As employers increasingly rely on pre-hire assessments to screen frontline candidates, a new study from HiringBranch challenges the conventional practice of reporting soft skills as separate scores. The research, discussed in the latest episode of the podcast You Should Know, suggests that evaluating empathy, active listening, acknowledgment, and reassurance in isolation may actually weaken the predictive power of hiring assessments.
Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, joined host William Tincup to delve into the findings. The Montreal-based company's study indicates that single-skill scoring produces only moderate correlation with human annotators, while a combined proprietary model yields much stronger correlations. This has significant implications for how companies hire for customer-facing roles, from retail associates to sales agents.
Bar-Moshe explained that their assessment method uses open-ended, voice-and-writing exercises to measure four pillars of customer service: acknowledgment, reassurance through positive language, empathy, and active listening. The assessments are scenario-based, translating job descriptions into conversation flows calibrated per client, region, and role. This approach aims to capture how candidates actually handle live customer interactions, rather than evaluating traits in a vacuum.
The conversation highlighted a concrete example: a retail confrontation over a mispriced item that turned on diplomacy rather than policy. Bar-Moshe emphasized that a candidate who can express empathy but cannot solve the issue or reassure the customer is not effective. "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.
HiringBranch's method is linguistically driven, not personality-based. Bar-Moshe, a trained linguist, described it as a "sociopragmatic analysis of the words that the candidate is actually saying." The company's team of IO psychologists and linguists uses years of textual data to build machine learning models that predict empathy and related skills, then validates those predictions against on-the-job performance months after hire. Calibrations differ by client, industry, and even geography, with regional variations across markets like Vancouver, Toronto, and Montreal shaping scoring weights for the same role.
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, titled "Assessing Skills One at a Time Is Costing You Better Hires," is part of the WRKdefined Podcast Network and is available on the You Should Know Podcast page. As the conversation around frontline hiring evolves, this research underscores the importance of holistic assessment in identifying candidates who can truly excel in customer-facing roles.
