As employers increasingly rely on pre-hire assessments to evaluate frontline talent, new research from HiringBranch challenges a common practice: reporting soft skills such as empathy, acknowledgment, active listening, and reassurance as isolated scores. The study, discussed in the latest episode of the podcast You Should Know, suggests that this approach weakens the predictive power of hiring assessments for customer-facing roles.
Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, appeared on the episode titled Assessing Skills One at a Time Is Costing You Better Hires, hosted by William Tincup and published on August 26, 2026. The Montreal-based company's research indicates that single-skill scoring shows only moderate correlation with human annotators, while a combined proprietary model produces much stronger correlations. This finding is particularly relevant as companies rethink how they measure candidates for positions like customer service representatives, sales agents, and retail associates, where live interactions are critical.
Bar-Moshe explained that HiringBranch uses an open-ended, voice-and-writing assessment design, taking a linguistic rather than personality-based approach. He described the method as a sociopragmatic analysis of the words that the candidate is actually saying. The company measures four pillars of customer service: acknowledgment, reassurance through positive language, empathy, and active listening. These skills are assessed through conversation flows and scenario-based tasks calibrated per client, region, and role.
The discussion highlighted a real-world example: a retail confrontation over mispriced broccoli that hinged on diplomacy rather than policy. Bar-Moshe emphasized the importance of combining skills, stating, "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."
HiringBranch's team of IO psychologists and linguists uses years of textual data to build machine learning models that predict these skills, validating them against on-the-job performance months after hire. Calibrations differ by client, industry, and geography, with regional variations across markets like Vancouver, Toronto, and Montreal shaping scoring weights for the same role. The company is also 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. This research underscores the importance of holistic evaluation in hiring, suggesting that a combined approach to scoring soft skills can lead to better hires and improved customer interactions.
