How to Use Predictive Analytics to Boost Employee Engagement
Most of the staff human resource professionals interact with feel disengaged. Predictive analytics driven by artificial intelligence is one of the most promising technologies employers can use to address this. Can employee engagement analytics boost engagement?
How Predictive Analytics Applies to Employee Engagement
Employee engagement is an HR concept describing the connection a person has to their workplace and the dedication they feel to their duties. How engaged someone is determines turnover, retention, and job satisfaction.
In the context of employee engagement, predictive analytics involves collecting and analyzing worker data to determine how likely specific outcomes are. Its goal is to make people feel more connected to their jobs, decreasing the voluntary quit rate.
When HR teams use predictive analytics, they must first collect employee engagement analytics — typically behavioral and performance data with historical and current metrics. Then, they analyze the information with artificial intelligence to uncover hidden patterns.
Why Use Predictive Analytics to Engage Employees?
HR teams should consider using predictive analytics to boost engagement because it increases retention — and many people are looking to quit at any given time. In 2022, 40% of workers reported they planned to leave their jobs within three to six months. Making them feel more dedicated and connected can convince them to stay.
While many factors contribute to retention and turnover rates, engagement is one of the biggest. Unfortunately, statistics show most people feel a disconnect with their employers. In the United States, over 50% of workers feel disengaged. In all likelihood, most of those people won’t think twice about quitting and finding work elsewhere.
While employers can track employee engagement analytics with alternative analysis methods, they won’t gain the same insights. They’ll be able to see when and how workers’ behavior and performance changes but won’t understand the context. Moreover, other tools uncover trends too late since they don’t offer predictions.
Predictive analytics helps employers understand why behavior and performance change, enabling them to take proactive action. More importantly, it makes accurate predictions about outcomes because it leverages AI to uncover patterns humans will overlook.
Potential Integration and Utilization Barriers to Consider
AI has been one of the biggest global trends for ages, so HR teams may not have to fight for adoption. That said, the C-suite might believe algorithms should do more than track employee engagement analytics. Professionals should be prepared to plead their case and back up their claims with data.
Another potential integration and utilization barrier is time. Every second counts when a team member is considering quitting and while AI works unbelievably fast, it learns relatively slowly. Gathering enough relevant data and training an algorithm is time-consuming.
Additionally, extracting insights from the AI’s output and taking action based on it can take weeks, if not months. By the time HR teams are ready to make the necessary changes to boost employee engagement, the high-risk workers may have already quit.
How to Use Predictive Analytics to Boost Engagement
While predictive analytics can be a powerful tool for boosting engagement, businesses will receive better outcomes if they know how to use it properly.
- Identify High-Risk Staff
Not all associates will make their discontent known — some will continue doing their job effectively until they hand in their two-week notice. HR professionals should use predictive analytics to uncover hidden behavioral trends, revealing who is more likely to quit.
While improving satisfaction with workplace-wide employee engagement analytics is a great approach, the HR team should also consider leveraging it case by case. After all, everyone has unique needs.
- Establish a Baseline
The HR team should consider using their algorithm during the hiring process. Roughly 33% of employees will leave their role within 90 days of being hired. Gathering data on their behavior, mannerisms, and performance history during interviews and onboarding can establish a baseline, enabling professionals to launch early intervention procedures if engagement drops.
- Determine Engagement Factors
AI can easily uncover hidden factors contributing to engagement. HR teams should use it to predict which ones will be most impactful so they remain proactive instead of reactive, ensuring staff remains consistently engaged. As a result, employee retention rates and job satisfaction will increase, making high turnover a thing of the past.
Predictive Analytics Can Increase Employee Retention
When HR teams work with advanced algorithms, they can accomplish more. Tracking employee engagement analytics with predictive technology enables employers to improve job satisfaction, increase retention, and minimize turnover. Instead of paying thousands of dollars to replace people who quit, they can maintain their talent.
Originally posted on OpenDataScience.com
Read more data science articles on OpenDataScience.com, including tutorials and guides from beginner to advanced levels! Subscribe to our weekly newsletter here and receive the latest news every Thursday. You can also get data science training on-demand wherever you are with our Ai+ Training platform. Interested in attending an ODSC event? Learn more about our upcoming events here.
