The current labor market is a gold mine of talent for startups
The whiplash we've seen in the labor market over the last few years has created an environment that startups can and should take advantage of.
Big tech cutting back on hiring is an opportunity for startups
Once seemingly unstoppable, big tech is now in reset mode.
We’re not talking about Snap’s earnings sending its stock plunging, or Twitter’s lackluster earnings report from earlier this morning. No, we’re talking about big tech. The world’s largest tech companies are pulling back in a way that could, perhaps, clear some brush for startups still making their way through the wild (the private markets) towards the promised land (the public markets).
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This is good news for smaller companies, which were long irked at the sheer amount of cash the Googles and Microsofts of the world could throw at potential hires, some of whom came from smaller, more financially-constrained startups.
But as with most good news, there’s a catch.
Growth cheat code: Use fractional hiring to stay on plan when cutting costs
Venture funds are clear: Given the uncertainty of the coming few years as the Fed seeks to unwind its decades-long monetary policy, the mandate for CEOs is to:
- Cut burn.
- Slow growth.
- Carefully manage towards profitability.
This is a tough pill to swallow for founders who were planning to accelerate growth this year. Open Twitter and you’ll find a cacophony of founders, investors and advisers doling out advice for what to do next: downsize your product offerings; freeze all hiring; consider mass layoffs.
The fact is, you can indeed cut burn and manage toward profitability while still defaulting to growth. In fact, that’s how the winners of this downturn will pull ahead.
To manage their huge levels of risk, large companies must freeze hiring. If you’re an entrepreneur, this is good news for you.
So what does that look like?
Fractional hiring is a growth cheat code
We’ve been operating as a bootstrapped business for close to a decade, so we’re familiar with forecasting budgets around very conservative scenarios and adjusting within 30-day or 90-day windows. This has allowed us to not only stay profitable but be nimble as well. When faced with economic chaos in March 2020, we maintained our growth rate by quickly adjusting budgets.
Instead of pausing hiring and delaying our team’s ability to execute, we employ a fractional model for hiring. As we’ve scaled headcount over the years, we’ve always tried to bring on key people first as (typically, part-time) contractors, and then convert them to full-time employees.
6 methods for reducing bias in candidate sourcing and screening
Over the last several years, an increasing number of companies have pledged to hire a more diverse workforce and begun releasing their diversity numbers annually. The results have been a mixed bag at best.
With so many organizations saying that diversity hiring is among their top goals and making good-faith efforts to revamp their recruiting practices accordingly, our team wanted to better understand why the results have fallen short. What we found surprised us: Subconscious bias tends to have the strongest impact on historically underrepresented racial and ethnic groups in the early stages of the interview process.
For example, the data revealed that while white candidates see higher passthrough rates at the very top of the funnel, Black and Hispanic/Latinx talent see higher passthrough rates across the remaining funnel stages: 62% of Black talent and 57% of Hispanic/Latinx talent are extended offers after on-sites, compared to just 54% of white talent.
This suggests that diversity is most often an issue in earlier stages of the interview process, driven at least in part by subconscious bias. Candidates from historically underrepresented racial and ethnic groups have to work harder to prove themselves than their white counterparts, despite seeing higher offer rates at later stages of the interview process.
Whenever you open a new role, start by asking the question: How do we ensure that our selection is based solely on criteria that’s relevant to the role?
To help address this issue, I’m sharing six strategies that recruiting teams can use to reduce bias in the early phases of the recruiting process, when candidates are both entering and progressing through interviews.
Rethink the criteria for your open roles
Research has found that many things people list on their LinkedIn profile or résumé have very little, if any, correlation with their future work performance.
For example, requiring or being predisposed to four-year degrees from certain institutions biases you toward privilege. Screening for leadership experience can also be racially biased, due to lower representation of non-white people at the executive level.
To avoid this, whenever you open a new role, start by asking the question: How do we ensure that our selection is based solely on criteria that’s relevant to the role?
From there, clarify which competencies and qualifications are absolutely necessary to success in the role, and rather than focusing on the candidate’s experience, education, or — if they’re early in their careers — GPAs, ask yourself what about their history suggests problem-solving skills, cognitive ability and a growth mindset.
Limit access to information that could cause bias
6 methods for reducing bias in candidate sourcing and screening
Over the last several years, an increasing number of companies have pledged to hire a more diverse workforce and begun releasing their diversity numbers annually. The results have been a mixed bag at best.
With so many organizations saying that diversity hiring is among their top goals and making good-faith efforts to revamp their recruiting practices accordingly, our team wanted to better understand why the results have fallen short. What we found surprised us: Subconscious bias tends to have the strongest impact on historically underrepresented racial and ethnic groups in the early stages of the interview process.
For example, the data revealed that while white candidates see higher passthrough rates at the very top of the funnel, Black and Hispanic/Latinx talent see higher passthrough rates across the remaining funnel stages: 62% of Black talent and 57% of Hispanic/Latinx talent are extended offers after on-sites, compared to just 54% of white talent.
This suggests that diversity is most often an issue in earlier stages of the interview process, driven at least in part by subconscious bias. Candidates from historically underrepresented racial and ethnic groups have to work harder to prove themselves than their white counterparts, despite seeing higher offer rates at later stages of the interview process.
Whenever you open a new role, start by asking the question: How do we ensure that our selection is based solely on criteria that’s relevant to the role?
To help address this issue, I’m sharing six strategies that recruiting teams can use to reduce bias in the early phases of the recruiting process, when candidates are both entering and progressing through interviews.
Rethink the criteria for your open roles
Research has found that many things people list on their LinkedIn profile or résumé have very little, if any, correlation with their future work performance.
For example, requiring or being predisposed to four-year degrees from certain institutions biases you toward privilege. Screening for leadership experience can also be racially biased, due to lower representation of non-white people at the executive level.
To avoid this, whenever you open a new role, start by asking the question: How do we ensure that our selection is based solely on criteria that’s relevant to the role?
From there, clarify which competencies and qualifications are absolutely necessary to success in the role, and rather than focusing on the candidate’s experience, education, or — if they’re early in their careers — GPAs, ask yourself what about their history suggests problem-solving skills, cognitive ability and a growth mindset.
Limit access to information that could cause bias
Emerging companies thrive on data. Shouldn’t they use it to improve hiring decisions?
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:
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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.
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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.
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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.