Here is a brief excerpt from an article written by Carla Arellano, Scott Keller and Kelli Moles for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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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.

Predictive retention: 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.

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Here is a direct link to rhe complete article.

A Partner in New York, Carla Arellano brings a background in finance and organization in helping companies strengthen and transform their culture and talent management and create value using people analytics.

Scott Keller is a Senior Partner, Southern California, who counsels Fortune 100 leadership teams on enterprise-level transformation, culture-change
programs, top-team performance, and CEO effectiveness