This article first appeared in DNA India on 19th January 2017.
I have always been fascinated by the process of campus hiring. Fascinated and somewhat bemused. Having done it once on a much smaller scale, I am astounded by how large organisations can simply go to campuses and hire employees by the hundreds, even thousands. I know that aptitude tests and interviews can help to some extent, but does that help in identifying high performers? Does the tier of the college matter? Do students from top tier colleges like the IITs and IIMs outperform those from other colleges?
Organisations do try and look at their hiring channels to identify the most promising ones. One of my students at IIM Ranchi was asked to do just this at a large government-owned organisation. His remit was to come up with a way to shortlist colleges that gave the organisation the ‘best’ employees. Obviously, one of the criterion for identifying such students is based on performance. But are their other ways to look at the data?
This student of mine showed me a very interesting graph which had average percent retention on the x-axis and average performance on the y-axis. For every college, the average performance scores and retention rates were calculated and plotted on this plot. Two arbitrary lines, at 65% attrition and 85% performance scores were drawn parallel to the y and x-axis, respectively, thereby dividing the plot into four quadrants. My attention was immediately drawn to colleges in the bottom left quadrant which had high rates of attrition in addition to lower performance scores. What interested me was the fact that most of the colleges in this quadrant were Tier-1 colleges, including a number of IITs. At the opposite vertical end, in the first quadrant, there were lesser known colleges, which showed both high performance and low attrition values. So what was going on here?
This is just a conjecture, and here is what I surmised. A government organisation, however large, is probably not the first choice for most students from Tier-1 colleges. They would prefer to join private companies, multi nationals or even start-ups.
Thus, the employees that this organisation was picking up were most likely at the bottom of their respective classes. In addition, these employees might constantly be on the lookout to join organisations that their classmates were in, leading to higher attrition rates. On the contrary, this organisation was probably hiring the cream from lower ranked colleges. Moreover, these students had more to prove and were willing to put in the hard work to do just that. This could very well account for the somewhat counter-intuitive results obtained by my student. It would indeed be an interesting exercise to figure out if there was a correlation between performance and class rank for these colleges as well.
As you can see, hiring analytics need not all be about making prediction. Simple data analysis can still help to throw up hidden nuggets and insights.