Hays used iMocha assessments to make data-driven decisions and eliminate bad hires

Over the years, the recruitment scenario has changed drastically; traditional methods like telephone interviews, in-person interviews, and pen-paper tests are no longer considered apt methods for screening candidates. In a data-driven world, data points and trends are of prime importance in evaluating skills. Recently, Hays had to procure accounting candidates for one of its clients. The job role demanded proficient financial and accounting skills. One of the team's major challenges was the lack of internal expertise to evaluate candidates on their financial knowledge. The recruiting team did not want to rely on gut feeling or put their faith in the elaborate resumes of candidates. They needed to quantify the candidates' financial knowledge and ability to apply it. In the past, Hays had encountered a few incidents where the candidate looked solid on paper, performed well at interviews, and even got hired by their clients. However, the real trouble started when their work output did not match the prowess exhibited at the interview stages. These were considered bad hires, which eventually affected the clients who had employed them. Hays wanted to shift to a data-driven recruitment process where skills would be thoroughly evaluated, and any selection or rejection was substantiated by adequate data.