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Artificial Intelligence can give power in your hand to analyze your data and generate insights, automatically?

We applied methodical and AI approaches to one of the critical problems in employee engagement – Employee Attrition. Let’s take a look at this case study.

Employee Attrition Analytics

There’s a law with the name “Joy’s law” which states “no matter who you are, most of the smartest people work for someone else”. Can we break this law through data analytics?

Employee attrition is a critical problem for the Human Resources department. In this era of competition, it becomes imperative to understand factors leading to employee attrition and employee retention. Some of those factors could be obvious while the others could be hidden.

Problem Statement

Can our data on employee attrition give insights into:

  • Why are people leaving the company?
  • Which segment of employees is leaving?
  • Where should we focus on?

Answers to some of these questions will help a CHRO take steps in the correct direction, improve employee morale and engagement to reduce attrition.

Approach

In this data analytics report (close to 40 pages) we take you through a methodical framework developed by us and deep dive into each step to understand the data, visualize it and analyze factors influencing attrition.

In this report we use R programming and Power BI to create a compelling data story.

Through this report we:

  1. Explore employee attrition data through various statistical and visualization techniques
  2. Find out factors influencing attrition
  3. Create a model to predict attrition
  4. Provide final conclusions

Statistical Analysis

We first perform univariate analysis against our variable of interest. Providing here with some examples.

Attrition by Age

Attrition by Job Role

Attrition by OverTime

Attrition by Distance from Home


We found how distance from home or years in company impact attrition. At the same time, we also saw how years with current manager and over time leads to attrition.

This analysis is good, but is not comprehensive. It does not allow us to check if there are multiple factors impacting attrition. We will analyze this using multivariate analysis.

A screenshot of the Power BI report for multi-variate analysis is attached here:

Based on the univariate analysis done in previous section we can ask several deeper questions to strengthen our standing.

  1. Why are young people leaving the company?
  2. Why is attrition so high in human resources and technical degree in Education Field?
  3. Why is attrition so high in Sales Rep Job Role?
  4. Why is attrition high with business travel as travel_frequently
  5. Why are employees who have not been promoted not leaving the company (attrition is very less)?
  6. What is the characteristics of employees who are doing overtime?

For answering such questions we use Power BI tool to analyze multiple factors at once. This tool allows us to interact with our data and give deeper level understanding on causes of attrition.

We are selecting the first question and analyzing it with Power BI.

Why are young people leaving the company?

After selecting age range for young employees (18-35), we can see:

  • 21.95% of attrition among young employees
  • Monthly income less than the median income (4K)
  • Most young employees who are leaving are Sales Representative, Lab tech, Sales Exec
  • 33% of them have bad work life balance

AI Visual

We took a leap in analyzing the data through Power BI AI Visual – Key Influencer Analysis.

What does it say about the data?

The visual automatically analyzed the factors contributing to attrition. Age and OverTime are the top factors. The visual also created segments contributing to attrition.

More detailed analysis can found here on our website.

Conclusion

In this case study, we saw various steps to approach a data analytics problem. We presented various statistical and visualization techniques to analyze the data. We also presented univariate and multivariate analysis for our problem and provided steps to create, analyze and fine-tune a model. For feature engineering and model fitting we used R programming, and for multivariate and interactive analysis we used Power BI.

We also deep dived into several questions related to employee attrition. Questions we answered were “why are young people leaving the company”, “why is attrition so high in Sales Rep role”, “what is the characteristics of people doing overtime”?


Our AI-powered data analytics framework has 4 major steps:

  1. Data Exploration
  2. Distribution Analysis
  3. Model Development
  4. Model Analysis, Conclusion and Recommendations

Though we have applied this framework to employee attrition problem, the framework can be applied to any data analytics problems.


You can contact us or schedule a call now to know more about our AI powered data analytics and insights approaches, and how it can impact your business positively.


Language used: R

Tools used: R Studio, Power BI

POWER BI REPORT LIVE DEMO >>                            DOWNLOAD DETAILED REPORT >>

Dataset: The dataset has been taken from IBM resource.

Link to the dataset: https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-HR-Employee-Attrition.xlsx

Thank you.

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