Human Capital Management is as essential to the survival of an organization as the organization itself. Leveraging on its power could result in better retention of talent. On the same lines is designed, the SAP Success Factors Workforce Analytics & Planning (WFA&P). Using its analytical and planning capabilities, professionals human resources could, in fact, answer questions about turnoverthe of the company? signs that an employee is about to leave and others. Embedded Intelligence, workforce planning, happen to be two of the 3 most important components of the WFA&P. With the state of predictive analytics improving, the platform will also acquire the capability to answer the How’s and Why’s of the questions.
Employee Flight Risk
The HCM has long been facing the issue of voluntary termination i.e., the employee resigning out of their volition. Customer churn prediction focuses on retaining the consumer by studying what made them avoid a transactional decision the last time & thereby pitching the right thing at the right time. Similarly, employee flight riskcompares the reasons cited by those who left and by those who stayed.
Automated Predictive Modelling
Once an employee’s resignation tendencies are recognized the human capital management could move forward suing SAP Automated Predictive Library (APL) in order to address the risk predictions.For instance, an enterprise could rely on a past record of the company head-count for the purpose of creating a model. This model can, therefore, be used to further aims in relation to the current head-count after a few modifications. APL uses ridge regression, a non-parametric algorithm so to reduce the need for data distribution.
Many factors contribute to the quality of a predictive model. Two of the basic factors are attributed size and record size. If the number of attributes is small, then the model is likely to be biased. Further, if the number of records is small, the variance may be too high to separate one outcome from another. Therefore, having an adequate number of both attributes and records are essential to creating a high-quality predictive model.
An HCM unit recognizes the fact that predictive analytics application can also be used to gain in-depth knowledge, not in a technical form which is almost incomprehensible but a simpler manner which could be explained by others. According to the model, there could be 4 pillars for a predictive model, namely Customer Grand, second Organization Tenure, third performance rating and last being Job Function. Performance parameters can be further segregated into the different types of employees and what kind amongst them resigned.
Predictive analysis has helped the human capital management departments leaps and bounds in mitigating the causes that lead to employees leaving their companies. As increasingly positive results manifest from one enterprise to another, it is only a matter of time, when this form of data science technology would become ever-prevalent.