Revolutionizing Talent Strategy: The Challenge of High Turnover in the Trucking Industry
- Trevor Higgs
- Jul 24
- 3 min read
Updated: Nov 2
Understanding the High Turnover Challenge
The trucking industry faces a significant challenge: high employee turnover. The demanding nature of this sector leads to constant attrition. This makes it difficult to maintain a stable and skilled workforce. High turnover results in increased recruiting costs, operational disruptions, and a diluted return on investment in training for trucking and transportation companies.
A leading transportation and logistics company, Trucking Edge, struggled to differentiate between candidates who would merely “fill a seat” and those who possessed the potential to succeed and build a long-term career. Without a reliable way to predict an employee's fit and resilience, their talent strategy was reactive. Preventable turnover was eroding their bottom line.
The Solution: Catalyzr's Career Quotient (CQ)
To tackle this challenge, Trucking Edge partnered with Catalyzr. This talent discovery platform combines the science of Industrial/Organizational (I/O) psychology with Artificial Intelligence (AI) to quantify human potential. Instead of relying solely on resumes and interviews, Trucking Edge implemented Catalyzr's Career Quotient (CQ) assessment.
A Streamlined Process
The process was straightforward:
Modeling Success: The company identified its most successful, long-tenured employees ("Exemplars") in key roles.
Assessing Potential: Catalyzr's assessments created a "Cognitive Fingerprint" for new hires and existing employees, measuring their General Mental Ability (GMA) and key work values.
Generating the CQ Score: By comparing an individual's Cognitive Fingerprint to the Exemplar Profiles, Catalyzr generated a CQ score for each employee. This score represents their innate potential to succeed in that specific role.
This allowed the company to make data-driven decisions, identifying individuals with a high CQ who were most likely to thrive, not just survive, in their roles.
The Results: A Drastic Reduction in Turnover and Risk
An analysis compared the retention rates of employees identified as having high potential, as measured by a high CQ score, with those hired without it. To focus on meaningful retention, the analysis excluded all terminations that occurred within the first five days of employment. It followed the employees from day five to day ninety of their employment. The results were clear and compelling.
Key Finding: 47.2% Reduction in Turnover
Employees with a high Catalyzr CQ score were 47.2% less likely to be terminated in the first 90 days than employees hired with low or no CQ scores.
Analysis of Employee Turnover
| Turnover Metric | With CQ Score | No CQ Score |
|------------------------------|---------------|-------------|
| Overall Turnover Rate | 12.50% | 23.68% |
| Voluntary Turnover Rate | 0.00% | 5.26% |
| Involuntary Turnover Rate | 12.50% | 18.42% |
Deeper Insights: The "Why" Behind the Numbers
The value of the CQ became even more apparent when analyzing the reasons for termination.
Zero Voluntary Turnover
The most significant finding was that not a single employee in the high CQ group chose to leave the company after the initial five-day period. This indicates a significantly higher level of job satisfaction and alignment for employees placed in roles where they have a high potential for success.
Lower-Risk Involuntary Terminations
While the involuntary turnover rate for the CQ group was lower, the qualitative difference was profound.
Terminations in the CQ group were primarily due to lower-risk issues, such as attendance and communication failures.
Terminations in the non-CQ group included critical, high-risk infractions such as a failed drug screen, a safety violation, and documented performance failures.
Conclusion: Building a More Stable and Committed Workforce
By implementing Catalyzr's Career Quotient, Trucking Edge moved beyond guesswork. They made hiring and development decisions based on a scientific measure of potential. This data-driven approach enabled them to significantly de-risk their talent strategy.
The results demonstrate that identifying employees with high potential for success leads to drastically lower overall turnover. It also fosters a more stable, committed workforce that is less prone to voluntary departures and high-risk behaviors.
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