Predictive Analytics for employee retention is critical for many businesses is critical when especially human capital is ranked as their most important asset. When a strong employee leaves, it impacts products, operations, sales and/or customers. Talent retention has a direct and immediate effect on the bottom line. Companies capture a lot of data and information about employees, and hidden in that data are indicators of voluntary terminations. Using advanced analytics techniques, it is possible to preemptively identify and address employees who have a high probability of voluntary terminations. Addressing employee voluntary terminations before they happen, it is possible to retain high value employees and improve corporate competitive advantages.
Corporate Technologies developed a predictive analytics capability derived from HR data such as:
- Employee events
- Employee performance
- Census data
- Market research
The capability provides reliable and actionable predictions on who is likely to voluntarily terminate in the next 6-12 months. The predictive models use key variables as well as customizable variables that adapt to each specific company. This solves the one-size-fits-all analytics that product vendors provide.
Knowing how many and who is likely to voluntarily terminate provides business value in:
- Retention strategies
- Talent Recruitment
- Succession planning
- Talent management
CTI Vision™ for predicting voluntary termination is created as a customizable and repeatable solution that can deliver accelerated insights to stay ahead of employee retention challenges. The solution is data driven and data delivered and can be delivered as a point-in-time view or deployed in an operational capacity.
Through our experience we have identified key variables and with a customized mix of client specifics, are able to develop a fit-for-purpose model that identifies predictive voluntary turnover.
Depending on the client needs, we can develop and deliver the predictive model in multiple languages that can be integrated into regular data processing.
We are database agnostic and work with most major database vendors. Our models are developed in SPSS or R and can be delivered in multiple programming languages.