Acquiring high performing talent is often seen as a difficult challenge because of the number of factors, both controllable and uncontrollable, that need to align at the time when you need to hire. This includes both macro factors, such as supply and demand forces that influence the availability and pricing of talent, and micro factors, such as your attractiveness to talent (employer value proposition) and the precision and accuracy of your recruitment process (talent selection process). Given the complexity of this challenge, it’s not surprising that we often feel that hiring is (and it often is) reactive and intuitive versus strategic and scientific. That is, we feel stuck “taking and filtering” what the market gives us versus “seeking and finding” the talent we need.

A large part of the problem is that we see the challenge of hiring as one of “prediction.”  We try to improve our odds of finding the needles in the haystack by investing in access to better candidate pools, taking a more structured / data driven approach to hiring to reduce bias, and/or buying talent with a successful track record. However, despite these seemingly better ways of improving our odds – hiring still seems like a game of chance. And it largely is! For example, despite narrowing candidate pools based on university and education, there is no evidence to suggest that these factors significantly predict performance. In one study, the educational background of a CEO was compared against the financial performance of the firm – and there was no significant evidence to show that the type or selectivity of the education of the CEO is related to the firm’s financial performance (Gottesman A.A. and Morey, M.R., 2006). The best predictor of performance is cognitive ability which explains about 40% of the variance in performance outcomes (Schmidt et al. 2016). This number increases to 58% when you combine cognitive ability tests with structured interviews - which still leaves a lot to chance. Lastly, stars in one environment may lose their luster in another environment. An analysis of roughly 1000 top research analysts (as ranked by institutional investor magazine) at 78 investment banks found 46% of them of did poorly in the year after they left one company for another. Their average performance plummeting by 20% and did not climb to prior levels even 5 years later (Nanda A. and Nohria N, 2014).

Consequently, we at HUELLA view hiring as a challenge of “positioning” instead of “prediction.” Like a good general manager building a sports team, we focus on filling key competency gaps today to deliver on immediate needs, but also ensure that you have the right talents and abilities to position you for strength across multiple outcomes in the future. We do this by:

• Deconstructing the role into key competencies and ranking them based on immediate strategic priorities and future outcomes. Competencies are the behavioral outcomes of the knowledge and skills that drive performance in a role. Variability in a competency is generally driven by experience, abilities (physical, cognitive, personality), and motivations. Some of these competencies will have greater importance to the organization because they provide strategic value (potential to improve the efficiency and effectiveness of the firm, exploit market opportunities, and/or neutralize potential threats) and/or are scarce (rare, specialized, or firm-specific). We systematically and mathematically rank and weight each competency across these two attributes to inform candidate pools (“where to fish?”) and focus selection criteria (“how to fish?”)

• Hiring for slope instead of the y-intercept. Leveraging a concept developed by John Ousterhout, Computer Science professor at Stanford – how fast you learn (slope) is more important than how much you begin with (y-intercept). The latter will always catch up and surpass the former over time. Many companies often hire based on experience (y-intercept), however as we saw above with the all-star analysts – you may be catching someone (and overpaying) when they’ve maxed out. We instead focus candidate abilities (cognitive, personality, and thought patterns and beliefs) and candidate motivators (personal and career goals, values, lifestyle preferences, stress tolerance).

• Designing customized contracting and compensation strategies to align value (individual and role) and risk (business and regulatory) to strategic business objectives. Many companies use salary guides and legal templates to inform how they price and manage risk for their talent. It’s not surprising that this often one-size-fits all approach leads to “nightmarish” situations – overvaluing talent driving a gap between expectation and outcome or undervaluing talent leading to turnover, losing IP to competitors, employment standard act violations etc.

Dhushan Thevarajah is the Founder and CEO of the Human Elevation Lab ("HUELLA"). A company that was borne out of Dhushan's own journey to elevate and reach his potential - both physically and mentally. He saw how simple shifts in his mental models and leveraging technology to make the "invisible" visible helped him achieve faster and further.  Combining this experience with his work experience of building and leading teams, designing decision tools to help model and inform choices, and fighting the daily fires across the functions of finance, HR, operations, and strategy of a growing company, Dhushan saw an opportunity to support companies shift their financial and mental frameworks and apply better decision tools for their talent acquisition and growth strategies. In turn, helping workforces reach their maximum potential.

Previously, Dhushan was the Chief Strategy Officer and Chief Operating Officer at BEworks, the world’s first commercial consulting team dedicated to the application of Behavioral Economics to real-world challenges. His blended interest in behavioral science and data analytics brought a more evidence-based and metric-driven approach to these roles. Dhushan also led multiple consulting projects in the banking, insurance, loyalty, and software industry, tackling diverse behavioral problems such as closing the gap between consumer intentions and actions, designing incentives and measures to drive key behaviors, and building training programs to optimize for learning and sustainability. Dhushan holds an M.Sc. from Queen’s University, where he studied and published work on the neural basis of strategic decision-making and holds an MBA from the Rotman School of Management.