Data and analytics are key to solving all kinds of business problems. Already, many organisations are using data and analytics to gain insights on their performance and use mathematical models to find viable directions for improvement while keeping track of the gains of this fact based way of decision making. Organisations apply analytics to all kinds of challenges in business areas like Operations, Customer Services and Marketing & Sales. The business area that seems to lag behind in using these advanced methods is Human Resources (HR). Of course a lot of HR related data is being gathered. However, much of the current HR related analytics create little impact. Even though more and more data is gathered and more sophisticated analysis becomes possible, HR rarely drives a strategic change. As Boudreau and Cascio indicate in Investing in People: “There is increasing sophistication in technology, data availability, and the capacity to report and disseminate HR information, but investments in HR data systems, scorecards and ERP fail to create strategic insights needed to drive organisational effectiveness. In short, many organisations are “hitting the wall” in HR measurement.” It’s my conviction that HR will be able to demolish this wall of measurement by turning to use advanced analytical methods and as a result increase its impact on organisational decision making and performance.
In practice, much of the HR related analytics happens in Business Intelligence (BI) tools like SpotFire, Tableau, ClickView or even MS Excel. These are great at accomplishing routine production of HR related reports and dashboards, but do not provide the support required to for example find the drivers for employee satisfaction or steer preventive measures to reduce turnover. To find those, predictive analytics capabilities are requires which BI tools typically don’t offer nor will the typical dashboard user have the capability to use these methods wisely. A BI tool will allow drill downs and supports the analyses of KPI’s of subgroups, but will not provide the explanation why this subgroup has these scores. You need to find your own explanation (or adopt a belief) which may be incorrect causing you to set up expensive change programs, possible addressing the wrong issues. To find the real drivers and causes, more advanced analytics tools and skill are required.
To illustrate, let’s have a look at employee turnover (data can be found here). Being able to understand the drivers of employee turnover and predict who is going to leave is of crucial importance to any company. It is estimated that for entry-level employees the costs of replacing them is between 30% and 50% of their annual salary. For mid-level employees, it costs upwards of 150% of their annual salary and for high-level or highly specialized employees, you're looking at 400% of their annual salary. Clearly understanding the drivers and being able to react on them can be a huge cost saver.
A typical way of how employee turnover is presented in BI tools is by means of a histogram. The histogram clearly shows that a lot of employees leave the company in the second year. This is especially true for the Human Resources and the Research and Development department. Also, at the Research and Development department, there is another peak of employees leaving the company at 10 years. Question is why? To answer that question, the histogram is not very useful and a more advanced methods are required.
Given the similarity with customer churn, it might be tempting to go for a logistic regression to predict the probability of turnover, or use a decision tree to find the relevant factors that drive turnover. However that would imply that we can’t incorporate an important factor that we are interested in, and that is time till resignation. A method that explicitly takes the time to an event into account is Survival analysis, also known as reliability analysis or duration modelling. The survival curve expresses the probability of survival (in this case staying at the company) over time. Survival analysis allows us to account for censoringand time-dependent explanatory variables, so incorporating the time since last salary raise or the time since last promotion. By estimating survival curves for different departments, job roles or other dimensions of interest, comparisons can be made and differences in resignation probabilities over time analysed.
Using the data from the histogram I created the following survival curves per department and job role. The high level of turnover in year 2 and 10 as seen in the histogram show as a strong reductions in the survival probability. Clearly visible are the differences per department and job roles (Sales reps seems to have a short future). The survival curves help us understand the rate of resignation better than the histograms as it shows how the probability of resignation develops over time, but it doesn’t provide the reason why people resign. For this we need a method that allows for additional explanatory variables to explain the resignations over time. A much used method for answering this type of question is the Cox Proportioned Hazard model, in medicine they are commonly used to describe the outcome of drug studies. To find out why so many people leave the Research & Development department, I used the Cox model to find that Years Since Last Promotion, Overtime and Job Satisfaction are the most significant factors. Job involvement, Job Level and Frequent Business Travel also explain resignation but are less significant. With these insights the HR department can turn to the manager of the Research & Development department and pro-actively come up with ways to reduce resignation levels by addressing the key factors.
The above example is just an illustration of how advanced analytics can be of value to the HR department and the organisation it is part of. With access to these advanced methods strategic impact of HR will increase, tearing down the wall of HR measurement. However, as this type of analysis is typically not routine and hence difficult to capture in a standard tool or way of working, HR departments also need to acquire the right analytical skills and mind set. There is more to using advanced analytical methods than just loading data in some analytics platform and pushing the run button, accepting the outcome as the best possible answer. Adequate business knowledge, being able to select and use the right analytical method and communicating outcomes to business owners are as much a requirement as having access to analytical software. With this in mind, for sure the HR measurement wall will cease to exist.