The recruitment cycle is shortening. While earlier recruiters used to use manual matching of jobs to profiles, now they can use text analytics to speed up the profile matching through a basic keyword search to shortlist resumes. The next step is to optimise this process to include not just specific keywords but also related keywords. For instance, the person might have used words like deep learning or machine learning, allowing the system to profile them as an analytics specialist even if it hasn’t been marked out specifically. This is now a contextual text-based search.

1. HR Analytics: The Future Of Hiring And Retention
The recruitment cycle is shortening. While earlier recruiters used to use manual matching of
jobs to profiles, now they can use text analytics to speed up the profile matching through a basic
keyword search to shortlist resumes. The next step is to optimise this process to include not just
specific keywords but also related keywords. For instance, the person might have used words
like deep learning or machine learning, allowing the system to profile them as an analytics
specialist even if it hasn’t been marked out specifically. This is now a contextual text-based
search.
Predictive Analytics
Much of today’s recruiting comes down to a gut feeling. That is because recruiters look at the
information on hand and make a decision based on their understanding. Removing bias and
coming up with an accurate assessment, analytics is moving from descriptive to predictive.
While profile matching is currently based on keyword matching, the moment you make a
contextual text-based search, the results are suddenly much more relevant with fewer misses.
Taking this further, we can look at the candidate's social media engagement to find out his
views and frame to help understand their attitude or whether they would be a cultural fit. A trend
analysis can also be undertaken to find out whether when a person can leave, and how soon
the candidate can join.
Is HR of today ready
HR analytics is barely scratching the surface of its vast potential. Currently used mostly in the
recruitment stage, the few tools and applications out there mostly focus on simple dashboards
that allow HR professionals for business intelligence purposes like keeping track of diversity and
hiring. These represent current facts but do not help improve anything. The moment analytics
comes into the picture is when HR will take a dramatic leap forward.
A good HR software should be self-evolving and trained for unsupervised learning so that it can
automatically correct. The application I am working on will find the best fit for an organisation.
There are statistical methods to find the candidate say which candidate can provide the higher
ROI for the organisation. By looking into the profile, we can find out what would be the optimal
CTC, what are their chances of accepting the offer letter, and when would people leave.