Introduction

In our analysis, we examined fluctuation trends over a period of two years (2023—2024) in a workforce of around 4,500 employees. We identified clear patterns in exit behavior — differentiated according to age, gender, seniority and role.

The most important findings? Turnover rates remain high among younger employees, voluntary layoffs are the main driver — and there are significant seasonal trends in both years.

This analysis helps to answer key questions:

• How do turnover rates differ by age group and gender?
• Which job families are particularly affected?
• Are there recurring seasonal patterns?
• What recommendations for action can be derived for employee retention and stability?

Key insights

A slight drop in the turnover rate of 19.8% In 2023 on 17.3% in 2024.

Clear differences between Job families — some rolls show significantly higher rates.

Distinct seasonal peaks — especially in the months following summer.

Understand fluctuation patterns — and be one step ahead of them!

Definition of turnover rate

The turnover rate is defined as the percentage of employees who leave the company in a specific period of time. It is calculated according to the so-called Schlüter formula:

(number of departures/(initial inventory + new entries)) × 100

The analysis relates exclusively to full-time and part-time permanent employees. Working students, interns and external service providers are excluded.

Pool sample overview

This analysis is based on internal employees with permanent full-time and part-time contracts. Working students, interns and external service providers were excluded.

• Number of employees: 4,525 employees (December 2024)
• Time period: January 2023 — December 31, 2025
• Gender: Male: 60% Female: 39% Non-binary/Unspecified: 1%
• Age groups: 20-29 years: 1,480 | 30-39 years: 1,844 | 40-49 years: 750 | 50+ years: 490
• Location: Germany
• Contract types: 90% full time/10% part time

Part 1:

About time

Our analysis shows a downward trend in the fluctuation of 19.8% In 2023 on 17.3% in 2024 — a positive signal that could point to better onboarding, employee engagement, or organizational stability.

Key findings

The turnover rate fell from 19.8% in 2023 to 17.3% in 2024. This decline could have been influenced by internal organizational changes, natural fluctuation, and economic factors such as stagnation and political instability in Germany since 2023.

With the analysis tools from PeopleIX Can you dive deeper into fluctuation trends and precisely identify the causes of turnover.

Uncover the causes of your fluctuation!

Part 2:

Voluntary vs. involuntary departures

To find out more about the fluctuation trend, we have the departures in volunteers and involuntary Categories subdivided.

Key findings

Quarterly correlation: Voluntary and involuntary departures often rose and fell at the same time.

Monthly pattern: No clear correlation.

Voluntary departures: Peaks in March, May, June and October.

Involuntary departures: More often in the first half of the year, often as a result of restructuring.

With insights from PeopleIX can you monitor your fluctuation data and track both voluntary and involuntary departures in real time.

Get a clearer insight into your fluctuation patterns!

Deep Dive podcast:

Why turnover undermines Employee Lifetime Value (ELTV)

What does high fluctuation really cost? This episode is about the impact of cancellations on ELTV.

Part 3:

Seniority and fluctuation

The analysis of turnover by length of service shows that employees with less than two years with the company have the highest departure rates, primarily due to involuntary departures.

Key findings

Employees with over 2 years of employment show a significantly lower involuntary fluctuation rate (approx. 30%). The differences between senior groups illustrate the higher vulnerability of new entrants, while experienced employees are less affected by layoffs. Higher positions also have no clear seasonal patterns, which could suggest a different logic in the labor market for these roles.

With data from PeopleIX Can you analyze the turnover patterns according to seniority and develop targeted retention strategies for risk groups.

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Part 4:

Seasonality of turnover rates

Turnover rates show seasonal patterns, with higher rates at the start of the year and a decline in the second half of the year.

Key findings

Turnover rates are higher in the first two quarters and then fall significantly. This is particularly the case with involuntary departures, which indicates that companies are adjusting their personnel structure in the first half of the year.

Use the new feature from PeopleIXto compare different time periods and better understand the fluctuation trends in your company.

Track your seasonal fluctuation cycles!

Part 5:

Overview of turnover rates by department and area

Key findings

In 2024, the highest fluctuation rate is Finance & Accounting as well as Sales & Marketing at 23.2%, slightly lower than 2023. General Management rises to 18.8%, while Operations falls from 18.6% to 14.6% People & Culture falls to 13.8%, and IT shows the biggest decline from 14.6% to 7.8%. Engineering remains stable at 9.6% Turnover remains high, particularly in commercial and managerial roles, while technical and support functions are more stable.

With these deep insights, HR teams can develop proactive retention programs and reduce turnover in a targeted manner.

Get a clearer insight into your fluctuation patterns!

Part 5:

Fluctuation by age group

Younger employees (20-39 years) left the company voluntarily more often, while older age groups (40-59 years) showed higher involuntary departures.

Key findings

Younger employees (20-39 years) are more likely to leave the company voluntarily, while 40-59 year olds are more likely to be involuntary departures — probably due to targeted restructuring and layoffs.

With the powerful segmentation features of PeopleIX Can you track turnover rates by age group and implement personalized retention strategies.

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Further in-depth analyses

Looking at fluctuation by age group, it is clear that younger employees (20—39 years) leave the company voluntarily more frequently, while older age groups (40—59 years) are more affected by involuntary fluctuation.

• Costs of fluctuation: How do the costs of voluntary and involuntary departures differ?
• Effects on performance: Do top performers leave the company more frequently or less frequently?
• Key positions: Which roles are particularly affected by fluctuation?

Understanding fluctuation. Identify risks. Act strategically.

With PeopleIX, you recognize patterns behind fluctuation, identify risks and actively counteract them — based on data and with foresight.

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