Apr 7, 2021
Short Studies

How Data Can Help You Keep Your Workers

Paul Suddon

Our team clustered a dataset containing information about IBM employees to discover the reasons why employees left their jobs. Using key variables like an employees salary, their age, their satisfaction level and the number of hours they work - we were able to isolate three distinct communities who left their jobs for different reasons. Employee retention is crucial for the success of a company and this investigation demonstrates that simple steps can be taken to improve the happiness and well-being of staff and consequentially prevent valuable employees from leaving their jobs.

A quick Google search can show you how expensive it is to train your staff. Onboarding alone has an average cost of $1886 per employee. This is before you add in hidden costs as well as the wage that is paid in this period where your employee isn’t working to their full potential. We invest in our workers with the hope that their time and effort will pay off. But each employee is unique in their own way. There is no guarantee that employees will develop in the way that we hope, contributing to the culture of the company, maintaining top standards in their careers or simply remaining in their jobs.

Employers will be all too aware of this. A recent study found that in the USA, millennials change jobs an average of four times in their first decade of working life. For Gen Xers — those born between 1965 and 1980 — one or two job changes during the same period has been shown to be more likely. It is costly to train and develop new workers. Retaining employees can save significant amounts of money for employers but the trick is understanding why they are leaving in the first place.

Millennials change jobs far more often than their parents did during the first 10 years of working life.

The Approach: Clustering Employees

In order to showcase how an enterprise company could reduce employee turnover, our team grabbed a dataset containing information about IBM employees alongside a variable detailing whether the employee has left or remained in their jobs. Then, we set out to build a clustering model in order to discover sub-communities within the group of employees that left their jobs.

Our team wanted to uncover the different reasons why groups of employees left their jobs.

3 Communities of Employees that Left their Jobs

First, we uploaded the data to Graphext. Then, we built a project clustering employees using their characteristics as factors and the field specifying whether they remained in the company as the target variable. We found 3 distinct clusters of employees that left their jobs, each with different motivating factors.

A network visualisation segmenting IBM employees based on whether they left their jobs or not.
Graph: 3 clusters of employees that left their jobs.

From the perspective of a HR department, the primary aim would be to reduce the number of high performing employees that left the company. We used Graphext’s compare panel to distinguish which clusters featured employees with the highest scores for their previous evaluation and identified that we should focus on the green and orange clusters.

Similarities between employees in the green and orange clusters show that, despite strong evaluations and having worked in the company for 5 years, they are very unlikely to have been promoted.

Employees in the green and orange clusters were good at their jobs but received low salaries and were not often promoted.

Using this finding, our team formed a hypothesis that the IBM HR department would be better offering a more competitive salary plan for these people if they wanted to keep their high performing employees. Without fully understanding more about the difference between these two communities, we could already assume that a lack of promotions and a low salary will increase the risk that people will leave your company.

Comparing the Features of 2 Key Employee Clusters

Our team then used  compare panel to uncover the defining differences between these two clusters. Satisfaction level was a significant variable here, with employees in the orange cluster seemingly sharing a low satisfaction level whilst employees in the green cluster enjoyed more job satisfaction.

A series of charts showing the difference between 2 communities of IBM employees that left their jobs.
Compare: Two communities of high performing employees that left their jobs.

Setting out to investigate why satisfaction level varied so greatly between the two clusters, our team found that employees in the orange cluster worked the most of all employees in the dataset (on average 250–300 hours every month). Although employees in the green cluster worked a lot, their monthly hours were more moderately balanced against the rest of the dataset. These findings led us to another hypothesis; that employees in the orange cluster leave IBM because they work a lot, don’t get promoted and receive low salaries.

The Defining Characteristics of Sales Team Members

Now, our team turned their attention to the green cluster, attempting to uncover the factors motivating these employees to leave their jobs. Employees in this community work only slightly less but are much happier. They seem to be able to handle stress as they still work on a number of projects but don’t have to work as much each month to do so. It’s possible that they are more efficient.

As we dove deeper into our analysis of this cluster, it became apparent that the majority of staff in this cluster are in sales. Since they are efficient workers and, as salespeople, should have strong business acumen, our team supposed that a potential workaround to prevent these employees from leaving their positions might involve moving them into management positions. Using this type of analytics, employers could identify candidates worthy of management consideration each year, guaranteeing that only hard-working, satisfied and loyal staff members were promoted and at the same time, reducing employee churn and recruitment costs.

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