People Analytics practices to improve Talent Acquisition & Retention

By Alex Hellemons

Define required insights
When the employee segments with a retention/attrition challenge have been identified, analysis questions can be defined for each segment. Or in other words, which insights are required to improve retention and reduce attrition? For example:

  • Within the groups with increased attrition, what factors are related to retention/attrition?
  • Why are people within these groups leaving the organization?
  • Where are these people leaving to?
  • What is the flight risk of people within these groups?
  • Which teams can expect unnatural attrition in the future?
  • What are potential mitigating actions for the people who are at risk?

Start with People Analytics 

When employee segments and analysis questions are defined Data Scientists can start cracking. Data Scientists will use Machine Learning techniques to answer the abovementioned questions, or they will go one step further and can build algorithms that support HR in its daily operation. Using predictive or even prescriptive data science techniques, Data Scientists can build an algorithm which indicates in which teams the chance of attrition is the highest. The algorithm can even provide suggestions to HR about potential interventions to prevent attrition in the team.

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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.


Related insights

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