Empower your HR teams to predict your candidates’ future performance.

Can your HR teams reliably predict the future performance of new hires?

Yes, now they can.

The final verdict after multiple rounds of assessments and interviews is still a human decision. While human involvement in this decision is not problematic, the basis of such decisions can have issues. Hiring is often based on a gut feeling about the candidates’ fit into the work culture of the organisation, their adaptability to the team they will work with, their personality characteristics, social dispositions, and their future performance. HR managers have also confessed of nesting unconscious biases and perceptions, that have made them reject many suitable candidates and hire quite a few unsuitable ones.

Can Technology Help?

Businesses today are operating under relentless competitive pressure. Unceasing innovation, digital transformation and employee adaptability to rapidly changing skill sets are becoming the norm. To stay relevant in this volatile, uncertain, complex and ambiguous environment, businesses are putting intense pressure on recruiters for swift-filling of their job vacancies with skilled and high-quality talent. Automation and AI technology for HR have enabled recruiters to sift through multiple CVs in no time. Also, online candidate application management system, assessments video interviews and candidate selection solutions have made the HR teams highly agile and productive. However, even the most advanced machine learning algorithms are unable to predict the future. They do not have an answer to the question, “Is this candidate future-ready?” Under these circumstances, an extremely agile and effective selection platform has become a necessity for HR teams– one that gives them an ultra-fast speed of selection as well as the assurance about their future performance.

It is critical for HR teams to understand that candidate experience is not limited to the perception of just the recruitment cycle, but every other aspect of the organisation. This includes employees personal growth and development, the work-culture, the attributes of employees, the core values of the company, its EVP and even the employer’s brand. In the job seeker’s market with a scarcity of good talent, companies are competing for the attention of the right candidates. The employer’s brand, engagement and the candidates’ hiring journey collectively contribute to candidates’ early perceptions of the organisation and are instrumental in their decision-making process throughout the hiring process.

Predictive Analysis in Hiring

Companies are leveraging new technologies, catalysed by machine learning and AI technology for HR to make data-driven hiring decisions. Hiring is being disrupted by candidate selection solutions, predictive talent, culture, and behavioural analytics, which empower HR teams to make the best possible decisions about their people based on attributes that are closely linked to the chances for future success in their job role.

Over the years, personality testing instruments have been leveraged by the HR teams that evaluate candidate’s personality traits, their cultural fit, and how would they respond in a given situation at the workplace. However, they brutally fail in predicting future performance. But this is the need of the hour as employees need to fit into future job profiles, making traits like curiosity, learnability and adaptability vital for being future-ready.

To ensure that their workforce is future-ready, HR managers wish to know which candidates in the hiring funnel will become top performers? Who has the greatest growth potential? Who could be an outlier? And, who is future-ready? But can predictive models answer these challenging questions?

Predictive models look for patterns in personality that predict job performance. For example, while hiring a salesperson you would expect someone who’s assertive, sociable, and has some emotional resilience (to name just a few personality traits). What’s interesting about the predictive model-building process is that it is able to assess countless combinations of patterns, often missed by the human eye.

Technology as a Soothsayer

The right tools to successfully predict future job performance can not only help your HR teams hire the right people but also can help you ensure that every hire is aligned to the future of your company and the requirements of the industry. This is where the human function of HR becomes even more so. The uniqueness of each candidate can be mapped to historical data that can help your HR teams predict future job performance without bias. However, since performing well in different jobs may require varied personality attributes, it is necessary to know the requirements of the specific job in which you are trying to predict the candidate’s success. To know the job requirements and decide which personality attributes are important for success, EasyTalent can take as input the organisation’s job analysis data.

However, it is seen that company generated data is often not exhaustive enough to make meaningful predictions about future performance. Even the data itself may have issues due to biases that have gone undetected over long periods of time. These are noticed only when teams begin to see patterns in their hiring practices that confirm such a defect.

Machine learning specialists at Amazon had to scrapped a four-year project after discovering a major drawback in a recruiting engine they had built. The engine was trained to vet candidates for software developer jobs based on patterns it found in resume data. However, the problem was that the resumes came mostly from men, because tech is a male-dominated industry. From this data, the engine learned that males were preferable, and it began excluding women from its recommendations. Even after making amends in their engine to correct the gender bias, there was no guarantee that it would not pick up other mechanisms of filtering candidates that were unfair. This is a classic case study about the limitations of machine learning and unintended gender bias threatening the integrity of Amazon’s hiring process and strategy.

At EasyTalent, we use the O*Net database which is the Occupational Information Network. It is one of the world’s most reliable data networks and contains hundreds of standardised and occupation-specific descriptors on almost 1,000 occupations, developed and continually updated by the labour department of the US Government. Once the candidates have taken the personality assessments, our AI engines map their attributes with the workstyles required to succeed at the job.

Predicting the Future Does Not Require a Crystal Ball

HR teams just need to granularly define the job family of the job position, not just the job-title and job-code of the job being created, but also the work styles.

Every person has a unique personality. Their personality characteristics determine how they behave in different situations. There are 16 personality characteristics (also known as work styles), which together influence how successful an employee would be in a job. The degree of influence that each of the individual personality characteristic (or work style) has on success varies from job to job. For example, the work style ‘social orientation’ (which indicates a person’s ease in networking with others) has a greater influence for success in sales jobs than in accounting. On the other hand, the work style ‘attention to detail’ has a greater influence for success in accounting jobs than in sales.

To help predict a candidate’s success in the job, EasyTalent uses its algorithm to automatically map the candidate’s personality (based on the Big Five framework, OCEAN), to the O*NET work styles for the job. It does this by quantifying how closely each of the candidate’s five personality dimensions (OCEAN-openness, conscientiousness, extraversion, agreeableness and neuroticism/emotional stability) aligns with each of the 16 work styles associated with that job. The closer the alignment, better is the success factor.

How It Works: Looking Inside the Crystal Ball

There are 16 personality characteristics (also known as work styles), which together influence how successful an employee would be in a job.

The 16 work styles are: achievement/effort, adaptability/flexibility, analytical thinking, attention to detail, concern for others, cooperation, dependability, independence, initiative, innovation, integrity, leadership, persistence, self-control, social-orientation and stress tolerance.

Defining work style rankings involves allocating to each of the work styles an importance level. A work style with a higher importance level has more influence on success in the job than a work style with lower importance.

To define the work style importance, the EasyTalent’s administrator selects the industry, job-family and occupation under which the job being created falls (these can be selected from drop down options in the Create Job screen).

EasyTalent then automatically maps the chosen industry/job-family/occupation to the O*Net database and displays the job’s 16 O*Net work styles ranked in order of their importance.

The administrator, can either accept the O*Net work style rankings for the job, or the administrator can enter their organisation-specific importance for the 16 work styles.

With all the datasets available to HR managers using EasyTalent, they do not require a crystal ball to predict candidates’ future performance. The ability to predict the future no longer just belongs to the soothsayers! With EasyTalent, your teams can make predictions that are not only more reliable but also can rank candidates on the basis of future performance.

Find out more about how EasyTalent can help your HR teams leverage AI and reliable historical data to make hiring decisions backed by the most relevant data.