In recent weeks I’ve been snooping around the edges of the market-formerly-known-as-human-resources and which now is being repositioned either (grandiosely) at talent management or (understatedly) as workforce management.

I lean toward the more mundane workforce term.

More to the point, a number of software companies are moving toward a more science-based, analytic approach to helping companies attract, hire, and retain employees that will ‘fit’. Companies like Evolv and Kenexa are pushing hard to make a reasonably obvious case, that applying scientific analysis to the issues involved across the life cycle of workforce management.

Some of the science that underlies the work of these companies is social science. For example, Kenexa published a recent  Worktrends report subtitled Why Two Out Of Three Of Your New Hires Are Considering Leaving And What You Can Do About It? which is backed up by survey results. And what does that survey show? 64% of new hires that answered “I did not receive the training to perform my current job effectively” intend to leave their new job, and 15% who answered that way were undecided. Clearly, this research would lead companies toward more aggressive efforts to train new employees. But that is not reflected in the software, per se, but could find its way into tools organized around competency management, as Kenexa offers.

The more tangible sort of workforce science is the growing opportunities in analytics, Evolv, for example, makes the following assertion:

Recruiting and Selection

Recruiting the best employees depends on finding the right fit for the right jobs at the right time. We combine workforce science with workforce analytics backed by massive amounts of Big Data. We use them to build selection models that adapt to changing macroeconomic conditions while simultaneously improving precision. We also incorporate individual client outcomes to enable the core selection algorithms to not only become ‘smarter’, but customized to each client’s unique workforce dynamics.

There is a great deal of research to support the claim that ‘fit’ matter in hiring. Industrial organizational psychology has different camps: some that argue the candidate needs to be matched to the skills and psychodynamics of the role that are supposed to fit, while others point to findings about the fit of the candidate in the company’s culture, and of course, those that say both are important. Not to waffle, I am going to say that conditions vary widely based on a lot of parameters, but I am a believe in fit in general, and particularly with regard to company culture: I am less of a believer in extremely tight and narrow assertions about specific personality traits best matching specific jobs, to the point where a company rules out people for a call center job because they have too much empathy, or curiosity, as seems to be the premise in this recent piece:

Joseph Walker, Meet the New Boss: Big Data

Xerox is being advised by Evolv Inc., a San Francisco start-up that helps companies hire and manage hourly workers. By putting applicants through a battery of tests and then tracking their job performance, Evolv has developed a model for the ideal call-center worker. The data say that person lives near the job, has reliable transportation and uses one or more social networks, but not more than four. He or she tends not to be overly inquisitive or empathetic, but is creative.

Applicants for the job take a 30-minute test that screens them for personality traits and puts them through scenarios they might encounter on the job. Then the program spits out a score: red for low potential, yellow for medium potential or green for high potential. Xerox accepts some yellows if it thinks it can train them, but mostly hires greens.

Though hiring is a crucial business function, conventional methods are remarkably short on rigor, experts say. Depending on who decides, what gets candidates hired can vary wildly—from academic achievement to work experience to appearance. Managers who go with their gut might get it right sometimes, but their hunches generally have little value in predicting how someone will perform on the job. Companies peddling a statistical approach to hiring say they can improve results by reducing the influence of a manager’s biases.

Kenaxa (and parent IBM, who acquired Kenexa for $1.3 billion) have coupled the company’s future to the idea of a “smarter workforce” based on analytic techniques underlying workforce management, and reflected marketing sizzle based on IBM’s other “smart” initiatives, like Smarter Cities. SuccessFactors (an SAP acquisition) comes close to infringing on that positioning by suggesting that companies might “work smarter with innovations from SuccessFactors.” And what puts the smarts in these tools? The combination of organizational and behavioral sciences, coupled with large-scale data analysis is likely to lead to systemically better results in workforce hiring, training, management, monitoring, and retention.

I buy the premise, and I will be trying, over the next few months, to look at how this plays out at each of the stages of the workforce life cycle. Zooming into different tools, and examining them in the light of my own psychodynamic 3C model of business. I wonder whether these tools work only in the competitive and collaborative sorts of businesses, or if other lighter-weight and bottom up sorts of tools are needed in the cooperative business, now starting to emerge?