QED Talent Risk Monitoring

QED People Analytics has developed an effective solution to monitor and pre-emptively address burnout risk within our workforce. We share this information openly with our client network and aim to produce the same outcomes in your organisation through the effective usage of your people data.

Talent risk monitor

The tool is designed with simplicity and effectiveness in mind; in summary, it monitors the number of weekly working hours each employee logs over a 3 month period, and assigns a risk metric to any above-normal hours experienced by the worker. This data is correlated with employee absence metrics (annual and sick leave dimensions specifically) to enrich the insights provided by the model regarding the overall burnout an employee may be experiencing. All dimensions are assigned risk grades, and those employees who are at an overall high risk (across any of the dimensions) are pre-emptively engaged by management to understand the individual’s scenario in more detail. The result is reduced employee dissatisfaction, increased management engagement, better role planning, and lower staff turnover.

Modelling

The model utilises available data from our internal platforms (HRIS and ERP) and visualises the results for usage in regular management discussion. It supports our people management efforts across our geographical locations, to improve the wellbeing of our workforce and improve our attrition metrics. A summary of at-risk employees is automatically shared with the relevant HR team and business leaders to action a check-in with affected team members and managers. We combine these insights with our culture and sentiment analysis data to better understand the issues that may be causing burnout risk.

Further enrichment

The next phase of our model is to further enrich our overview capability by combining KPI/performance, commitment and resilience metrics. This holistic approach allows us to deeply understand the current challenges in the workforce, in very particular areas of the business, and furthermore provides a basis for future prediction.

Ultimately, we aim to combine exit reasons analysis together with fully automated and integrated internal data sources, to pinpoint (with specificity) the areas of our business which require intervention before we face the potential loss of invested talent. Our investment in AI tools and neuroscientific methods afford a significant level of insight over and above the traditional metric approach. Even without such further investments, our baseline model offers an early warning indicator of workforce risk which has proven invaluable to the business. Speak to us today about how we can support your organisation with the same capability.