R Helps With Employee Churn
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by Joseph Rickert
Pasha Roberts, Chief Scientist at Talent Analytics, is writing a series of articles on Employee Churn for the Predictive Analytics Times that comprise a really instructive and valuable example of using R to do some basic predictive modeling. So far, Pasha has published Employee Churn 201 in which he makes a case for the importance of modeling employee churn, and Employee Churn 202 where he builds a fairly sophisticated interactive model from first principles using only RStudio and basic R functions. And, while the series is not even complete, I think that is is going to be unique because it is working well on multiple levels.
In Churn 201, Pasha uses R almost incidentally, to produce the following plot that illustrates the concepts involved in understanding the costs and benefits contributed by a single employee.
At the lowest level, this is a nice example of what might be called a “programming literate essay”. R clearly isn’t necessary just to build create a graphic. (Note the use of ggplot's annotate() capability.) But, if you look at the R code behind the scenes, you will see that Pasha has gone a bit further. In a few lines of annotated code he has sketched out a self-documenting model that someone else could use to get “back of the envelope” results for their business. The exercise is roughly at the level of what a business analyst might attempt in an Excel spreadsheet.
In Employee Churn 202, Pasha goes still further moving the series from essays alone to a modeling effort. He uses basic survival analysis ideas and simple R functions to create a sophisticated decision model that computes several performance measures including something he calls Expected Cumulative Net Benefit. This measures the net benefit to the corporation employees who leave for both “good” and “bad” reasons.
The following figure shows the simulation running in RStudio complete with interactive tools built with the manipulate() function to perform “what if” analyses and display the results.
Running the simulation is easy. All of the code is available on Github where the file, churn202.md, provides details on how things work. Once you have run the code in the churn202.R or issued the command source(“churn202.R”) from the console, running the function manipSim202() will produce the simulation. (Note that might be necessary to click on “gear” icon in the upper left hand corner of the plots panel to have the slide bar controls appear.) The function runSensitivityTests()varies each of the parameters in the simulation through a reasonable range of values, while holding the other parameters fixed, to show the sensitivity of Expected Net Chumulative Benefit to each parameter. The function runHistograms() produces histograms of the synthetic data that drive the simulation and hints at the data collection effort that would be required to run the simulation for real.
By placing the code on GitHub and inviting feedback, comments and pull requests Pasha has raised his literary efforts to the status of an open source, employee churn project without comprimising the clarity of his exposition. I, for one, am looking forward to the rest of thes series.
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