As employers grapple with the complexities of evaluating soft skills in frontline candidates, new research from HiringBranch suggests that the common practice of reporting empathy, acknowledgment, active listening, and reassurance as isolated scores may be undermining hiring decisions. The findings were discussed in a recent episode of the podcast You Should Know, hosted by William Tincup, featuring Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch. The episode, titled Assessing Skills One at a Time Is Costing You Better Hires, was published on August 26, 2026, and highlights the importance of a holistic approach to measuring interpersonal abilities in customer-facing roles.
Bar-Moshe explained that single-skill scoring shows only moderate correlation with human annotators, whereas a combined proprietary model yields much stronger correlations. This insight is critical for industries such as retail, customer service, and sales, where live interactions require a blend of skills rather than isolated traits. He illustrated this with a retail scenario involving a customer complaint about mispriced broccoli, where the resolution hinged on diplomacy rather than mere policy adherence. "If a candidate can express empathy, but is unable to solve the issue correctly or to comprehend the issue correctly or to reassure the customer, then this empathy is nice, but it's actually useless," Bar-Moshe said.
HiringBranch's assessment design uses open-ended voice and writing prompts to capture candidates' natural language, which is then analyzed through a sociopragmatic lens. This linguistic approach, rather than a personality-based one, allows the company to measure four key pillars: acknowledgment, reassurance through positive language, empathy, and active listening. By translating job descriptions into conversation flows and scenario-based assessments calibrated per client, region, and role, HiringBranch ensures that evaluations are contextually relevant. The company's team of IO psychologists and linguists uses years of textual data to train machine learning models that predict these skills, validating them against on-the-job performance months after hire.
Regional differences also play a role, with markets like Vancouver, Toronto, and Montreal requiring different scoring weights for the same role. Bar-Moshe also previewed a self-serve capability in development that would allow hiring managers to build assessments from a library of conversation flows and skills, reducing reliance on weak or generic job descriptions. The full study will be available under the AI research tab on the HiringBranch website.
The implications of this research are significant for HR professionals and hiring managers who often rely on individual soft skill scores to filter candidates. By adopting a combined model, organizations can better predict which candidates will thrive in roles that demand nuanced communication and problem-solving. As the workforce evolves, this holistic approach may become a standard in pre-hire assessments.
