Over the last decade, recruitment and Human Capital solution vendors have experienced a boom in demand for specialist services. The range of products and services available to organisations aiming to reduce the costs associated with poorly managed selection processes has increased. Personality assessments, competency testing, structured interviews and assessment centres -- all have become widely available and are an attractive option, especially for larger organisations.
While these products and services may provide some assurance to management that the most suitable applicants are selected, the reality is that an applicant''s performance during a selection process provides a limited indication of their actual on the job performance. Similarly, existing methods of employee selection are unable to reliably recruit those whose performance is in alignment with organisational objectives.
This article outlines an analytical approach that supports decision-making during recruitment. We present an example of how employee data from a large company can be analysed to enhance the selection process. Specifically, we have provided a case example for an approach to reduce turnover costs.
Modern approaches to measuring and analysing human capital metrics have given us better information to use in making recruiting decisions. The measurement of constructs such as organisational culture and climate provide management valuable insight into an organisations structure and what strategic initiatives will influence employee performance.
Similar to financial analysis, biodata analysis provides insight into statistical trends within an organisation. If turnover has become a significant cost to an organisation, statistical analysis can provide predictive information on which to base recruitment and selection decisions.
Turnover: A Real Cost
During a strategic planning exercise, the Human Resource (HR) team for a large multi-national organisation was in the process of identifying strategic initiatives aimed at reducing expenditure. Whilst conducting an analysis of operating expenses, the team identified that recruitment and training expenditure was marginally greater than the industry benchmark. They discovered that the spending resulted from a high rate of turnover.
Consequently, they agreed that an initiative aimed specifically at reducing company turnover was required. Even though there are a range of initiatives available to promote employee retention rates, one approach focuses on recruiting applicants that have a high probability of remaining within the organisation. For the tenure of an applicant to be predicted, the following procedure was followed.
The existing data archive of employee application forms and employee records was analysed to identify what information in the application form could be used as accurate predictors for classifying applicants as future long or short-term employees. It was estimated that, for turnover costs to be fully recuperated, an employee would have to remain within the organisation for a minimum of 3 years.
The following variables were identified as possible predictors of tenure:
- Employee Category
- Sex
- Age at commencement of employment with the organisation
- Service in years (previous jobs)
- Highest schools qualification
- Other qualifications
- Number of previous jobs held
- Total job experience (years) prior to joining the organisation
- Clerical test
- Numerical test
- Verbal test
- Performance ratings
- Training ratings
The most important variable at this stage is (1). This indicates whether the employee is still employed within the organisation after a three-year period or whether they left prior to completing a three-year term.
Analysis
A statistical analysis was conducted that provided a predictive value for each of the variables listed above. From this, the significant predictors of Employee Category (1) were identified. A combined analysis using only the significant predictors was then conducted. The result of this procedure is an equation that can be used in future recruitment processes. For example:
The equation consists of a constant (x), and additional weights (b) that are multiplied by the information gathered from potential employees'' application forms (items 7, 8 and 10 above). The values for (x) and (b) are consistent and are used for all applicants.
By populating the equation with an applicant''s details (and completing the appropriate calculation), the product is a probability estimate. This estimate indicates the likelihood of that applicant remaining in the organisation longer than the 3-year period. A high probability (.73) indicates an expected tenure >3 years, whereas a smaller probability (.22) suggests a much shorter term of employment. By using this procedure, the HR team will improve the employee selection decisions aimed at reducing turnover and overhead costs associated with short-tenured employees.
Limitations
As with all statistical procedures there are limitations:
- The analysis is only as good as the quality of the archive data;
- Certain data sets may not contain variables that are significant predictors of employee tenure.
Additionally, in the instances that the variables such as age and sex are predictors of turnover, concerns may arise for any Equal Opportunity initiatives that an organisation may be supporting. If this was to become a major concern for the practitioner implementing this kind of initiative, considerations based on the demographic characteristics of the client organisation must be made.
Practical Application
Even though the case example was aimed at tenure, the same approach can be used for predicting other employee indexes, such as performance. The value of this analysis is that it is based on information provided by the organisation; therefore, the equation is tailored specifically for that business demographic.
The advantage of having this type of service available from Deloitte is that, currently, this service is not offered by any other Human Capital solutions service provider. The equation is a high-level tool that is of great value to a company aiming to make strategic decisions regarding recruitment. Ideally, for a complete service to be provided, additional calculations should be included to support the prediction equation. This would include, for example, the Schmidt-Hunter Estimation Procedure (an equation for determining the impact of improved personnel selection practices on workforce productivity), and an ongoing analysis of turnover costs. Even though these calculations could not be completed until one financial year after changes to the selection procedure had been implemented, it is important to support any service of this kind with real dollar values.
