In today’s highly competitive market environment, how to effectively retain talents has become one of the issues that enterprises need to face. Conduct turnover predictive analytics to anticipate turnover risks, as once the turnover rate rises, it not only increases the cost of recruitment and training, but also can lead to a decrease in team efficiency and hidden losses such as knowledge loss.
In such a scenario, it has become an important strategy for human resources management to make good use of data analysis to predict the turnover risk of employees and take proactive interventions in advance. With the help of AI tools, enterprises can face this challenge more efficiently and accurately, and achieve twice the result with half the effort.
Conduct turnover predictive analytics: Why do companies need to?
The impact of brain drain far outweighs the cost of hiring, and the loss of key talent often takes months or even years to remedy. Traditionally, companies have relied on experience and subjective judgment to predict employee turnover, but this approach lacks data support and has limited accuracy, leaving HR departments often in a situation of reactive remediation. This poses a challenge to the stability and long-term development of enterprises.
According to statistics released by Statista in 2024, the main reasons for leaving U.S. employees in 2022 include low salaries, lack of respect at work, and lack of opportunities for advancement. If companies can use AI to analyze key indicators during their employment, such as the annual salary increase of high-performing employees, they can predict and effectively reduce the turnover rate in advance, and retain key talent for the company.
Common challenges of using Excel to analyze turnover data
In the past, many HRs have tried to leverage Excel for predictive analytics on turnover, but often face the following challenges:
Difficult data integration: Exit analysis needs to deal with data sources from multiple systems, such as payroll systems, attendance records, performance appraisal reports, etc. It often takes a lot of time just to open the file, and the differences in different data formats often lead to a cumbersome integration process, which further increases the workload.
Complex formula design: When using Excel, it is common to encounter complex formula design logic, especially when calculating cross-indicators, a slight improper design may lead to errors, resulting in inaccurate results.
Confusion about the choice of metrics: Many HRs often don’t know where to start when doing exit analysis, and are confused about which metrics to analyze. Does it depend on the employee’s working experience? Salary adjustment? Or is it the pressure of the team’s work? Without a clear direction, it is easy to get stuck in the analysis, and in the end, it may affect the accuracy of decision-making.
Real-time data: Excel analysis reports often rely on “worker work”, which cannot update data in real time and lacks an automated early warning system. When the risk of employee turnover occurs, HR often fails to understand and respond in a timely manner, missing the key opportunity to retain talent.
These challenges illustrate that Excel alone is really difficult to meet the needs of enterprises for data accuracy and efficiency, so making good use of AI data tools can make turnover prediction more efficient.
Make good use of AI data tools to easily accelerate the analysis process with one click
Accurately predicting future turnover often requires complex statistical models, which can be quite a daunting challenge for HRs without a background in data analytics. However, through the AI data analysis platform, you don’t need to understand complex technology, and you can quickly grasp the turnover risk of employees with a visual list of decision-making rules.
Through AI data analysis, identify the profile of people with high turnover risk
As shown in the figure above, we pour employee-related information into the AI data analysis platform, such as employee rosters, salary adjustment records, annual performance appraisal forms, absence record forms, employee satisfaction questionnaires, etc.
The system will automatically generate a series of key rules that affect turnover, such as the first set of rules “personal leave” and “no salary adjustment in the past 3 years”, under this combination, the probability of employees leaving the company is as high as 99%. Or the third set of rules “between 1 and 5 years of experience” and “not filling in the employee satisfaction survey”, the probability of employees leaving the company is as high as 88%.
Such analysis results not only save a lot of manual judgment time, but also effectively avoid missing important turnover risk factors.
Use AI data analysis to gain insight into the key factors of employee turnover
The AI data platform can also quickly compare the correlation between the resigned employees and the corresponding data, and further detect the main factors that may cause the company’s employees to leave the company.
Through AI data analysis, we can predict the turnover risk of each employee in the next year
We can also upload the information of the current employee to check whether the employee is a high-risk group for leaving, such as the trust in the picture, which indicates the turnover probability of the employee in the next year, and conduct follow-up care and interviews, which will reduce the turnover rate of employees.
conclusion
Through the No-Code platform, HR can quickly build a turnover prediction model without writing any code. These tools make it easy to integrate multiple data sources, automate key metrics, and generate real-time predictions to help HR pinpoint high-risk employees and develop prevention strategies. It not only saves a lot of time, but also lowers the technical threshold and improves efficiency in an all-round way.
The use of AI is not only helpful for businesses, but also for employees. Through real-time employee care, companies can create a healthier and more harmonious working environment, further enhancing employees’ sense of belonging and satisfaction. When employees feel valued, they are more willing to put in the work, forming a positive cycle. In particular, AI has shown significant value in job forecasting, not only to help companies reduce the risk of brain drain, but also to create a more engaging workplace culture.
As AI continues to evolve, we’re witnessing how it’s changing the way businesses manage their human resources, leading to more innovative and efficient applications. In the future, AI is expected to play a greater role in HR management and help enterprises stay competitive. If you’re looking to learn more about the real-world application of AI in HR management, there’s never been a better time to take action!
KSCC is a management consulting company in Taiwan. Our services include corporate in-house training, consulting, and leadership management.For more information about our corporate services, please feel free to visit our website: https://kscthinktank.com.tw/custom-training/