Employee Sentiment = People Analytics + Workforce Analytics There’s a good reason why we made the title of this blog post a mathematical equation— because employee sentiment can be quantified, and the power that number gives you is very, very big.
Let’s start with the basics, though. Employee sentiment is the way your employees feel about your organization. All of the reviews you see on platforms like Indeed and Glassdoor are examples of employee sentiment. In them, you get to read the good, the bad, and the ugly, and if you have access to the right analytics platform, you can see how all of those opinions can be turned into a score that can be analyzed and, most importantly, improved. So how does the rest of the equation fit in? •
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People analytics are a data-driven approach to managing people at work. With them, employers can use statistical insights from employee-related data to make talent decisions, including hiring, compensation, retention, and performance. For example, you might take all of the timesheets for a specific project to see how well your employees collaborated on it. Or, you might look at all of the client renewals one employee generated and decide if they’re worthy of a raise. In the end, people analytics are designed to advance your employees’ success and give you insight into any people-related problems that may exist in your organization. Workforce analytics study the performance of your team as a whole so that you can make smarter business decisions. These analytics go beyond the people themselves and provide insights into your organization’s operations. For example, is the training that your new hires receive as effective and efficient as it should be? Is your annual turnover rate higher than you would like it to be? How diverse is your organization?
These analytics can answer all of these questions, and more. While employee sentiment is relatively new to the HR analytics landscape, people analytics and workforce analytics are not. Over a century ago, an industrial engineer named Frederick Taylor studied the movements of his iron workers in an effort to determine how much weight they should be transporting each day. If his workers struggled mightily or got injured, they were carrying too much weight to be successful. But if they moved around with complete ease, they weren’t carrying enough weight