Recruitment Analytics is more than just resume matching

This post first appeared in DNA India on 27th October, 2016.

One of the areas where analytics has made much headway within the HR world is in recruitment analytics. There are a number of new startups that are working towards helping organizations to find the right candidate. Every startup claims to have its own unique algorithm to identify the right “fit” between the candidates and the company. These could vary from using large quantities of data to derive industry-specific candidate profiles or the use of referrals to hire the most suitable candidates since it is known that referrals are possibly the channel with the highest conversion ratio while hiring.

Many organizations have outsourced the first step of the hiring, namely, resume shortlisting to such portals and organizations which, in a way, is the right thing to do. However, is that all there is to recruiting analytics? I would argue that there is way more to recruiting than merely the ability to get a better set of shortlisted candidates. In the book “Work Rules”, Laszlo Bock of Google, takes us through Google’s journey for optimizing their recruitment process. There are two main areas for this. The first area is in optimizing the cost. This could be done through a better pipeline of candidates flowing through the system, utilizing newer and better algorithms for resume matching as well as looking at multiple recruitment channels including campus hiring, referral hiring etc., and in streamlining the entire recruitment process within the organization. This can lead to significant cost savings. A critical metric for this is the time to hire (TTH) which is directly related to the expenses related to hiring.

Organizations ought to look at their entire hiring process, break it down into chunks and then look at saving time in each of these sub-processes using a LEAN approach. A result of this approach might be that some parts of the overall recruitment pipeline are outsourced and that is actually preferable in order to achieve the overall goal of time and cost reduction.

The second area is in optimizing the quality of hire. This can be done by having a structured interviewing process, using data to make critical hiring decisions and in rating interviewers based on their successes. Interviewer rating data can be used to help identify areas of weaknesses for the interviewers and provide them with the required training as well as in maintaining quality standards. As Bock explains in the book, this enabled Google to cut down on the number of interviews per candidate from nearly 25 to about 4 which impacts not just TTH but also the overall candidate experience. In an era where candidates share their experience with companies through portals like Glassdoor, ensuring that candidates passing through your organization have a great experience regardless of their making the cut goes a long way in making your organization be seen as an attractive place to work

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