What is Predictive Analytics in HR?

Predictive analytics is not exactly a new concept. It gained prominence when a certain baseball team applied it to create a history in sports. Afterward, predictive analytics saw a wide adoption rate in the sporting world. Likewise, data analytics has left a mark on how organizations make decisions in the business world.

Predictive Analytics in HR meaning and definition

Predictive Analytics is a rising technology that HR leaders and managers use to forecast future outcomes by analyzing past and present data. It involves the use of data mining techniques to predict specific outcomes that empower hiring teams with the ability to make data-driven decisions. Using predictive analytics tools and software, HR leaders can take historical data in raw form and transform them into valuable information.

How Does Predictive Analytics Work?

Predictive analytics functions digitally, probing data to organize, analyze, and extract information to identify correlations and patterns. It transforms raw data into interpretable information that enhances decision-making.

Why Companies Should Care About Predictive Analytics in HR

Companies can use predictive analytics to forecast potential skill gaps and make strategic plans to fill them up. In addition, an equitable reward system can reduce employee desire to quit a job, reducing the employee turnover rate. As a result, companies make better decisions by leveraging data, saving costs, and boosting employee satisfaction. Also, for smooth recruitment exercises, companies, through forecasting, can put in place solid strategies to identify and retain talents, minimizing the risks of hiring unqualified personnel.

Implementing a Successful Predictive Analytics in HR

Establishing effective predictive analytics in HR is not an easy task. However, it is not impossible too. HR heads can initiate functional predictive analytics through these tips:

1. Define business targets: HR members should work closely with their teams to set long-term SMART company goals. In addition, relevant metrics through which these goals are measured should be put in place.

2. Address Ethical Issues: Knowingly or unknowingly, organizations could show favoritism or treat certain demographics of employees in the workplace unfairly. Predictive analytic teams should not use discrimination-building data to avoid discrimination and unfair treatment. Employees need to feel a sense of acceptance to feel valued and motivated.

3. Capitalize on the power of predictive analysis. HR heads can harness their full power by applying predictive analysis to specific goals. For instance, HR heads can incorporate predictive analytics to measure employee output against the level required, allowing them to have an insight into workforce productivity. They can then make decisions and establish strategies based on this information.

Maintaining Company's Culture Using Predictive Analytics in HR

Predictive analytics drives HR leaders to make evidence-based decisions that foster employee growth and productivity. Using this technology ethically and effectively, companies can outsource, acquire, and retain the right workforce that aligns with the company’s culture. A culturally homogeneous workforce can be a game-changer in ensuring a near-perfect working environment. Also, HR-management discussion regarding talent acquisition ultimately shifts from cost-based to investment-based as they have a long-term overview of what they need.

The Other Side

Many analytics systems measure specific characteristics that are irrelevant to individual job performance. Keeping data on aspects of employees' life and lifestyle and using them to drive decisions could lead to unethical decision-making.

Employees may be asked, "What type of pets do you like? What is your favorite food?" Sometimes, employees' hometowns are used to predict specific outcomes. For example, employees raised in urban areas are more likely to quit their job or roles than employees from rural or suburban areas. Although not against any laws, judging people over data points they have little or no control over is unethical.

Such toxic situations are avoidable by ensuring that only ethical information influences the data-driven decisions of predictive analytics. Therefore, to ensure predictive analytics is efficient, it has to eliminate bias by considering ethics.

Practical Case Study of Predictive Analysis in HR

AMC theatre recognized the importance of employees as the face of a brand in customer service. The company used analytics and profiling to identify candidates to get more qualified and high-performing customer-service representatives. Ultimately, they experienced a reduced turnover rate, better customer satisfaction, and higher employee engagement. May other companies and employers employ predictive analytics in in-house decision making, brand building, etc.

Where Else

As cybersecurity grows in importance, behavioral analytics monitors all activities on a network to identify unusual patterns that may indicate fraud. Companies can detect patterns and prevent criminal acts by combining predictive analytics and other analytical methods. Furthermore, to reduce marketing and advertising cost, managers employ data analytics to predict consumer behavior, responses to specific marketing campaigns, or purchases. Eventually, they can attract, grow, and retain their most loyal customers. In addition, with predictive analytics, credit card companies can use predictive models to incorporate relevant data to a customer's creditworthiness, determining their credit score. To function efficiently, hotels use predictive models to predict the number of guests for any given time to maximize revenue, while airlines use technology to set ticket prices.