Curated News
By: NewsRamp Editorial Staff
August 28, 2026
Stop Scoring Skills in a Vacuum: A Better Way to Hire
TLDR
- HiringBranch's new research shows combined skills scoring outperforms isolated scores, offering a competitive edge in hiring better frontline talent.
- HiringBranch uses sociopragmatic analysis and machine learning to measure empathy and other skills, validated against on-the-job performance.
- This approach ensures candidates with empathy can also solve issues, leading to better customer experiences and fairer hiring.
- HiringBranch's research highlights that empathy alone is useless without problem-solving, and they tailor assessments by region.
Impact - Why it Matters
This research challenges the status quo in hiring assessments, urging companies to look beyond isolated skill scores. By adopting a holistic, context-aware approach, employers can identify candidates who truly excel in customer-facing roles, reducing turnover and improving customer satisfaction. For job seekers, it means being evaluated on real-world abilities rather than fragmented metrics, leveling the playing field for those who shine in practice but not in traditional tests.
Summary
In the latest episode of the podcast You Should Know, hosted by William Tincup, the spotlight falls on a groundbreaking approach to hiring that could reshape how companies evaluate customer-facing talent. The episode, titled Assessing Skills One at a Time Is Costing You Better Hires, features Assaf Bar-Moshe, Chief of Research and Development Officer at HiringBranch, a Montreal-based assessment company. Bar-Moshe unveils new research that challenges the traditional practice of scoring empathy, acknowledgment, active listening, and reassurance as isolated metrics in pre-hire evaluations. Instead, he advocates for a combined model that reflects the complexity of real customer interactions, which he argues leads to better hires.
The conversation dives into the mechanics of HiringBranch's innovative assessment design, which uses open-ended voice and writing prompts to simulate live customer scenarios. Bar-Moshe, a trained linguist, explains that while single-skill scoring shows only moderate correlation with human judgment, their proprietary combined model, which integrates the four pillars of customer service—empathy, acknowledgment, reassurance, and active listening—yields much stronger predictive validity. He emphasizes the importance of a sociopragmatic analysis of candidates' words, rather than personality tests, to gauge on-the-job performance. The assessments are calibrated per client, region, and role, reflecting nuances across markets like Vancouver, Toronto, and Montreal. Bar-Moshe also hints at a future self-serve tool that would allow hiring managers to build assessments from a library of conversation flows, reducing reliance on generic job descriptions. The full study will be available on the HiringBranch website under the AI research tab.
The episode illustrates the real-world stakes with a retail anecdote about a mispriced broccoli confrontation, where diplomacy trumped policy. Bar-Moshe's key insight: "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 episode is part of the WRKdefined Podcast Network, which reaches over 3.9 million monthly listeners, and is available now at the You Should Know Podcast page.
Source Statement
This curated news summary relied on content distributed by Newsworthy.ai. Read the original source here, Stop Scoring Skills in a Vacuum: A Better Way to Hire

