Nverse

Problem Statement:

A leading hospital chain faced high staff turnover rates, especially among nurses and junior doctors. The HR team needed insights into the causes of this turnover and strategies to enhance employee satisfaction and retention.

Our Approach:

We began by collecting data on employee demographics, job roles, tenure, feedback, and exit interviews. Using predictive analytics, we aimed to identify potential attrition risks and their root causes.

Tools Used:

  • QlikView for interactive reporting
  • Python (TensorFlow) for predictive analytics
  • HRMS integration for real-time data feeds

Outcome:

The HR analytics dashboard provided:

  • A heatmap of departments with the highest turnover rates.
  • Predictive insights identifying employees at risk of leaving in the next quarter.
  • Recommendations on policy changes, like flexible working hours and mentorship programs, that led to a 15% decrease in turnover in the subsequent year.

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