AI and Turnover Rate: How to Save More Money with "Predictive Analytics"?!

You are likely aware of the comprehensive digital transformation happening in all organizations, big and small. You also probably see that everyone is now using artificial intelligence in various types of work. Some have even started talking about how AI will replace humans in the not-so-distant future. But!

Does the role of digital transformation and artificial intelligence stop at simplifying routine tasks, automation, and writing messages and reports? If you're like us and "don't believe so," keep reading.

Of course, the role of digital transformation and AI extends beyond these tasks to other more complex, and certainly more important, missions. This includes changing how we deal with one of the biggest challenges in human resources: the employee turnover rate. We are now on the verge of a technological revolution using one of AI's data analysis techniques: predictive analytics!

AI-powered predictive analytics is not just a digital shift; it's a paradigm shift that transforms employee retention strategies from being reactive to being proactively strategic. Imagine having the ability to know which employees are most at risk of leaving before they even think about it, giving you the opportunity to intervene and make a real difference. This is the promise of predictive HR analysis.

The Hidden Cost of Employee Turnover: A Hefty Bill Weighing Down Companies!

Let's be frank: employee turnover is not just a number on an HR report. It's a real financial and operational drain that can exhaust any organization. Imagine that losing a single employee could cost your company between 50% and 200% of their annual salary, depending on the role and level of experience. This cost is not limited to recruitment and training expenses; it also includes the loss of institutional knowledge, a decline in productivity, and the impact on the morale of the current team. It's a hefty bill that we must all seek ways to mitigate.

So, what is Predictive Analytics in HR?

Simply put, it is a science that uses historical HR data, along with sophisticated statistical tools and smart algorithms, to make accurate predictions about future events related to the workforce. In an HR context, this means predicting turnover rates, forecasting future performance levels, and even anticipating the skills your organization will need in the future. The core idea is to move from "what happened?" to "what will happen?" and "how can we influence it?".

But how can AI tell us which employee is about to leave before they even do?

This is done by analyzing patterns. As mentioned earlier, this technology is provided with historical HR data. The AI analyzes this data and extracts patterns of performance and accompanying events for employees who have previously left the organization. It then compares the behaviors and performance of current employees with the patterns identified, highlighting commonalities between the past and present. This level of detail is almost impossible for the human mind to gather, given the massive amount of data, especially in large and older organizations.

For example, these technical models might notice a decline in a specific employee's engagement scores , an unexplained increase in absenteeism , or a correlation between turnover and a specific tenure or manager. The system compares these with the patterns from its data analysis, and when a match occurs, it considers them "signals" that departure is imminent.

These models work by connecting the dots between these signals, such as a drop in performance, a missed promotion, or even a change in email behavior (ethically and with privacy in mind, of course). By analyzing these variables, the system can identify employees at risk of leaving before it's too late. A prime example of this is the HR team at IBM, which achieved an incredible 95% accuracy in predicting turnover using predictive analytics. This achievement confirms the immense potential of this technology.

Turning Insights into Action: Data-Driven Employee Retention Strategies!

The role of predictive analytics is not just to identify the problem; it also extends to guiding HR teams toward targeted and effective interventions. When the system identifies an at-risk employee, HR can begin to think about personalized solutions, such as:

Policy Adjustments: The data might reveal that a certain policy is negatively affecting the retention of a specific employee group.

• Improving Career Paths: Does the employee feel stagnant? New development opportunities or clear career paths can be offered.

• Strengthening Manager-Employee Relationships: The direct manager is often the cornerstone of employee retention. Analytics can highlight teams facing challenges in the manager-employee relationship, allowing for intervention to provide support and training for managers.

These data-driven interventions ensure that HR isn't just "shooting in the dark," but is directing its efforts and resources where the need is greatest and where the real impact can be achieved.

Data Collection and Preparation: The Foundation of Accurate Prediction

After all of the above, you might be saying to yourself, "What promising technology!" But this wonderful, promising technology is nothing without "data." No predictive model can work efficiently without accurate and comprehensive data. This is the backbone of any successful analytical system. HR departments must ensure that data is regularly collected from various sources, such as:

• Human Resources Information Systems (HRIS): Containing basic information about employees, salaries, hiring, and more.

• Performance Management Tools: Providing insights into performance reviews, goals, and feedback.

• Employee Surveys: A direct measure of employee engagement and satisfaction.

• Attendance and Absence Data: Absence patterns can be an early indicator of problems.

Equally important to collection is

cleaning and preparing the data for analysis. Unclean or incomplete data can lead to false and unreliable predictions, which undermines all your efforts.

I have the data now, how can I build this model?!

Don't worry, dear reader, we won't leave you in the middle of the road. Fortunately, you don't need to be a data scientist to implement predictive analytics. There are many tools and platforms available that make this process much easier. For example, platforms like

Tableau and Visier are powerful data analysis platforms that offer visual dashboards and advanced predictive capabilities designed specifically for HR.

For more advanced professionals, programming languages like

Python offer unlimited flexibility in building custom predictive models.

In addition, many modern HRIS systems now integrate analytical and predictive capabilities as part of their packages. The choice of the right tool depends on the size of your organization, the complexity of your data, and your level of technical expertise.

In conclusion, we can say...

That digital transformation and artificial intelligence give HR the ability to become a true strategic driver within the organization. By adopting predictive analytics technologies, HR leaders can get ahead of reality, even if just by one step. This leads to significant cost savings , and the building of a more stable, engaged, and effective workforce that is better prepared to face future challenges. As for the question of whether AI will replace humans, we cannot be certain. But we can learn from history just as a machine learns from data analysis: humans invented machines, everyone thought they would be replaced, but one way or another, humans found their way back into the production cycle. The matter is more complex than we imagine.