Applying Data to the Hiring Process– Separating a good hire from a bad hire requires more than gut instinct

June 9, 2022

Hiring is one of the biggest challenges facing employers today.

According to Robin Eichert, founder of PeopleSense Consulting, among the many obstacles within the hiring process is the ability to discern a good hire from a bad hire. Good hires manifest themselves in workers who make a positive contribution to the organization in the role they are asked to play. Bad hires have many incarnations, but most commonly result in the employment of those who are simply a poor match for the responsibilities of the job they were hired to do. This is why Eichert, and many others in the recruitment, retention and talent management space are strong advocates for applying a data-driven approach to the hiring process.

“Data changes the hiring process by validating some of the behaviors discussed in an interview,” says Eichert. “There are good tools that provide a scientific foundation that help employers better evaluate strengths, workstyle, interests; all of which help remove some of the guesswork. Pairing the gut instinct of a practiced hiring manager with good data makes for better hiring.”

Eichert’s organization uses PXT Select, an assessment tool she says helps provide a foundation to assess the whole candidate. These types of tools, of which there are many, offer agencies of all sizes the benefit of applying proven data metrics to the screening process of prospective employees. For agents and brokers looking to hire, Eichert suggests three key data points are crucial: cognitive, behavioral and interests.

How someone moves through a job assignment offers insight into their cognitive abilities and style, allowing for a more practical comparison to the skills needed for the role in question. For example, an individual who requires extensive details or instructions on each step of a process may be better suited to certain job roles than someone who can gather basic principles and prefers to take a less linear approach to the task(s) at hand.

Someone who thrives in a highly structured, routine-driven environment might be better suited to a claims representative, adjuster or actuarial role versus that of an independent insurance agent for whom much of the work, client needs, and approach may vary from day to day. Similarly, someone who has a more introverted personality is less likely to be a match for an agent role that requires constant networking and social skills.

Based on her experience, Eichert has seen that job candidates with more technical interests tend to stay and thrive in the field of insurance. These candidates enjoy solving problems, exploring complex data areas, appreciate math or thrive in undertaking administrative tasks. People who have more creative interests (think painting, music, etc.), don’t tend to stay in the insurance workforce for long, likely because they aren’t drawn to the things that are as rewarding or satisfying as much as the creative interests they possess.

While there are exceptions to every rule, Eichert says these data-driven measurements, in combination with an agency’s existing employment practices and the experience of hiring managers, can contribute to overall better recruitment and, importantly, retention of talent.

The hiring process presents both hard and soft costs. Hard costs are easily quantified; however, the soft costs of a bad hire can be difficult to measure. The cost of damaged employee morale, lost time and potential negative impressions among clients and business partners can be, for many insurance agencies, unacceptably high. Applying a data-driven approach to the hiring process can help validate critical hiring decisions that shape both the agency and its bottom line.