Actionable Predictive Analytics — the Next Big Thing in Talent Management

Every leader wants to know what is the “next big thing” in talent management? Well in my book, it is the forward-looking talent management approach known as predictive analytics. If you are unfamiliar with the term, predictive analytics are simply a set of decision-making metrics or statistics that alert or warn decision-makers about upcoming problems and opportunities in talent areas like recruiting and retention. Predictive analytics are clearly superior to traditional HR metrics, which simply tell you what happened last year.

What happened last year is unlikely to be an accurate indicator of what will likely happen this or next year. For example, last year with high unemployment rates and a weak economy, turnover rates were low. But it would be a fatal assumption to assume that those low turnover rates would continue in an improving economy.

Without Action, Big Data Is a Big Waste

But even predictive analytics have limitations, because it turns out that providing decision-makers with large volumes of data and information does not automatically result in better talent management decisions. Even predictive analytics can be labeled as “so-what metrics” because they don’t excite or alarm the reader. Consider “actionable predictive analytics” which add several factors (i.e. cost and recommended action factors) that increase the likelihood that decision-makers will take some action after reviewing the analytics. Remember that that is the goal, to increase the speed and quality of talent management decision-making as a result of providing the right amount of information, in the right format at the right time. Perhaps an example illustrating the difference between the three different categories of metrics would be appropriate:

The Top 10 Benefits of Using “Actionable Predictive Analytics” in HR

Traditional HR metrics are overly simplistic in that they merely report what happened last year. Much like telling you who won the Super Bowl last year, historical metrics don’t add as much value as telling you six months in advance who will likely win the Super Bowl this year. Analytics are superior because they analyze past and current data and reveal patterns and trends. If you are trying to sell your leadership on switching to analytics, below you’ll find a list of the top 10 factors that make “Actionable Predictive Analytics” superior.

• Time to prepare — predictive analytics tell you what is about to happen, so that you have time to prepare a plan to mitigate the damages or even avoid the upcoming problem altogether. On a positive note, they can also warn decision-makers about upcoming talent opportunities, like an upcoming period of reduced talent competition in the recruiting marketplace.
• An opportunity to be strategic — part of the very definition of being strategic is to be forward-looking, so implementing predictive analytics can demonstrate to executives that you are acting strategically by forecasting, assessing risk, and preparing for the future. And because other business functions have been using them so long, joining them will make you appear more businesslike and building internal partnerships with already successful users can make implementation much easier.
• Become aware of shifts in historical patterns — predictive analytics let you know which of the past historical patterns will remain steady or will no longer hold true for the future. This information allows decision-makers to shift their focus toward those changing patterns.
• Understanding why those shifts are happening — the best predictive analytics identify not just which factors are changing but also why they are changing, so decision-makers can implement solutions that best fit the root cause of the problem.
• Becoming aware of new relationships in talent management — predictive analytics can reveal new relationships between talent factors that did not exist in the past. For example a policy shift requiring part-timers to work under 30 hours per week may have unintended consequences in that it could now significantly hamper recruiting and increase turnover problems in jobs that had no past history of recruiting or retention problems.
• Increasing the odds that decision-makers will act — because actionable predictive analytics “advise” decision makers on the estimated costs of upcoming problems and which actions have the highest probability of solving the predicted problems, decision makers are more likely to actually take action when they are learned about an upcoming problem.
• “Time to the answer” may be reduced — making decisions fast is also central. Because most predictive analytics approaches are more integrated and comprehensive than traditional HR metrics, the time that it takes to get an answer to a decision maker’s query may be reduced dramatically.
• They can allow you to model — advanced analytics processes allow decision-makers to develop a model which allows them to try out different alternatives and to vary the constraints and the assumptions in order to see how the results would change. Such models or if-then scenarios can excite decision makers, while at the same time helping to avoid major errors through pre-testing.
• Provide a competitive advantage — if your talent competitors don’t develop predictive analytics, the predictions provided to your decision-makers can provide your firm with significant talent and business advantages.
• They can cover all critical talent areas — predictive metrics can provide heads-up alerts in all of the important talent areas including recruiting, retention, leadership, performance management, and internal movement.

Predictive Analytics Are More Common Outside of Talent Management

Predictive analytics may be new to you because they are in fact relatively rare within talent management, but they have been around for decades in the business world.

The most common example is weather prediction. Predictive analytics allow businesses and farms impacted by weather to prepare for upcoming weather events. Hurricane predictions for example have become amazingly accurate as a result of the use of “big data” and statistical approaches which predict upcoming storms. Predictive policing is becoming more common where analytics help police departments know in advance where and when crimes are most likely to occur in their city. Predictive analytics have recently become extremely popular in the area of consumer behavior, where they have been used to predict future shopping behavior and changing patterns. The insurance industry gets the nod for the longest history of use with predictive analytics they have used to identify patterns of illnesses and accidents.

Within talent management, Google has excelled, producing predictive analytics in hiring, leadership, and retention. In the retention area, Google learned to use a combination of seven different factors to predict which employees were most likely to leave (in some cases, before the employee actually realized it themselves). In other cases, Sprint used analytics to predict which new hires were likely to quit and Cisco once used predictive metrics to identify which struggling new hires were likely to succeed over the long term.

Note: In next week’s follow-up article on ERE.net, “Implementing Actionable Predictive Analytics In Talent Management,” I will describe the components of high-impact “actionable predictive analytics” that encourage managers to act on upcoming talent management problems and outline the functional areas of talent management that predictive analytics can cover.