Recruitment as a function is evolving. The right use of technology has become a necessity to make our hiring more accurate and efficient. Many companies have already started using Data Analytics as their next big move to improve their hiring. Getting the right resource at the right time is the key for any organization.
What the study says – Analytics has three levels of sophistication:
1) Hindsight: Data collection and reporting
2) Insight: Making sense of the data and developing tangible actionable insights
3) Foresight: Develop predictive models to predict future behaviour or success ratios
Let me try to relate it to my earlier article Artificial Intelligence, Hiring & You. I got a good response to this article. Few readers also had some doubts about the usage of Artificial Intelligence and weightages attached to each attribute while calculating the suitability score.Yes, currently I am putting the weightages based on the importance attached by the recruiters to all these attributes while screening the profiles. Job descriptions assign different priorities to various attributes viz. notice period, salary budget, experience in a specific version of technologies (CSS3/CSS5, Oracle 9i/10g/11g), distance, overseas experience etc. Similarly, I am also doing the same by assigning weightages to different attributes as seen it in the highlighted top row in the spreadsheet above.
Machine Learning system needs to “learn” from historical data or “training sets” representing different scenarios, different decision-making styles at different times. The Machine Learning algorithm can draw some patterns from the historical data and derive future predictions based on it. Larger training set means a more accurate prediction. Over a period of time, the algorithm will become better at its prediction as it learns from every candidate considered for every job.
Currently, a majority of the hiring decisions are based on our hunch, past experience and there is no unified measure to it. Machine learning can help us to build some structure around it and help us predicting probable outcomes if we train it with a large enough training set.
To simplify let me share some of my personal experiences that I learnt from. Initially, when I started, I used to create the suitability score tracker manually with some excel formulas and bit of VB programming. It used to take a lot of time for me to identify the most suitable candidates for even one job at a time. To calculate the suitability score for a typical batch of 10 candidates being evaluated by a hiring manager for a job would take me a whole day. Also, the weightages used were purely based on my past experience and there was no way to tell whether I was right or wrong. Hiring manager’s feedback helped me to correct the weightage numbers so as to reflect his preferences. Gradually, I started learning about that specific hiring manager’s way of making decisions, his likes and dislikes, his “must have” attributes (technology, CTC, qualification etc.) based on the feedback.
As time passed I worked on 20 JD’s, 200 resumes for the same hiring manager and to my surprise, I could almost surely predict the top 3 spots. I and my tracker started to more or less accurately predict the probable outcome of a particular candidate for a particular JD. What happened was that I could capture the hiring manager’s thinking pattern and preferences in my tracker.
Enthused by this initial success I worked with some more hiring managers for a few more positions. As of date, the results are consistent. I got better and more confident as I dealt with more managers and JDs.
This experience has truly led me to believe that this form of disruptive recruitment analytics can help predict and streamline the hiring process to a great extent, thus saving overall hiring time and costs significantly. Moreover, hiring the right candidate dramatically reduces the attrition rates and helps enhance the productivity of the company.
Please share your views and suggestion
Thank you so much for your patient reading. I am curious knowing your views & comments about the usage of #MachineLearning (#AI) into #Recruitments.
Dinesh Gokhale is a seasoned professional with more than a decade of diversified experience into IT recruitment along with B2B sales, marketing, artificial intelligence and lean hiring technology. He is admired for his soft-skills.
Dinesh also is an enthusiastic sportsperson, reader, traveller and a die hard foodie. In his selfless desire of giving something back to mother nature & society, he has been involved in many social initiatives. Currently, he is working in the capacity of Sr. Principal Consultant with Rezoomex.