How data helps you understand applicant requirements better!
A well-founded Requirements analysis According to DIN 33430, forms the basis of a successful and professional Staff selection. Within the framework of these, criteria for professional success are defined. The central central question of a requirements analysis is therefore: What does an ideal applicant have to bring with him in order to be successful in the vacant position?
Why is requirements analysis so important?
If you want applicants who actually fit, you have to know and say exactly what you are looking for. The basis for this is a well-founded and scientifically oriented personnel selection. A well-founded, data-driven requirements analysis is therefore the basis for good personnel selection.
If you don't know where you want to go, you shouldn't be surprised when you arrive somewhere else. (Mark Twain).
But how does such a requirement analysis actually work?
Requirements analysis primarily distinguishes between two methods: experience-guided, intuitive methods and person-empirical methods.
- Experience-guided intuitive methods
Experience-guided intuitive methods rely, as the name suggests, on experience-based or intuition-based judgments from experts. These are usually experienced HR managers or managers from the specialist sector. It can often also make sense to include previous employees. The requirement profile is
- Personal-empirical methods
Personal-empirical methods use statistical relationships between the characteristics of workers and their professional performance to derive requirement profiles. example: If you observe that sales employees achieve their sales goals particularly frequently with a high degree of empathy, empathy would be included in the requirement profile. The aim is that the profile always contains exactly the requirements that best predict professional success.
Comparing the methods
The advantage of experience-based methods is their ease of implementation. In practice, such a requirements analysis is often completed after just two to three joint agreements between managers and HR managers. But the appearance of simplicity is deceptive — because the method also entails several problems that reduce the efficiency and effectiveness of personnel selection, particularly in the long term. Because such are common internal agreements misleading and result in the definition of requirements that are no longer directly related to professional success in the position. Let's take a closer look at each of the issues of the procedure.
Issue 1: Distortions due to subjective perceptions of experts
Requirement profiles should be objective, reliable and valid (Read more about test quality criteria here). An important prerequisite for this is that different experts identify identical or at least very similar requirements when analyzing a new vacancy. However, this is rarely the case with experience-based requirements analyses. The reason for this is that experts are often strongly influenced by their own previous experiences and subjective perceptions.
An example:
Executives and HR managers tend to Name competencies that they believe they themselves have.
The episode:
The result is a subjective and often distorted picture of the requirements placed on applicants — the requirements analysis is therefore not objective.
How empirical methods solve the problem:
This is where one of the key advantages of empirical methods comes into play. This is because the technology-based and statistical creation of requirement profiles, which makes use of the latest findings in the area of machine learning, opens up numerous sources of error through subjective perceptions reduce — the objectivity of the requirements analysis increases.
Issue 2: Looking at the past instead of consistently focusing on the future
Another problem with experience-based methods is that experts often do not notice changes in the required competencies over time or only do so late. Requirements that were suitable for selecting applicants in the past (e.g. those in previous job advertisements) are often transferred to the future. How relevant these competencies actually (still) are for the advertised position is rarely questioned anymore.
An example:
The requirement good grades in school and studies Bring along, is copied into job profiles without reflection. However, good grades alone no longer reliably predict professional success in a dynamic working world.
The episode:
Requirement profiles are based on the past and not on the future. This is particularly problematic when requirements change rapidly and continuously — the reality in an increasingly dynamic working world.
How empirical methods solve the problem:
Empirical methods continuously measure correlations between professional success and competencies and personality traits from applicants and employees. As a result, even minimal changes in requirements can be identified quickly and reliably. Another advantage: The more data is available, the better the algorithm becomes. As a result, not only is the algorithm self-learning, but HR managers are also learning more and more about the requirements that should be placed on ideal applicants.
Issue 3: The search for the egg-laying woolly milk sow
HR managers and managers often base their requirements analysis on job advertisements from competing companies. Companies often seem to want to exceed themselves in the variety and complexity of the requirements mentioned above. This results in job advertisements with a colorful collection of competencies that an individual applicant alone is often no longer able to possess.
The importance of the criteria (see must, can and target criteria) is only rarely weighted.
An example:
Companies are looking for young, agile people with extensive professional experience and yet the courage to “question the status quo.” However, gaps in the curriculum vitae or a curriculum vitae that is not entirely straightforward, as the status quo has been questioned in the short term, are of course still not allowed.
The episode:
One standard phrase follows another. However, what HR managers actually mean by the multitude of listed competencies and how they want to check this in the personnel selection process is often less clearly defined. If the “egg-laying woolly milk sow”, which meets all requirements, cannot be found, especially in times of a shortage of skilled workers, the requirement profiles often recede into the background. The person who seems to deviate the least from the ideal profile, or simply the person who, according to the well-known “gut feeling”, fits best into the company is therefore selected.
How empirical methods solve the problem:
The result of empirical analyses is a few but central and specifically defined requirements for applicants. These requirements are demonstrably linked to professional success in the position and can be achieved through aptitude diagnostic methods (e.g. playful psychometric mini-games) measure directly. Depending on the strength of the relationship between individual competencies and professional success, the significance of the criteria can also be weighted (classification in mandatory, target and optional criteria).
Conclusion:
Especially in increasingly dynamic work environments, it will no longer be enough to rely solely on intuition and previous experience. Instead, enable Methods in the area of machine learning a data-driven determination of requirement profiles. This makes it possible to create more objective, precise and future-oriented requirement profiles, which is in the sense of a continuous improvement process constantly develop.
The benefits of a requirements analysis with Aivy
- Objectivity instead of subjective perceptions
- Focus on the future rather than on the past
- Precision of fewer central requirements, instead of long lists of blanket phrases
