Predictive Analytics as applied to Human Resources, People Science, or HR Analytics, as it is sometimes called, is well-recognized for its potential. A well-quoted Department of Labor statistic reported by Forbes and others estimates the cost of a bad hire at “at least” 30% of one-year’s compensation. Bad hires in senior executive roles can run a great deal more, easily into six figures. Moreover, while these number are generally accepted and quantifiable, CFO’s surveyed regarded the cost in morale and productivity resulting from bad hires as more important to the business. Reducing bad hires alone can possibly justify an investment in predictive analytics, but there are other opportunities as well, including:
- Talent Acquisition—the positive side of bad hire prevention, predictive analytics can identify profiles for successful candidates
- Compensation Management—the familiar “raise pool” of the 90’s leveled out compensation, such that top performers were paid similarly to average employees. This pleased average employees and compelled the top performers to leave.
- Turnover Management—the dread “churn”, especially in high-energy environments such as inside sales can cost hugely in training, customer relationships and morale
Implementing People Analytics in big companies would seem to be a high priority, so it is curious that PwC’s 2016 CEO survey showed that when CEO’s were asked how they would be changing their talent acquisition strategy for 2016, “Use of predictive workforce analysis” was far down a long list at 4%.
For all of the reasons mentioned above and more, applying people science and analytics to your workforce is a company-wide project, not an HR function. A People Science initiative will have little chance of moving forward without the direct support of leadership. There are several good reasons for this reticence, including:
- There isn’t enough good data. “Big Data” generally means a lot of data, because an effective sample size is critical to accurate analysis. Some SMB’s that hire many temporary employees on a project basis might have accumulated enough data. Big companies almost certainly have, but it may not be the right data. In the PwC survey mentioned above, the #1 talent acquisition change was a focus on a pipeline of future leaders. Even a large company may not have enough data on its senior management hires, so People Analytics may not solve the CEO’s #1 problem.
- Nobody really wants to admit that they aren’t good at hiring people. This is a cultural issue, and because it is not discussed, it is not solved.
- It can be somewhat creepy. Deloitte claims to have a client that can predict the departure of an employee based on his/her social media posts. There are many other use cases that are not intrusive, but with enthusiastic pursuit of new information a company can drift quickly into an area that may cause morale or even legal issues.
- CEO’s generally don’t expect their people-centered HR Team to turn into data scientists. There will be an increase in headcount to sustain the initiative.
These are all valid reasons in their own way, and caution is needed as well as an unbiased perspective. Companies should consider bringing in outside professional resources for a feasibility study that answers the following:
- Can People Science answer a question (or questions) that need to be answered in the short term? Does the company already know that it needs to reduce bad hires, or churn, or both, or is there some other immediate need?
- Is the right data available, and is there enough? There will never be perfect data to support a perfect prediction. Significant sample size determination is not always a yes or no proposition. Making an informed decision to move forward requires experience.
- Will there be longer-term benefits in investing now?
Bad hires, churn and low morale are not just HR issues, they are business issues. The reasons large companies and SMB’s have for investigating the possibilities of People Science are very compelling. Organizing a team to run an inquiry and then bringing in a qualified team to produce a feasibility study are low-risk high potential value decisions every CEO should consider in 2017.