The workplace is broken.
Research by Gallup shows that, of the 1 billion workers worldwide, only 15-percent consider themselves engaged—and we’re not just talking third-world sweatshops. Even advanced countries like the United States have their fair share of workplace woes.
How might companies address this?
HR analytics (also called people analytics) is shedding light on why people behave the way they do in the workplace. It makes use of data (e.g. KPIs, employee surveys, productivity reports, resumés, etc.) to answer questions on productivity, job satisfaction, employee turnover, and all things people.
BAs a result, we’re seeing companies of all shapes and sizes invest in data-driven human resource management. Here are some common use cases:
Who are my employees?
Not all employees are cut from the same cloth. Some prefer people-facing jobs, and others work better with machines. Some are motivated by benefits, and others respond to recognition. The major challenge HR managers face is figuring out who belongs where.
Employee segmentation is the practice of grouping employees into well-defined buckets based on demographics (e.g age, gender, race, and civil status) and behavior (e.g. skills, personality type, values, and spending habits). It often leads us to questions such as: Who are our top performers? Are our highest paid staff also the most productive?
One of the most popular examples of employee segmentation is Google’s Project Aristotle. The project sought to understand team dynamics—that is, what makes a successful team?—by studying hundreds of teams across Google’s 51,000 employees (spoiler alert: the answers were conversational turn-taking and empathy).
Employee segmentation can also lead to better hiring. An American call center operator called NOVO 1 was able to identify common traits among its top performers, and decided to hire more people like them. The effort paid off as it allowed them to shorten job interviews from one hour to twelve minutes, cut call times by a minute, and reduce attrition by 39-percent.
Are my employees engaged at work?
Companies spend billions on employee engagement—but it doesn’t always translate. Research by Dale Carnegie shows that 22-percent of employees think their organizations spend too much time and money trying to engage employees, and 26-percent say efforts to engage employees are a distraction from getting real work done.
Why the disconnect?
Many companies that invest in employee engagement ignore a crucial step. They put money in expensive engagement activities without first figuring out what makes their employees tick.
For example, an employee motivated by health benefits may not enjoy team building activities; and one who responds to sales bonuses may not respond to recognition.
Employers need to understand that each company is unique—so is each team and each employee. The next time someone decides to spend on engagement, look at the data and find out what drives it all the way down to the employee level. Often, you’ll be pleasantly surprised that the cheapest initiatives can lead to the most significant results. Your managers will be thrilled.
In Bank of America, simply switching from solo breaks to group breaks increased productivity by 23-percent and reduced employee stress levels by an average of 19-percent. No fancy gimmick required.
If you have no employee data yet, consider regular employee surveys or tracking activity using badges and IDs—or, if you can afford to be more sophisticated, investing in continuous listening will allow you to collect millions of data points per employee and perform advanced people analytics.
Managing employee engagement can contribute to higher job satisfaction, better work-life balance, and stronger personal development in the workplace—all of which add to productivity and improve your bottom-line.
How do I track employee performance?
Performance is always at the top of every manager’s mind. After all, that’s how people are measured in the workplace (and how bonuses are computed at the end of every quarter). It’s no longer enough to say Mary is doing a good job John isn’t. We need to be able to quantify and qualify what constitutes a “good job” and what doesn’t. In the words of the late-great Peter Drucker: What gets measured gets managed.
People have been tracking performance for ages, some say as far back as the Chinese Wei dynasty during the third century. However, the KPI-based framework we know today has only been around since the 90s, when Dr. Robert Kaplan and Dr. David Norton introduced the balanced scorecard.
Today, companies are tracking thousands of metrics, some as esoteric as team dynamics or company culture, or even as mundane as the average number of bathroom breaks or trips to the pantry. This explosion in employee data has helped companies find discover many factors that contribute to performance. Of course, there are some instances where data collection can feel oppressive to the employee…but that’s a topic for another blog.
Employee Turnover Prediction
Who are most likely to leave the company?
Losing employees is expensive. Apart from the obvious costs of losing a new hire, the void that an employee leaves behind can disrupt an otherwise smooth workflow. Of course, turnover is inevitable; there will always be people leaving companies for better pay, new opportunities, and many other reasons.
The best thing a company can do is understand why people leave.
We mentioned earlier that people leave for different reasons. The key to successful attrition management is to identify which people leave and for which reasons. This is a use case called Flight Risk Scoring or, in layman’s terms, employee turnover prediction.
The goal of this use case is to differentiate employees who are likely to leave from those who are likely to stay. The findings can be interesting such as: people in sales are more likely to leave than those in IT (perhaps IT people are better compensated), or employees who work under Boss X are more likely to leave than those under Boss Y (maybe Boss X is the problem).
Once you figure out why certain employees leave, it becomes much easier to take action and keep those employees from leaving.
How long does the typical employee last in a job?
If employee turnover prediction identifies who in the company is likely to leave, time-to-attrition modeling identifies when a particular employee is likely to leave.
Microsoft has gotten this down to a science. They’ve been able to use predictive modelling to tag flight risks ahead of time, and make the necessary adjustments, either by proactively looking for new talent or by offering better incentives. This has allowed them to reduce the time it takes to fill new positions and mitigate the costs associated with attrition.
Employee data is possibly HR’s most important asset. The more you know about your employees, the better can manage them and treat them.
If you find managing people a challenge, stay tuned for our upcoming articles. We’ll be guiding you through the above use cases one by one, including some instructionals on MS Excel.