How AI and Data Analytics can be used to reduce worker turnover: White Paper

Applying AI to strengthen your workforce is easier than you think

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Across the healthcare and service sectors, 60-65% of all worker turnover occurs within the first year of employment. Unchecked, ongoing churn of new hires creates a snowball effect that cascades across the entire organization. Turnover leads to more turnover, increasing labor-related bottom line costs. Top line revenue then degrades as an overwhelmed workforce struggles to deliver quality care and services.

Research studies for decades have found that the most impactful, consistent strategy that reduces new hire turnover is the infusion of new hires who will be ‘right’ for the position.

But what is ‘right’, and does your team know it when they see it?

When applied correctly, the tools of data science help employers identify the ‘right’ candidate.  When the tools include the latest machine learning techniques, they not only effectively match people to specific jobs at specific locations, they blow up all previous notions of ‘right’ that undermine the process today.

Many technologies claim to evaluate job applicants and provide recommendations.  More often than not, these technologies are merely comparing the words in a job applicant’s resume with the words in the job description.  This is not predicting that the candidate will produce outcomes on the job — it is evaluating the applicant’s ability to write a resume that reflects the job description.

Data analytics is not word-matching. It is part of a sophisticated range of techniques that analyze the outcomes which businesses wish to optimize – performance outcomes, retention, engagement, or any other measurable metric.  A machine learning platform can work through these continuous data flows and identify the job applicants who are most likely to produce similar outcomes in similar jobs/departments. Learn what AI + Data Analytics really can do when applied correctly and intelligently.