As employers grapple with the complexities of hiring frontline talent, a new study from HiringBranch suggests that evaluating soft skills in isolation may be undermining the accuracy of hiring predictions. The research, discussed in the latest episode of the podcast You Should Know, challenges the common practice of reporting empathy, acknowledgment, active listening, and reassurance as separate scores. Instead, it proposes a combined model that better captures the interplay of these skills in live customer interactions.
Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, joined host William Tincup to unpack the findings. The Montreal-based company, which specializes in voice-and-writing assessments, found that single-skill scoring produces only moderate correlation with human annotators, whereas a combined proprietary model yields much stronger correlations. This suggests that assessing skills in a siloed manner may not reflect how they are actually deployed in real-world scenarios.
The study centers on four pillars of customer service: acknowledgment, reassurance through positive language, empathy, and active listening. HiringBranch's assessments are designed as open-ended, scenario-based conversations that mimic actual customer interactions. Job descriptions are translated into conversation flows calibrated per client, region, and role. For instance, regional variations across markets like Vancouver, Toronto, and Montreal lead to different scoring weights for the same role, reflecting nuanced differences in communication norms.
Bar-Moshe, a trained linguist, explained that the company takes a linguistic rather than personality-based approach, describing it as a "sociopragmatic analysis of the words that the candidate is actually saying." This method involves analyzing years of textual data to build machine learning models that predict empathy and related skills, then validating those predictions against on-the-job performance months after hire.
The research underscores a critical insight: a candidate might express empathy but fail to solve the customer's issue or reassure them effectively. In such cases, empathy alone is insufficient. "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 during the podcast. This finding has significant implications for hiring managers who rely on isolated skill scores to make decisions.
The episode also touched on practical applications. Tincup recounted a retail confrontation over mispriced broccoli that hinged on diplomacy rather than policy, illustrating the importance of integrated skills. 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 is set to appear under the AI research tab on the HiringBranch website. As businesses continue to refine their hiring processes, this research offers a data-driven argument for a more holistic evaluation of soft skills, particularly for frontline positions where customer interaction is paramount.
