Contextual Intelligence Nails Relevance and Experience of Talent Pool

May 11, 2016

"You can't judge a fish by its ability to climb a tree!”

Interestingly, this obvious assertion finds significant relevance in the HR industry when it comes to fulfilling the burgeoning demand of resources to satisfy the talent needs of various industries across verticals.

The plethora of candidate sourcing channels and hiring platforms make it obvious that recruiters in an organization seldom make efforts to reach out to candidates but often frequently than not, they are unable to achieve the expected relevance of their hiring.

Now the vital question is - What is it that we need to do to get the right candidates i.e., to find and bring the best fit for a company? Before we dig deeper into this question, we have to understand 3 key factors that companies are looking for in a candidate:

1) Desirable Skills

2) Relevant Experience

3) Quick Adaptability

All these 3 factors amalgamate to form the base of what we call "Contextual Intelligence”.

Contextual Intelligence in Talent Sourcing

Diversity of talent pool can be broadly thought of as a measurable difference among various candidates based on skills, geographies, verticals, experience etc. For a long time, analysing a candidate's fitment for a job has been a tedious process requiring human intervention.

It typically involves going through the candidate’s profile and making an educated guess about shortlisting, interview scheduling and releasing offer.

Over time, there has been an exponential growth in the number of - candidates available, availability of various jobs and the combination of skills required. This situation cries for a solution which has the ability to segregate candidates, jobs, roles and enables easier decision-making process for HR. The challenge is to understand the right context for a particular job and accordingly pull out the best fit candidates.

Skills and Experience – Not as Easy to Discern by Recruiters

An organization might decide to hire from locally available talent pool but due to increased globalization efforts going on in almost all of the major multinational corporations, this factor is slowly diminishing. Hence making contextual intelligence technology an even more prudent choice.

Skills are mostly mutually related and hence make it difficult to be comprehended. Drawing their correlations manually has limits. We require automated systems which can look for related skills and keep on enhancing the corpus.

Experience, on the other hand too has to be relevant for the particular job and as per requirement. Adaptability of a candidate in an organization depends on what kind of roles he/she has undertaken, performed the best and so on.

Unarguably, recruiters can't go through all the candidate profiles to find out the above mentioned facets – skills and experience - and that is where Contextual Intelligence kicks in.

Contextual Intelligence Helps Take Correct Approach: Saves Time, Money and Brings Value

Contextual Intelligence analyzes unstructured candidate data as per requirements which vary from company to company. It paves the way for organizations to set their own parameters, benchmarks and criteria in the hiring process. It substantially takes into account the hiring scenarios, timelines and candidate data at large to filter out results which come handy for recruiters, not just in the present but also in the future.

The advent of social media and professional networks makes it compelling for recruiters to reach out for candidates in these channels too. The horizon of Contextual Search and Intelligence accommodate all of the changing trends of recruitment.

Constant improvement in the methodologies is what defines efficiency and indeed, we need to keep on distilling the parameters and thought process. The motivation behind Contextual Intelligence in the area of talent mapping adheres to the fact that taking a correct approach while hiring saves time, money and more importantly brings value to the company.

Context-based search brings intelligence to the long-lasting pursuit of suitable candidates and in talent mapping, a space which requires meaningful data and not just any data.

The funny part is - what is meaningful and what is not? Even this is a variant of time, organization and geography! Turns out to be challenging, isn’t it?