Whether they have had accident at work (9)

The factor “Last evaluation” had no relevant influence and it was automatically discarded by VORTX.

Satisfaction Scenarios

In the table below we have the result of the processing with the separation of employees into groups done automatically by the platform. In all, 120 groups have been found, and here we will focus on only the 20 most relevant and leave the others out as isolated cases and not the focus of the analysis.

english-table

Model Visual Validation

Typically managers, as far as we have experienced,  are not sure regarding machine’s ability automate the discovery of insights. Therefore, as proof of the model, we chose to show the raw data visually to demonstrate the insights aforementioned.

grupo-9-o-mais-insatisfeitos

The pattern of hours worked by the 588 people in scenario 9 (very dissatisfied). X Axis = Monthly working hours.

 

grupo-1

The pattern of hours worked in the largest scenario (1), which has 4085 employees, a good job satisfaction and a low level of job evasion. X Axis – Monthly working hours

In the view below, each circle represents a contributor in four dimensions:

  • The level of satisfaction on the Y axis.
  • Average hours per month on the X axis.
  • Orange colors for people who left the company and blue for those who remain.
  • Circle size represents the number of years in the company.

general-pattern

Alright, we just saw the overall pattern including the whole organization, so what would happen if we see it by the department?

accounting-and-it

managment-to-product

rd-and-support

technical

Conclusions and Recommendations

This study shed some light on the improvement of human resource management, which is at the heart of today’s businesses. Applying data analytics algorithms in this area allows automating and accelerating the process of pattern discovery in complex environments with, let’s say 50 variables or more. Here it was just a few. Meanwhile, the search for patterns in a traditional BI continues to be a purely artisanal work with a well know imitation of 4 dimensions per attempt (read more on this at Understanding the differences between BI, Big Data and Data Mining). The automation of discovery is an extremely important step in predictive analytics, in this case, the evasion of highly qualified professionals and possible dissatisfactions overlooked by management.

With VORTX’s ability to discover the different scenarios, we were able to analyze the data and conclude that:

  • People in group 1 and 2 (55% of the company) have a reasonable work satisfaction with a weekly load of 50 hours on average, without receiving promotion or suffering an accident at work.
  • The pattern persists in all departments.
  • The most satisfied groups of the 20 largest were the 7 and 10 who worked more than 247 hours a month, took on several projects but as they did not receive promotion they left the company. These people should be retained since there seams to be highly qualified.
  • Group 16 proves that it is possible to earn a good salary and be dissatisfied. These 77 people should be interviewed to identify the root cause of such unsatisfaction.
  • The cut-off line for non-company employees is: minimum 170 and maximum 238 hours worked per month.People with more than 3.5 years of work harder and are more satisfied.
  • Monthly hours above 261 resulted in very low levels of satisfaction.
  • Monthly hours below 261 with a number of projects greater than 3 turns out in high job satisfaction.
  • Scenario 15 shows the importance of promotion over the last 5 years of work.
  • The ones with more than 5 projects decrease their satisfaction, the ideal number is between 3 and 5. Of course, in this case, to better understand the indicator is necessary to better understand what the number of projects represents to different departments.

For managers, collecting as many indicators as possible is always good especially without interruption in all areas. More variables to enrich your model would be:

  • The distance between employee’s home and work.
  • The average time that is taken from home to work.
  • The number of children.
  • The number of phone calls or emails sent and received.
  • Gender and age and the reason for leaving the job.

We hope this information is useful for you guys in some way. If you find it relevant, share it with your colleagues. If in doubt, contact us! A big hug and success in developing your own HR strategy!

Autores
Joni Hoppen
Founder of Aquarela, professor and lecturer in the area of Data Science, master in Information Systems, focused on processes of rapid prototyping of Big Data Analytics and data culture.

Marcos Santos
Founder of Aquarela, CEO and architect of the VORTX platform. Master in Engineering and Knowledge Management, enthusiast of new technologies, having expertise in Scala functional language and algorithms of Machine Learning and IA.

Informações para referenciação: Gostou do material? Caso queira enriquecer sua pesquisa ou relatório (seja blog post ou artigo acadêmico), referencie nosso conteúdo como: Aquarela 2018 - Inteligência Artificial para negócios (www.aquare.la).