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How predictive analytics work in aviation recruitment

Traditional recruitment methods, often reliant on subjective assessments and lengthy processes, have recently proven insufficient in identifying candidates who can meet the industry’s evolving needs. However, aviation recruitment is slowly embracing the power of predictive analytics. By beginning to use vast datasets, aviation recruiters are now equipped with innovative solutions to forecast candidate performance and align talent with organizational goals more effectively than ever before. 

In this article, Jainita Hogervorst, Director of Aerviva Aviation Consultancy, a company based in Dubai specializing in aviation recruitment and document management, explores the transformative impact of predictive analytics in recruitment and discusses how data-driven insights and predictive models are reshaping the career landscape. 

What impact is predictive analytics having on the industry? 

Predictive analytics is nothing new to the aviation industry. It already plays an integral role in flight planning, maintenance operations and fleet management.  It involves the use of historical data and advanced algorithms to anticipate future outcomes.  

And this approach is now being embraced by recruiters in the aviation sector. By leveraging vast datasets encompassing candidate attributes, job performance metrics, and industry trends, predictive models can effectively forecast candidate performance with a high degree of accuracy. 

According to “The New Science of Sales Recruiting,” created by Ideal, automating the top of the recruitment funnel to screen and shortlist candidates saves up to 57% of the time spent looking for a single person. “Predictive analytics in recruitment has brought about significant efficiency gains by streamlining the candidate selection process and optimizing resource allocation. Predictive analytics in recruitment begins with leveraging historical data. Then advanced algorithms help recruiters identify top talent that aligns closely with their organizational needs. This targeted approach minimizes the time and resources traditionally expended on manual candidate screening and selection processes,” says Hogervorst. 

However, Hogervorst mentions that this approach not only streamlines candidate selection processes but also minimizes biases inherent in traditional recruitment methods. “Predictive analytics enhances retention rates by identifying candidates whose skills and characteristics align closely with organizational needs and culture. And it does this in a more subjective way, identifying attributes in a candidate irrespective of factors like gender, race or nationality.” 

Passive recruitment is one recruitment approach that could interest aviation companies. This is where predictive analytics is used to identify potential applicants who are not actively looking for a job. In the context of aviation, HR teams could focus on mechanic and technician positions. In 2024, demand for Aircraft mechanics and service technicians is set to increase by 12% in the industry (according to V7 recruitment), while a 10% jump in demand for Avionic technicians is expected. Using predictive analytics to approach candidates with appropriate transferable skills who aren’t actively considering a role in aviation could be an effective way to plug this gap. 

How can predictive analytics help HR teams choose the right person? 

Identifying high-potential candidates always requires a multifaceted approach that considers not only technical skills but also soft skills and cultural fit. Recruiters often look for candidates with a strong foundation in relevant education and experience, coupled with qualities such as adaptability, problem-solving abilities, and leadership potential. According to Hogervorst, predictive analytics plays a pivotal role in this process by systematically evaluating candidate attributes against predefined criteria.  

“By analyzing vast datasets encompassing past recruitment successes and organizational performance indicators, predictive models can identify patterns indicative of high-potential candidates. These models assess various factors, including candidate skills, experience levels, and cultural fit, to predict the likelihood of success in a given role. Through the use of predictive analytics, recruiters can showcase examples of candidates who not only meet but exceed organizational goals, demonstrating the effectiveness of this approach in identifying talent that aligns closely with the company’s objectives and values,” says the Director of Aerviva Aviation Consultancy. 

Furthermore, predictive analytics revolutionizes the recruitment process by providing recruiters with insights into candidates’ potential success in specific roles. By analyzing historical data on candidate attributes, job performance metrics, and organizational outcomes, predictive models can effectively forecast how candidates are likely to perform in the future. For example, Google uses interview questions that are fully automated and synced with a predictive analytics system. This system not only helps them to find the ideal candidates, but also helps the company predict who ay be more likely to leave the company and when. Another example is X (formerly Twitter), which leverages predictive analytics to determine the characteristics of high-performing individuals.  

“These models take into account various factors influencing job success, including technical skills, soft skills, past experiences, and cultural fit. Sources for this data include platforms like LinkedIn, or resumes shared online, plus information gathered during the recruitment process like answers to interview questions. Aviation companies could also define specific projects that they want experience working in, or, in the case of maintenance, specific models that they have worked on. By weighing these factors against predetermined criteria, predictive analytics can generate highly accurate predictions of candidate performance,” explains Hogervorst. 

What does the future hold? 

According to a 2022 SkyQuest Technology survey, the use of predictive analytics has grown by almost 50 percent in the last three years. This might suggest that, looking ahead, the future of predictive analytics in recruitment is poised for continued evolution, driven by emerging trends in technology. This is especially important given the positive effects that recruitment using analytic tools is bringing to the organizations. 

“By enhancing recruitment efficiency and promoting better talent-fit, predictive analytics not only saves time and resources but also contributes to long-term organizational success and sustainability in the aviation industry. Additionally, predictive analytics enhances the alignment between candidate skills and organizational requirements, leading to improved satisfaction and retention. When candidates are selected based on their potential to excel in specific roles, they are more likely to feel fulfilled and engaged in their work. This, in turn, fosters a positive work environment and reduces turnover rates within the organization,” says Hogervorst. 

However, along with these advancements come challenges such as data privacy concerns and algorithm bias. The Director of Aerviva Aviation Consultancy mentions that in order to address these challenges, continuous innovation and adaptation are essential.  

Ultimately, while there are challenges to be addressed, the benefits outweigh the risks according to Hogervorst. With the potential to reduce the time spent finding candidates, ensure a good fit between candidate and company, minimise bias, and facilitate long-term improvements in retention, predictive analytics in aviation recruitment is here to stay. 

The post How predictive analytics work in aviation recruitment appeared first on AeroTime.

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