Real-Time Prediction of Employee Engagement Using Social Media and Text Mining

Employee engagement is becoming a critical component of an organization's success. For many companies, increases in engagement mean increases in productivity. While organizations have long used traditional questionnaires to measure engagement, these leave room for interpretation; and trying to get clarity makes the questionnaires longer, which decreases participation. On the other hand, user-generated content can often act as a barometer on employee engagement, but does not lend itself easily to engagement prediction. We present a model leveraging machine-learning techniques to analyze and predict employee engagement, using both user-generated social analytics and text content from organization-wide surveys, to calculate a real-time engagement level for employees. Our method helped identify specific words and phrases used by individual employees that play an important role in engagement prediction. This study was able to uncover critical themes driving employee engagement and behavior at the enterprise level. Our model for textual analysis cannot only predict engagement, but also provide highly valuable, and actionable, data for business leaders about the health of their organizations.