For many companies, each hiring decision is a gamble. 

     “She seemed great in the interview. I feel like she’ll do a good job in the role.” 

     “He would fit in well with the team. I think we should give him an offer”

One month, six months, or a year later, how many of those hires are living up to expectations?

What teams really want is someone who can actually do the job, who has the right skills and experience to jump into the role and succeed. But often, workers who seem perfect on paper turn out to be less-than-perfect on the job. 

At Bluecrew, we’re all about data. We know what an impact access to real-time employee analytics makes for our clients, and we use all of the information we collect to help companies make the best hiring decisions possible. This led us to ask the question: do we have enough data to predict worker success from the start?

Short answer: we do. And we’re sharing our findings in our latest study, Predicting Worker Success: Factors To Consider and How Humans Miss Them

Here’s what you need to know: Focusing only on the factors that predict success as part of data-driven hiring can reduce bad hires by 38 percent.

Using Bluecrew’s matching-technology algorithm dramatically increases the quality of hire by focusing on the data points that matter most. Eliminating outside, invisible factors like age, gender, appearance, and even sloppy resumes allows us to more effectively match the right workers with the job. The result? Reducing the number of bad hires made by 38 percent. This increase in worker quality is a clear sign that workers’ skills and past performance are strong indicators of how they will perform in a job, and that reducing external influence helps employers select better employees for their roles. 

Get insight into which 6 factors help us determine the best fit by downloading our Worker Success Guide today. 

Making quality hires is important for any company, but it becomes even more consequential as unemployment rates decline. In today’s tight hiring market, companies simply cannot afford bad hires, and making hiring decisions based on predictive factors for success not only raises the quality of your workers but can lower time-to-fill and decrease turnover as well. 

Learn more in Predicting Worker Success: Factors To Consider and How Humans Miss Them.