Case Study: Largest Global ITeS Giant achieves standardized voice evaluation and keeps false rejects below 10% with SHL

SVAR achieves standardization of voice evaluation process; holds false rejects below 10%

Largest Global ITeS Giant faced inconsistency and heavy manual load in pan‑India voice hiring: centralized voice trainers had to evaluate spoken English across multiple parameters, creating bottlenecks and risk of losing good candidates. SHL deployed SVAR, an IVR‑based spoken English evaluation tool, to standardize pre‑screening and reliably flag candidates suitable for voice profiles.

Using SVAR on 88 applicants, SHL showed a 92% classification agreement with the organization’s manual trainers (keeping false rejects below 10%) and an 89% concordance on candidates deemed not suitable. By matching trainer ratings across six spoken‑English traits, SHL’s SVAR demonstrated measurable standardization and the potential to significantly reduce evaluator workload and improve operational efficiency when used before the manual round.