A new study from HiringBranch, a Montreal-based assessment company, suggests that evaluating soft skills as isolated scores may undermine the accuracy of frontline 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 metrics. Instead, the company advocates for a combined approach that better reflects the realities of live customer interactions.
Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, joined host William Tincup to delve into the findings. The study reveals that single-skill scoring yields only moderate correlation with human annotators, while a combined proprietary model produces significantly stronger correlations. This suggests that an over-reliance on isolated skill scores may lead to suboptimal hiring decisions, particularly for roles such as customer service representatives, sales agents, and retail associates.
The research is grounded in the four pillars of customer service that HiringBranch measures: acknowledgment, reassurance through positive language, empathy, and active listening. Bar-Moshe, a trained linguist, explained that the company employs a sociopragmatic analysis of candidate responses, focusing on the actual words used rather than personality traits. This linguistic approach, combined with machine learning models built on years of textual data, allows HiringBranch to predict these skills and validate them against on-the-job performance months after hire.
Bar-Moshe illustrated the importance of a holistic view with a practical example: "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." This highlights the need for a balanced assessment that captures how skills interact in real-world scenarios.
The episode also touched on how HiringBranch translates job descriptions into conversation flows and scenario-based assessments calibrated per client, region, and role. Notably, the company adjusts scoring weights based on geographic variations, such as differences between Vancouver, Toronto, and Montreal, even for the same role. This regional calibration ensures that assessments are relevant to local customer expectations and communication styles.
Looking ahead, Bar-Moshe previewed a self-serve capability in development that would allow hiring managers to build assessments from a library of conversation flows and skills. This feature aims to reduce reliance on weak or generic job descriptions, enabling more precise and effective hiring evaluations. The full study will be published under the AI research tab on the HiringBranch website.
The implications of this research are significant for businesses that rely on frontline employees to deliver exceptional customer service. By adopting a more integrated approach to assessing soft skills, organizations can improve their hiring accuracy, reduce turnover, and enhance overall customer satisfaction. As the workforce evolves, data-driven insights like these will be crucial for leaders seeking to build resilient and high-performing teams.
