Using Machine Learning and Sentiment Analysis to Tackle Employee Burnout


Human Resource professionals are now using an advanced data-driven approach to improving employee retention. Machine learning software analyzes engagement survey responses and online reviews – quickly determining the “why” behind scores and quantifying the key themes that are driving burnout.

Work-life balance. Cynicism. Loss of enthusiasm. These are critically important words to digest when looking at employee happiness. Andrew Alexander, MD, and Kenneth Ballou, MD, listed them as symptoms of physician burnout in a recent article by the American Journal of Medicine, which also cites burnout on the rise – from 45.5 percent in 2011 to 54.4 percent in 2014.

According to a recent @work piece by Sheryl Kraft, job burnout, per se, accounts for an estimated $125 billion to $190 billion in healthcare spending each year and has been attributed to diabetes, heart disease, gastrointestinal issues, high cholesterol, and even death for those under the age of 45. And doesn’t even include detrimental roadblocks to an organization’s culture and ultimately customer consequences.

With that in mind, let’s take a look at one way to reduce burnout, improve employee engagement, and subsequently increase customer satisfaction and loyalty – all through sentiment analysis. First, we’ll look at the benefits of engaged employees.

Benefits of Engaged Employees
Engaged employees feel more internally motivated than those who have lost their interest or who have burned out. Let’s look at a few signs of engaged, satisfied and happy workers:

Customer-facing staff make eye contact, speak cordially and spread their happiness to visitors, and other staff members.
Nurses empathize more with patients, and they’re more mindful of a patient’s feelings and needs. Doctors show a more focused and truly sincere interest in patient care.
Food service workers feel more motivated to focus on customers and meeting their needs.
Overall, staff shows fewer turnovers, less absenteeism, less apathy, and make fewer mistakes.

How Employee Sentiment Analysis Works
It’s hard to hone in on employee sentiment no matter what size the organization, but large, complex organizations have a unique challenge. Human Resources systems can spit out surveys and monitor feedback channels to get a read on how their workforce is feeling. This returns employee engagement scores that can be monitored over time. It quickly becomes impossible to read, let alone act, on every piece of open-ended feedback.

That’s where sentiment analysis comes into play. Many organizations already have a feedback system in place, and a wealth of data is already sitting and waiting for analysis. There may be a lot of feedback data to process, and it can be daunting. Sentiment analysis software takes a look at all employee survey responses and quickly determines the “why” behind the engagement scores.

Clustering Qualitative Feedback Into Themes Using Machine Learning
To begin sentiment analysis, surveys can be seen as the “voice of the employee.” Engagement surveys that are distributed on a regular basis show engagement scores (i.e., how engaged are the respective employees) to track over time. Surveys often solicit open-ended feedback which returns voluminous amounts of sentiment in text form.

That sentiment can then be clustered into themes or topic areas. A good text and sentiment analytics platform will use machine learning (artificial intelligence, or AI) that uses algorithms trained to recognize the themes.

Employee engagement themes identified by machine learning include:

Career Growth
Work-life balance

Sentiment Analytics provides insights into what is driving morale
As themes are recognized, each employee comment from the various surveys can be tagged with the relevant theme(s). Each tag can be assigned a sentiment, for example positive, negative, or neutral. The AI algorithms do the grunt work – reading the qualitative feedback and organizing comments into different buckets with respective tags.

The HR or equivalent team within the organization can then review and take action on the insights from the newly AI-structured data.

By immediately viewing insights that were given by the “machine,” human resource professionals can then understand what impacts employee happiness without reliance on anecdotes and hunches, and subsequently, take action to make sentiment more positive where applicable.

For example, maybe the negative sentiment was a result of a new software application rollout, a relocation or move, a management change, or a modification in the benefit plan.


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