A new study from HiringBranch, a Montreal-based assessment company, suggests that evaluating soft skills such as empathy, active listening, and reassurance as isolated scores may undermine the accuracy of frontline hiring decisions. The research, discussed in the latest episode of the podcast You Should Know, challenges a common pre-employment screening practice and offers a more integrated alternative.
The episode, titled "Assessing Skills One at a Time Is Costing You Better Hires," was published on August 26, 2026, and hosted by William Tincup. It featured Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, who explained the implications of the company's findings for employers hiring customer service reps, sales agents, and retail associates.
According to Bar-Moshe, the study found that scoring individual soft skills separately yields only moderate correlation with human evaluators, whereas a combined proprietary model produces much stronger correlations. This suggests that focusing on isolated skills may not accurately predict a candidate's ability to handle real-world customer interactions.
Bar-Moshe emphasized that soft skills are interdependent in practice. "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," he said. This perspective underscores the need for a holistic assessment that captures how skills work together in dynamic situations.
HiringBranch's approach uses open-ended, voice-and-writing assessments that place candidates in conversation-based scenarios. The company measures four pillars of customer service: acknowledgment, reassurance through positive language, empathy, and active listening. These pillars are translated into conversation flows and scenario-based assessments calibrated per client, region, and role. For example, job descriptions are converted into specific dialogue scenarios that mimic actual customer interactions.
Bar-Moshe, a trained linguist, explained that HiringBranch employs a linguistic rather than personality-based methodology. "We take a sociopragmatic analysis of the words that the candidate is actually saying," he said. The company's team of IO psychologists and linguists uses years of textual data to build machine learning models that predict these skills, then validates the predictions against on-the-job performance months after hire.
The research also highlights regional variations in how skills are valued. Calibrations differ by client, industry, and even geography, with markets like Vancouver, Toronto, and Montreal shaping different scoring weights for the same role. This suggests that a one-size-fits-all scoring system may be inadequate.
Looking ahead, HiringBranch is developing a self-serve capability 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 episode is part of the You Should Know podcast, which is part of the WRKdefined Podcast Network and reaches more than 3.9 million verified listeners monthly. It is available on major podcast platforms and at the You Should Know Podcast page.
These findings come at a time when employers are rethinking how to measure frontline talent against the realities of live customer interactions. The implication is that relying on isolated skill scores may lead to suboptimal hiring decisions, while a more integrated approach could better identify candidates who can genuinely excel in customer-facing roles.
