In God we Trust, All Others bring data’ has never been more true than it is today. We discuss how to make sense of voluminous candidate data and enable more informed hiring decisions
Back in the day, recruiting was primarily dependent of the â€˜instinct of the employer’ â€“ whether he or she felt a candidate was in-line with a particular role/position with a look-through across the resume, indicating previous experience and the various jobs held in the past.
Today, this has been amended and appended by several new ideas and approaches; â€˜Analytics’ is now a key driver in the overall hiring process, significantly redefining recruitment practices.
In this context, predictive analytics helps companies stay proactive, assessing, anticipating, and pre-empting outcomes or candidate behaviors on actual data. This is the result of a number of tech trends:
- The large data volumes coming in from a variety of social media channels and platforms
- The increasing utilization of workforce analytics solutions which are now easily accessible and available
- The ability to create algorithms from unemployment rates, growth indices, GDP, turnover numbers, and other trends to evaluate the future requirements/needs for HR teams/leaders.
Aditya Narayan Mishra, CEO, CIEL HR Services, shares his thoughts on this movement:
â€œOrganizations have a huge amount of data about their employees: their personal details, education, family background and so on. They also have data about employees’ performance and behaviors. Hence, they can correlate all these three dimensions to determine the typical profile of an employee who is likely to be successful with them. That’s the power of predictive analyticsOpens a new window !â€
Here then, are the three primary impact areas of Predictive Analytics in recruitment:
1. Hiring process quality enhancements
Recruitment quality is majorly affected by predictive analytics. By the combination of recruitment processes **with production performance, data on attritionOpens a new window , employee lifecycle information, and engagement survey feedback, organizations can now build prototypes that can predict the future performance of an applicant**.
Hiring models are now being reimagined with smarter processes and the constant assessment of incoming data. Take Ontame.ioOpens a new window , for instance. This tech company uses predictive analytics to suggest the right hiring channels and budgets, based on the specific role.
2. Intelligent and efficient sourcing
Sourcing is often a major pain point for recruiters, taking extended periods of time. Predictive analytics helps HR teams optimize their hiring strategies, removing poor or ineffective sources. Further, the same models can be deployed to evaluate job boards, 3rd party-recruiting firms, in-house recruiters, among other sources.
Here’s an example: early this year, background verification expert IDfy.com baggedOpens a new window $ 3 million â€“ they combine predictive analytics with available data sources to eliminate â€˜bad’ information.
3. Faster and targeted hiring
Finally, predictive analytics has a significant impact on the speed of hiring.
As the hiring model continues to develop and becomes more fine-tuned and intricate â€“ the ability to rapidly select best-fit candidates also improves substantially. Consider predictive analytics-led solution OutMatchOpens a new window , the tool that boasts the fastest candidate assessment for the by-the-hour hiring segment.
Recruiters can now move quickly, cut through the unnecessary clutter, and establish connections with the candidate, and quickly move forward. As a result, only the aptest candidates are selected.
Mishra goes on to further outline this process:
â€œPredictive analytics can help in predicting what employees are looking for and comparing these needs with what the managers are feeling, thinking and doing. There are tools these days to help managers recognize good performances and offer instant feedbacks; peers are able to express their gratitude towards others, help others as subject matter experts and express their views and opinions on an issue. Analysing results of these conversations tell us who the top performers are and how they are feeling.â€
One of these tools is Ascendify’s AspireOpens a new window , using predictive analytics and AI to assess skill levels, gaps, and possible resource placement.
Today, several companies have actioned predictive analytics and report revitalized performance in the recruiting space â€“ while also registering improved retention figures. In talent management, for instance, Google is known for having introduced revolutionary predictive analytics programs for recruitment, retention, and leadership. Other majors, like Cisco and Sprint, are also using predictive analytics to change hiring systems and performance measurement mechanisms.
Clearly, given the current workplace with its focus on millennial employees and the need to offer enriched employee experiences, tools such as these can help locate, foster, propel, and retain the best talent, with an eye on helping an individual maximize his/her potential.
Experts estimate that predictive analytics will find adoption amongst a majority of firms across the globe, for the hiring and management of external applicants and internal employee progression pathways. Solutions like GreenhouseOpens a new window that couple predictive analytics with machine learning, achieving an impressive 90% prediction accuracy, are poised to take over the landscape.