A leading healthcare technology company improved its Resource Management Planning with an accurate prediction of the candidate joining through Feedback Insights Predictive model on candidate dropouts across locations and designations.
What is the Challenge?
- The client was facing the disappointment of seeing candidates drop out at the last minute. They wanted to minimize this and reduce the process of searching for the right candidate all over again. This adversely impacted their resource planning and operational efficiency. They believed that data-led intelligence would be useful in tracing the trajectory of candidates and identifying trends to become proactive and plan better.
Addressing the Challenge
- Feedback Insights built a predictive model to forecast the likelihood of a candidate joining. The team reviewed industry practices to identify the right variables for the model. 18 variables were identified based on the candidate profile, resume source, and internal processes.
- Assigning appropriate weights to variables by conducting discriminant analysis.
- The analysis was separate for different designation levels and key locations.
- Categorizing the candidates as hot, warm, or cold based on quartile analysis.
- Building the model and integrating it into the system.