It is indeed the modern age talent acquisition where “funded” start-ups have poached almost every potential candidate with high ‘decision & money making’ roles. It has been an emerging challenge for seasoned companies to attract & hire the right talent, and quite a close competition (healthy I would say) between the start-ups, ecommerce & the matured players. Since the smart candidate is in win-win situation, they are best at negotiations while ensuring timely joining.
I had been in back to back recruitment drives in Bangalore, Pune, Chennai, Hyderabad, Gurgaon & NOIDA. The common of all I see is the selected candidate is either in discussion with another company for the offer, or already holding another offer in hand, here’s when few organizations target such candidates who are already shortlisted for one organization & then go ahead offering them higher package for the same role.
Emergence of Predictive Tool in Talent Acquisition
However, the question is, is this best practice?? I guess no! It is much more debatable! But let us face that market is consolidating and will be at the mature stage for next 5 years as every product life cycle goes through, it is the phase where the start-ups are either merging into bigger players or are just consolidating. Candidates or even employers are searching for jobs for themselves, if they have the skills that are worth a better role and package. With some articles and facts I can estimate here that out of all the starts-ups, 95% are not successful. In such companies, graduates are hired by some of their senior batch mates are fired when the project, app, or product fails to launch/succeed.
HENCE talent acquisition in this time is a major challenge, and technology is becoming the backbone of all solutions from websites to now mobile app only driven product businesses, to all services industry, hence the engineers are at their best of times, and recruiters are at their worst, I am a recruiter and ask me the pain one goes through to fetch the right talent is extreme and further after finding one to ensure the joining of him/her in even killer.
Recently we developed an in-house predictive tool called “linking success matrix” to determine the probability of potential and offered candidate to join or lets reverse the statement and say “Probability of potential and offered candidate to drop”. Yeah its sounds a little unfair but it is like that as the drop-out ratio today is >15% which is surprising.
The idea and mythology used behind this tool is not much complicated but high data driven & with 100% absolute/accurate data. Building the data from candidates available in the market. The ones to be called for interview, till they got rejected and importantly for the ones who got selected.
Let’s start with insuring the basic details like, age, gender, permanent address, school, scores, AIEEE / CAT rank/score, graduating college and course particulars à we have also bifurcated the Indian and international colleges in categories of “A+”, A,”B”, “B+” this gives us the clear vision to correctly map the candidates w.r.t. level, comp. and even projects / teams now the details which plays an important role is the organization s/he is looking-out for job, which was the ex-company, what were the projects he has on, had been at what levels and salary hikes over time total experience v/s interest to be in as individual contributor or a team manager, inclination towards to role, relocation and family background as well.
With all stated data metrics is developed (this will vary from org to org) also compensation study plays a major role in here and also the mapping of industry parity with your position on it, e.g.: if to an IItian other e-commerce are paying higher than your organization so at what percentile you are, & if you are almost at the same or closer to 100th percentile, then the offer made and the metrics referred will be more role driven over comp keep the other factors also in favour of the potential candidate.
The metrics is a sum of data scores v/s the role requirements and output is proximity probability (keeping the unavoidable circumstances to a <5% cases to case basis)
So with the little bit calculation on the following:
- Reason of change (should include – industry, role, level, work profile, etc.)
- Overall package (includes – compensation, 2 years earning potential, perks, facilities, etc.)
- Pedigree (academics – premier or other institutes, last org, org wish list, etc.)
With calculation to these simple matrices, a model can be defined on probability of drop-out in 3 parameter, HIGH, MEDIUM & LOW.
NOW, this I would say is transformation in talent acquisition, more of going around which predictive modelling so for hiring 10 developers, the study will be immense and making the resources more job fit and working out on the model the hiring numbers can be revised to higher than actual offers to developers since prediction will be of uncertainty for joining.
The future talent acquisition will be much more predictive and manual evaluation may take a back seat. HR / Talent acquisition will play an important role in the evolving recruitment industry with advanced hiring tools, software and processes to hire the potential candidates.
With 5 years of recruitment experience in IT industry, consumer Internet & e-commerce industry, Sarung is still expanding his horizons in the domain. Graduated in bachelors of business studies, the blogger is MBA in HR & holds certification in Executive HR from XLRI. Presently, the HR leader is working with MakeMyTrip & taking care of Talent Acquisition. Sarung loves digital & boundless networking.