A New Metric for Evaluating Predictive HR Tech Solutions
In a recent article, “A New Wave of HR Technology Consolidation Begins,” leading industry analyst Josh Bersin heralds the rise of new vendors offering innovative HR technologies that are built around “’productivity’ and ‘engaged experiences’ not just ‘best practice HR practices.’”
These new technologies are designed to solve a problem many companies face: they need HR tech tools that employees will actually use. Bersin writes, “We are moving away from an era of ‘systems of engagement’ to ‘systems of productivity.’ If the software doesn’t help us get work done, we rarely use it.”
People Analytics: Often Underused or Misunderstood
This move to “systems of productivity” also needs to be applied to people analytics technologies. Perhaps no HR technologies are more often underused or misunderstood than analytics tools.
Two factors drive this underuse and misunderstanding:
Many companies have only modest in-house analytics expertise.
As analytics capabilities within HCM solutions progress, they often become more complex, which can make them more difficult to use or understand.
As analytics technologies progress, it will only become more important for companies to evaluate whether technologies will be “systems of productivity” in their organizations. In many cases, the right analytics technologies for companies will have robust analytics but won’t require users to actually have robust analytics expertise to create value from them.
Evaluating Predictive Analytics Solutions—TtPV
Some of the most powerful advances in HR tech are occurring in predictive technologies. By being able to predict the propensity or likelihood of actions (such as when a passive candidate is likely to be most receptive to a job change conversation), these technologies give companies a significant advantage over those having to rely on gut feel.
As with other HR technologies, companies need to evaluate predictive analytics solutions to determine if they are “systems of productivity”—tools they will use. But with HCM solutions that offer predictive capabilities, there’s another important factor companies need to consider in their evaluations: the time it takes to get predictive value from the solutions.
In a new article (available for download here or by contacting us), aptly titled “Time to Predictive Value in HCM Solutions,” HfS Research analyst Steve Goldberg shows how some predictive analytics solutions have dependencies that can take months or even years to address. For example, some solutions rely on having a large enough relevant data set, or sufficient analytics or data science competencies among staff.
To help companies navigate evaluating predictive technologies, Goldberg proposes a new industry metric: Time-to-Predictive-Value (TtPV). TtPV is the average length of time it takes for a typical organization to consistently experience the predictive capabilities within a technology tool or system, and therefore derive meaningful and incremental business benefits from that solution.
Goldberg writes, “The suggested metric is a guidepost that could help organizations more realistically and accurately assess the potential impact of sophisticated HR technology that includes predictive capabilities.”
Obviously, companies want TtPV to be low—ideally zero. Any significant delay in generating value is likely to significantly reduce the value—and use—of a predictive technology. As Goldberg notes, the business value of reducing time-to-predictive-value in these technologies is somewhat akin to the business impact of reducing time-to-productivity with employees.
Goldberg uses the ENGAGE recruiting technology platform as an example of a predictive technology with a low TtPV. The technology, which predicts when passive candidates may be ready for a job change, sharply limits time-to-predictive-value because:
Customers don’t need to spend time accumulating an adequate data set since they are given access to a pool of millions of passive candidates from day one.
The company data set and candidate pool is updated continuously, as are key changes or compelling events that impact companies or individuals.
Unlike many predictive engines’ time-consuming auto-correction and adjustment cycles, ENGAGE simply auto-updates the most relevant and recent person, company, industry and other external data.
The factors that form the tool’s predictive value are not industry-specific, enabling customers to simply “plug into” the system. The same core data points almost always correlate with key people being willing to consider an employment change.
An Increasingly Vital Role
People analytics, and predictive capabilities in particular, will play an increasingly vital role in moving the HR technology landscape forward.
To maximize the benefits companies receive from these technologies—to maximize customer adoption and value realization—it’s critical that they these tools are true “systems of productivity,” and that their predictive capabilities create value in very short order.