As human beings we are not very good at understanding the true nature of reality. This is not because we do not try, it is because the complexity of what we are trying to understand is beyond the scope of our senses and our brains to comprehend from an objective position. The ancient philosophers were well aware of the limits of human capability and more recently Dan Ariely has done some interesting work in helping us understand and reduce the negative impacts of our limited cognitive capabilities.
The joy of numbers and tracking activity through a consistent and repeatable system is that it gets past the biases of perception and limits of cognition which can skew the decisions we make about people and organizational practices. Given that median labour costs are 53 percent of total expenses, any and all decisions which affect the value created by the people covered under this budget should be seen as important to get right.
Here is a look at how some simple data that tracks resignation rates by tenure can help to enhance decision making and the value of the outcomes. The chart shows that an individual’s likelihood of resigning reduces significantly the longer they are with an organization. Based on 2 years of data the average resignation rate for those with less than 1 year of service is 5 times that of people with 3 to 5 years of service. The biggest drop in resignation rates comes between 2 to 3 years and 3 to 5 years of service.
The first question raised by this chart is why are first year resignation rates so high? What is it that individual’s or organizations are not doing that means (across a database of 100,000 employees) one in every ten people leaves their role within the first year. There is a lot of cost in the hiring process and a lot of wasted effort when that person chooses to leave. There is evidence here that suggests investing more time and effort in selecting employees would payback in reduced voluntary turnover costs.
The second more subtle indicator from this chart is the difference between 2 to 3 years of tenure and 3 to 5 years of tenure. This time frame shows the largest gap in terms of reduction in resignations. The gap between the two bars is the largest on a percentage basis. This drop suggests that something significant happens after three years of service increasing the likelihood that individuals will stay with the organization.
This information is valuable when it comes to a range of HR decisions. Maybe we should only invest in leadership development for those who have been with the organization for more than three years. This group are more likely to stay with the organization and thus provide a return on this investment.
Alternatively we focus our development efforts on those in the 2 to 3 year window with the intention of holding them for 3 years and helping to increase their loyalty for the longer term. Maybe we only promote to manager those who have worked in the organization for three years again leading to more likelihood of our management talent sticking around.
All of these are sensible strategies and have the potential to bring great value to the organization. All of them derive from the process of systematically tracking and reporting data and using it as the basis for decisions and future action. There is still a fair degree of judgment involved in using the data, however there is no intuition or no reliance on imperfect perception.
If three is a magic number – meaning 3 years is the turning point for an individual`s likelihood of resigning – then we have learned something of enduring worth that should push forward our endeavours to monitor and understand the powerful human dynamics which drive organizational performance.
 HR Metrics Service Annual Report 2010 – All Sectors
Ian J. Cook
HR Metric Service
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