Digital recruitment – think Monster and LinkedIn – is routine today. But what if a voice-powered AI assistant like Amazon’s Alexa could find the best candidate for a critical role just by asking it?
By leveraging the power of social networks and data and analytics, enterprising employers already are sourcing, screening and retaining talent more efficiently and effectively, so this scenario is not that far away.
Consider what people analytics are bringing to the talent hunt:
Networked talent sourcing
Savvy sourcing recruiters find the right talent faster by leveraging social networks, web 2.0, newsgroups, blogs and online data sources to scan “passive” talent pools. Oracle’s subsidiary Opower employs such talent analytics to hire about 200 employees annually. And it specifically uses a big data approach to identifying diverse talent most receptive to a job change. As a result, female hires increased to 47 percent from 40 percent and minority technical hires jumped to 11 percent from 1.5 percent.
Analytic screening and assessment
This recruitment approach moves things a step farther by automating parts of the hiring process to predict which candidates will be high performers and cultural fits. This proves invaluable since bad hires prove expensive: roughly 30 percent of the person’s first-year earnings, estimates the U.S. Department of Labor. Google has used people analytics to identify false negatives in rejected candidates based on profiles of successful employees, and it subsequently asks missed candidates to reapply. Xerox uses online tests that have cut attrition by 20 percent.
Keeping star talent ranks among companies’ biggest challenges since over 60 percent of employees could be tempted to take a new job. By using a predictive retention model, which delivers deeper insight into who is likely to leave and what motivates them to stay, companies find they hold their top performers more cheaply and effectively. It draws on both internal and external data (e.g., LinkedIn profiles). A U.S. insurer, for example, used analytics to show that the quality of direct supervisors, recognition and training most determined turnover. This enabled the company to eliminate costly signup bonuses that had little impact.
These examples only scratch the surface of what’s already possible in securing and retaining talent. They serve as a harbinger of the transformation ahead – thanks to people analytics.
What we know so far is that employers require three foundations to create value from people analytics. They must:
- Identify use cases with clear and measurable goals – True value is driven by identifying testable data and structuring hypotheses about your workforce.
- Gather and structure your people data – Understand the data you possess and its quality. Also be able to recognize the data you lack.
- Align the executive team – Grasp the value of predictive people analytics to ensure that test cases are completed and analyzed to free up the required investment in getting results.
We still must wait a while before we ask an AI-powered personal assistant like Alexa to, for instance, “tap our external recruitment sources to find a new operations chief.” In the meantime, the savvy organizations that use people analytics well will undoubtedly gain a distinct competitive advantage.
Cover Source: McKinsey&Company