While emerging companies are often started by technically minded founders and funded by VCs for their data-driven approaches to product and growth, the irony is that these companies are often using less data and rigor when it comes to hiring talent than more traditional, less data-focused companies. The truth is, the way in which tech companies hire has been relatively untouched by disruption, with most still relying on resumes and conversational interviews for its highest-stake decisions.

The consequences of this is not only detrimental to building teams, but to the overall diversity of the startup space.

Data-driven hiring isn’t just about having the right funnel metrics in place to determine efficiency of process, it extends to the information we choose to collect (or not collect) and measure to determine if someone is a fit for a role. There’s a science to building teams, and therefore selecting talent to join teams. So, why is hiring in early-stage companies still not regarded as a data-driven activity?

Some argue that by nature, talent selection involves people and so can’t truly be scientific. People are unique, complex, emotional and unpredictable. Additionally, few people think they’re a bad judge of character and talent, most overconfidently hold the belief that they’ve got a superior instinct and “nose” for talent. Hiring talent is one of the few operational activities in business where formal training or decades of experience isn’t expected in order to be better than average.

Move away from gut-based evaluations

The impact of this outdated way of thinking is felt across the board — first and foremost when it comes to team dynamics. To first know if someone is qualified, you need to know what you’re assessing for. Companies that operate with a shallow understanding of what drives success in a role lack the vital information needed to build a strong system of selection. The output is a weak hiring process that is heavy on unstructured interviewing, light on predictive signals and relies on gut-based evaluations.

Chemistry, confidence and charisma are more likely to determine whether a candidate lands a role versus competence to do the job. As a result, almost half of new hires are estimated to fail and be ineffective, and weak teams are built. The lack of reliable data also means most companies suffer from a broken feedback loop between hiring and team performance, which stunts learning and improvement. How do you know if your selection process is efficiently assessing for the skills, traits and behaviors that drive top performance if you’re not connecting the dots?

The dangers of subjective approaches

More dangerously, a hiring process that’s not designed to collect and evaluate based on evidence almost always results in a lack of team diversity, which as we know stunts innovation and therefore limits company success.

Subjective approaches to talent selection and development create a revolving door of unconscious biases and exclusion, with a resounding impact on what now makes up the homogenous tech ecosystem. This is not helped by natural overreliance on networks as means to fill hiring pipelines in early-stage company building.

Lastly, for talent operators and people practitioners, it does no favors for the credibility of their profession. Recruiting and selecting talent will continue to be branded an unsophisticated, lesser back-office function, or as a “dark art” that is about as data-informed as looking into a crystal ball.

Taking an evidence-based approach

In bringing more objectivity to the hiring process, founders and their teams are served best when starting with a clear, evidence-based definition of what success markers look like in a role, and then putting structure around each stage of selection to assess for a specific skill or behavioral trait: What and when will you assess? What criteria will you evaluate the data based on? In other words, the objective is to get as close as possible to unearthing signals that are reliable enough to accurately predict that someone will perform in a role.

Up until recently, science-based talent assessment tools, which help hiring managers make more objective evaluations, have been largely used by bigger, more established firms that suffer from high-volumes of job applications — the luxury “Google” problem. However, three recent shifts suggest we’re about to see a trend in their adoption by earlier-stage startups as they scale their teams:

1. Pressure to build diverse and inclusive teams. 2020 has pushed diversity and inclusion to the top of the agenda for most companies. Assessment tools used as part of team-building can help groups better identify where specific cognitive, personality and skill gaps exist, and therefore focus hiring for those missing ingredients. Candidate assessment also helps reduce unconscious bias that might creep into interviews by showing more objective information about someone’s strengths and weaknesses.

2. The sharp rise in job applicants. The COVID-19 pandemic has had two significant effects on recruiting. First, companies have been forced to embrace hiring talent in remote roles, which has increased the size of the global talent pool for most jobs inside a tech firm. Second, the increase in available talent has meant that the average number of job applications has risen dramatically. This shift from a candidate-driven market to an employer-driven one means that selecting signal from noise is increasingly becoming a challenge even for early companies with a less-established talent brand.

3. Better designed, more affordable products on the market. For a long time, talent assessment software has been largely inaccessible to noncorporate clients. Academic user interfaces and off-putting candidate experiences has meant that many scientifically robust tools simply haven’t been able to capture the attention of tech and product-obsessed buyers. Additionally, many tools that require add-on consultancy or specialist training to administer and interpret are simply out of range of early-stage budgets. With new entrants to the assessment market that have automation, product design and compliance at their core, scale-ups will be able to justify spending in this area and perceptions will change as they become essential SaaS products in their team’s operating toolkits.

As these outside factors continue to push hiring toward a more evidence-based approach, businesses must prioritize making these changes to their hiring practices. While unstructured interviews might feel most natural, they’re perilous for accurate talent selection and while the conversation might be nice, they create noise that does nothing for making smart, accurate decisions based on what really matters.

Instinctive feelings and “going with your gut” in hiring should be treated with caution and decisions should always be based on role-relevant evidence you pinpoint. Emerging companies looking to set a strong team foundation shouldn’t risk the redundancies and biases created by subjective hiring decisions.

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