Predicting employee turnover has become increasingly crucial in recent years as organizations strive to build long-tenured teams, retain institutional knowledge, and achieve professional goals more efficiently. Losing employees can decrease productivity and reduce the morale and commitment of teams toward organizational goals. Replacing an employee may cost companies and HR significantly in talent acquisition efforts, onboarding, and training. This added expense, alongside lost knowledge of operations and team harmony, highlights the value of preemptive interventions like predictive AI in employee retention practices.

AI and machine learning in HR have emerged as transformative tools, allowing business leaders to leverage employee engagement metrics to determine and mitigate workforce attrition. 

Key applications of machine learning in HR include:

• Machine learning algorithms can forecast turnover risk using past data.

• Through access to individual employee insights and analytical data, machine learning can assist in strategizing and personalizing retention initiatives based on different employees’ expectations and engagement levels.

• Machine learning models can be continuously monitored and retrained using new data to improve predictions and intervention effectiveness.

This article discusses how AI can spot early warning signs of resignation before they happen.

The Significance of Machine Learning in HR

Traditional data collection methods like self-assessment surveys, performance reviews, 1:1 interactions, etc., often fail to provide accurate insights into employee engagement levels, especially in large organizations—for instance, a retail chain with a slew of stores across different regions and a large workforce.

• Machine learning models are capable of collecting, processing, and analyzing substantial volumes of employee data and recognizing trends and patterns, which might indicate a likelihood of employee attrition.

• AI algorithms can recognize complex patterns in employee behavior and performance data that may be missed by a human analyst but be connected directly with turnover risk.

• AI allows the creation of predictive models that can predict turnover likelihood based on historical data of an organization or even an industry.

Such predictive insights can help HR to act early, solving problems before they lead to turnover.

How Predicting Employee Turnover with AI Works?

Predicting employee turnover involves the use of predictive analytics for HR to assess the likelihood that a team member may leave the organization in the near future. This is achieved by analyzing datasets linked to employees and looking for patterns and trends that correlate with resignation. The data typically contains performance metrics, engagement surveys, and employee demographics, as well as external factors that include market tendencies and economic forces. 

Datasets for successful workforce attrition analysis may ideally comprise:

• Employee demographics such as age, gender, and educational levels.

• Monthly engagement scores as provided by managers.

• Employee’s tenure with the current employer.

• Historical data of employees’ starting and exiting dates with previous employers. 

• Employees’ recent promotion and salary increment history.

• Performance reviews and ratings by managers.

• Record of absenteeism (sick leaves, off-time, etc.).

• Engagement insights as obtained from surveys, feedback, and interactions.

• External market conditions and economic factors such as inflation, recession, etc.

The above data points may be transformed into predictive features—such as declining engagement or satisfaction, an increase in absenteeism, recent drops in performance ratings, or declines in survey engagement scores—to predict the risk of turnover in the near future. Techniques such as data collection, feature engineering, model selection, and training are essential for implementing machine learning effectively in HR.

Strategies for Reducing Employee Turnover with AI

The goal of incorporating AI in human resources is not just for employee resignation prediction but also for businesses to act on those predictions and start targeted interventions where they are required. AI may help HR strategize customized retention plans for each employee based on the insights they glean from AI analytics:

• Professional development programs, which include training or mentorship models can provide employees  opportunities to engage and grow in their careers. For instance, if an analysis concludes that an employee registering low engagement scores who hasn't been promoted in recent years shows a high turnover risk, HR may intervene with career progression opportunities for the said individual.

• Leadership training may be provided to individuals who show leadership potential through practical learning opportunities, workshops, one-on-one sessions, coaching and support.

• Wellness initiatives may be implemented to encourage mental and physical well-being for employees who may be grappling with workplace stress and disengagement.

Conclusion

A study by the International Journal of Management & Entrepreneurship Research shows that incorporating machine learning models in HR provides access to actionable insights for organizations to intervene proactively and reduce turnover risk. Integrating AI in HR analytics can help transform an organization's employee retention strategies. As workforces evolve, AI can be a powerful tool for HR to make preemptive interventions and increase productivity, reduce disengagement, and avert turnover risk as much as possible.