Diagnostic modelling
Data Scientists can use data to create diagnostic models that identify the factors that are drivers for employee turnover. These models can be used to formulate actions plans and policies.
Predictive modelling
Data Scientists can use data to create predictive models that can identify the factors that are most likely to lead to employee turnover. These models can be used to identify employees who are at risk of leaving and to take steps to retain them.
Cluster analysis
Data Scientists can use cluster analysis to group employees together based on similar characteristics, such as job role, tenure, or engagement level. This can help organizations to identify patterns in employee turnover and to develop targeted retention strategies for different groups of employees.
Text analysis (via AI)
Data Scientists can use text analysis techniques to analyze text originating from employee surveys, performance reviews, to identify common themes or patterns in employee feedback. This can help organizations to identify areas where improvements are needed and to take steps to address any issues that may be contributing to employee turnover.
Network analysis
Data Scientists can use network analysis to identify key influencers or connectors within an organization. This can help organizations to identify employees who are vital to the organization and to take steps to retain them and/or use them as ambassadors in change programs, thereby driving buy-in amongst employees.
Deep learning
Data Scientists can use deep learning algorithms to analyze (anonymized) unstructured data such as employee emails, chats and other digital interactions to identify patterns in employee behavior that might indicate that an employee is considering leaving the company.
