How People Analytics Helps Predict Employee Turnover

Every HR professional knows the problem: staff turnover can have a significant impact on an organization's corporate culture and costs. When valuable employees leave, not only are their experience and knowledge lost, but the costs of recruiting and training new employees also increase. However, there is really something that can be done about it.

People analytics offers HR teams a solution in predicting and providing insight into progress. By using data analysis and predictive models, companies can use employee data to predict turnover and take targeted measures to reduce this. In this blog, we'll discuss what people analytics is, how it works and how you can use it effectively to predict staff turnover.

What is People Analytics?

People analytics is a data-driven approach that helps HR professionals make informed decisions based on factual data about employees. While traditional HR methods are often based on intuition and experience, people analytics enables companies to gain objective insights into employee performance, satisfaction, absenteeism and turnover.

How does HR analytics work?

In people analytics, various types of personnel data are analyzed, such as:

• Job Satisfaction

• Performance and productivity

• Absenteeism

• Internal and external feedback

This data is combined and analyzed using statistical models and machine learning algorithms. This allows the HR team to discover patterns that would otherwise be difficult to observe and gain predictive insights about employee turnover.

Why is People Analytics Important for Predicting Attrition?

Predicting staff turnover is a complex task. It is not easy to predict which employees may leave without extensive data analysis. After all, people are not numbers on a spreadsheet; they have different motivations, behaviors, and circumstances that influence their decision to stay or leave.

People analytics helps to understand this complexity by analyzing various factors that influence turnover. By using data, the HR team can identify trends and patterns that point to potential leavers.

Key data insights for forecasting progress:

1. Job Satisfaction

Employees who are dissatisfied are more likely to leave the company. Regular satisfaction surveys can indicate early who is at risk.

2. Absenteeism

An increase in absenteeism may indicate burnout or dissatisfaction.

3. Performance and productivity

Declining performance can be a sign of demotivation, which often precedes departure.

4. Feedback and Engagement

Employees who feel less engaged or give negative feedback in 360° evaluations have an increased risk of attrition.

Example: People Analytics in Action

Let's take a look at a concrete example of how people analytics is used in practice to predict attrition.

Case study: a technology company with a high turnover among software developers

A major tech company noticed that their software developers had a high turnover rate. This led to significant recruitment costs and project delays. The HR team decided to use people analytics to better understand why employees left and how to prevent this.

Roadmap for getting started with HR analytics:

1. Data collection (input)

The HR team collected data about job satisfaction, absenteeism, performance, and workload in the development teams.

2. Analysis (insight)

Using predictive analytics, it was found that developers with low satisfaction scores and frequent overtime had a higher attrition risk.

3. Intervention (impact)

The company offered this risk group more flexibility in working hours, improved career development opportunities and invested in their well-being. Result: turnover fell by 15% in the following year.

This example shows how companies can use people analytics to take preventive measures to reduce turnover.

Key Data for Predicting Attrition

To effectively predict attrition, companies need to focus on specific types of data. It's not just about absenteeism or performance assessments; people analytics offers a wider spectrum of insights that HR professionals can use to predict attrition.

Here are some of the most important types of data:

1. Satisfaction scores

Regular employee satisfaction surveys provide a good picture of the mood among staff.

2. Absenteeism

Rising absenteeism rates may indicate an increased attrition risk.

3. Performance and productivity

Lower performance or productivity declines can be a sign of decreased motivation and engagement.

4. Internal feedback

360° evaluations and peer feedback can provide valuable insights into how an employee functions within a team.

Benefits of People Analytics for HR Professionals

The use of people analytics offers significant benefits for HR professionals who deal with staff turnover. Here are some of the key benefits:

1. Cost efficiency

Recruiting and onboarding new employees often costs a company twice the annual salary of the departing employee. By predicting and reducing attrition, companies can make significant savings.

2. Better staff planning

People analytics makes it possible to anticipate who may leave, so HR teams can plan replacements and support employees in high-risk groups.

3. Improved engagement

Employees appreciate it when their employer is proactive about their well-being. This leads to higher engagement and a more positive work culture.

Practical Steps for Implementing People Analytics

Implementing people analytics into your HR strategy can seem overwhelming, but with a structured approach, you can gradually gain valuable insights. Here is a practical step-by-step plan:

1. Collect the right data

Start collecting employee data such as satisfaction scores, absenteeism, performance, and productivity. This data forms the basis for your analyses.

2. Determine your KPIs

Decide which specific metrics you want to improve. This can be the attrition rate or a specific performance indicator.

3. Predict risk areas

Use machine learning models or specialized HR analytics tools to identify employees or teams at increased risk of attrition.

4. Implement targeted interventions

Once you've identified risk areas, take targeted actions such as improving work-life balance or providing career development opportunities.

5. Monitor and evaluate

Analyze the effectiveness of the measures taken and adjust them where necessary based on new insights.

Possible Challenges in Implementing People Analytics

While people analytics can provide powerful insights, there are also some implementation challenges:

1. Privacy issues

Employees want to know how their data is being used. Transparency and compliance with the GDPR regulations are crucial to gaining trust.

2. Resistance to change

Not everyone in the organization is open to data-driven decisions. HR teams need to communicate results and engage stakeholders to get buy-in.

3. Complexity of data analysis

Analyzing large amounts of data requires sophisticated tools and expertise. Consider external specialists or training programs to train your team.

Conclusion

People analytics provides HR professionals with powerful tools to predict and reduce staff turnover. By using data, companies can improve their staff planning, retain employees and save costs.

Want to know how your company can make use of people analytics? Request a free demo and learn how Deepler can help you predict and lower turnover with HR analytics.

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