Organizations that hire high-tech employees are always in competition with other employers — not just within the tech industry itself, but across all sectors. 

As companies in traditional industries rapidly develop and advance their technology offerings, the hiring market for tech employees continues to get tighter. Mercer data shows 8.2% turnover in 2023 in the United States — already higher than the global average of 6.4% — and one quarter of US technology companies have reported turnover rates of nearly 12%. 

There are a lot of anecdotes and theories floating around about what makes people leave their jobs in tech, so we wanted to contribute to the conversation in a data-driven way. We analyzed turnover rates in high tech by applying the deep analytical work we do for our clients to a broad dataset. Our goal is to help leaders think more critically about why tech employees leave, so they can create better retention strategies. 

The insights from our research will help you identify groups of employees with a higher likelihood of quitting, so you know where to focus when making changes to your talent strategy, location strategy, hiring, compensation and benefits, and skill-based development. That said, not everything about turnover can, or will, have a specific solution.

Let’s find out who, why, and where people are quitting right now in the world of high tech.

Our analytical approach

We built regression models that explain variation in voluntary turnover by controlling for various job and employee factors. We used the Mercer | Comptryx database for our research. The data include information such as an employee’s job level, type of work, tenure, performance, and pay.

The data architecture is standardized across the database, meaning that an entry-level professional in industrial design means the same thing across companies. This standardized architecture allowed us to conduct cross-company analyses by different segments within high tech.

Our analysis focused on U.S. employees who were either actively employed or terminated, were not expatriates, and had general experience (proxied by age), tenure, and pay information. The result was a dataset that included about 365,000 records, covering the period from July 2021 to September 2022. 

Our findings

Industry segment

Not all high-tech companies are alike. If we take the Software/SaaS segment as a comparison point, employees in Networking and Telecommunications industries are more likely to quit. By comparison, employees in Computer/Hardware Systems, Consumer Technology, Electronics, or Internet/E-Commerce segments are less likely to quit. 

Geographical area

We looked at a dozen well-known tech hubs to see the impact on turnover. As you might expect, there are several geographies where people are more likely to leave all else being equal; the probability of quitting is higher in the San Francisco Bay Area as compared to most areas. However, there are some locations where it’s harder to retain by pure virtue of location: Austin, Texas, and the North Carolina Research Triangle Park have higher turnover rates comparted to the San Francisco Bay Area. This suggests that retaining talent in these tech hubs is harder despite “not being in the Bay Area.” 
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Age

Younger employees are more likely to quit than older employees. The likelihood of staying increases with age until 60-64, then reverses after age 65 as employees reach historical retirement age. This relationship between age and turnover is consistent across segments in our Mercer | Comptryx dataset.
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Tenure

Employees are most likely to quit between two and five years of tenure. New hires gain experience and learn about their employer for the first few years, then choose to leave or stay in this time frame.

After the first five-year period, the remaining employees are less likely to quit until they have about 20 years of tenure. The tenure effect on quit probability is generally consistent across segments in our dataset.

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Performance and promotions

Top performers are 4% less likely to quit, and recently promoted employees are 6% less likely to quit. This means that if an organization has a 10% turnover rate for average performers, a top performer (all else equal) has a 6% probability of quitting This suggests that in the technology industry, promotions are more retentive than higher performance ratings.

The effects of performance on promotion are mostly consistent across segments, but strongest in Networking and Telecommunications. Interestingly, employees working in Internet/E-Commerce segments are 1% more likely to quit as top performers, suggesting a unique performance-retention relationship in this segment.

-4%

Top performer
(vs. not)

-6%

Recently promoted
(vs. not)

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Like the performance rating effect on turnover probability, the promotion effect on quit probability is generally consistent across segments and similar to the overall trend.
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Base Pay

Quit probability decreases as base pay increases: every 1% increase in base pay is associated with 3% decrease in quit probability, all else being equal (including the employee’s career level). It may be obvious, but the statistics have quantified the relationship: higher base salaries — all else equal — do retain employees in the technology industry.

The pay effect on turnover probability is stronger in Networking and ‘Other’ segments. Another noteworthy pattern observed is that employees are more likely to quit after a pay bump in Professional Services and Telecommunications.

Due to data limitations of employees who have left employers, we could not investigate the impact of short-term incentives (STI) or long-term incentives (LTI) on turnover. Future research will consider the impacts of these potentially important predictors.

Gender

There is not much difference in quit probability by gender, either in magnitude or statistical significance — but women do tend to be clustered in lower levels of the organizations. Women are slightly more likely to quit in Computer/Hardware and Internet/e-commerce segments, but by less than 1%.

Function

Admin, support, and research jobs have more frequent quits than operations or professional jobs, and the effects are generally consistent across segments.

Hourly status

Hourly employees are more likely to quit by 1% than exempt employees, though this finding is mostly statistically insignificant. Looking at different segments, non-exempt employees in Telecommunications are slightly more likely to quit than overall, and Internet/e-commerce employees are slightly less likely. 

Level

Higher-level employees are more likely to leave, along with lower support levels. This effect is generally consistent in most segments, but most level effects on quit probability are not statistically significant in the Consumer Technology, Telecommunications, and Electronics segments.

Tips to retain tech talent

Understanding the reasons why employees leave can be challenging. It often takes a combination of an analytical approach along with understanding the employee experience. Below we have some tips and groups to focus retention efforts on:
  1. Focus on retaining employees with between two and five years of tenure, after which their retention likelihood increases.
  2. Use high performance ratings and promotions as signals of an employee’s value to the organization.
  3. Acknowledge (and plan for) that turnover is likely to be higher for employees younger than 30, technology industry segments such as Networking and Telecommunications, and locations such as Seattle/Tacoma, Austin, and the North Carolina Research Triangle Park.

Contributors

    Find out what drives turnover at your organization — and why

    Though many of the overall trends in turnover drivers are mostly consistent — with some small areas of divergence — there are most likely differences in root cause at the organizational level. Now that you know the turnover drivers across industries, it’s time to figure out what’s happening at your specific organization. 

    Mercer can provide analytics on why people are leaving at your organization and help you understand your unique challenges, turnover drivers, and the employee experience. Complete the form to speak with a specialist.