Data Visualisation + Reporting Solutions
Experiencing increasing demand for services in a tightening labour market, a multinational firm asked Harmonic Analytics to develop a quantifiably robust method to predict the likelihood of staff leaving.
The additional pressure of immigration restrictions due to COVID-19 also decreased the number of available employees, creating competitive employment demands within the industry. The firm wanted to understand the key drivers that motivate staff to remain at the company.
Harmonic Analytics developed a statistical model based on staff information of both past and present employees. This included basic information like age, gender, and working location, as well as employment specific information such as tenure, salary, and billable utilisation. The model identifies key features that contribute to staff retention. This information is used to help the firm prioritise initiatives that keep staff motivated and increase overall employee retention.
With the established model, the firm can also identify whether current staff are at a low, medium, or high risk of leaving. This statistical model underlies an interactive R Shiny dashboard that leadership staff can access to understand overarching patterns and drill down into the details. The model results are presented in unison with an array of metadata and visualisations.
The dashboard landing page aggregates and highlights key aspects of the firm’s employee make up, giving the user the ability to refresh the displayed information by a specific business unit. Other pages on the dashboard dig deeper, telling the ‘employee story’ and describing how propensity to leave - as determined by the model - relates to other employee features. This allows leadership staff to understand what progression level group contains the highest proportion of staff at risk of leaving.
An error has occurred. Please try again later.