The Holy Grail, Bigfoot and quality Adam Sandler movies all have one thing in common: people swear they exist, but haven’t been able to prove it. (Okay, that may be too harsh, since we did enjoy “Happy Gilmore.” But I digress.)
The same can be said for workforce mobility analytics. Yes, the data behind your mobility program tells a story, and that story can make a compelling case for just how much mobility matters within your organization. But where analytical data has been helping other business units improve planning and spending for decades, it’s still a mostly untapped resource in the workforce mobility realm.
So if you’re a mobility manager looking to harness the power of analytical data, where do you start?
Experts agree that the most important first step is to determine the impact of the analytics you are striving to create. Ask yourself: what are the critical questions we need answered with data? What decisions will be made based on these answers?
When it comes to mobility data, you want to focus on two categories: descriptive and predictive.
Descriptive analytics focus on what is happening – counts, rates, volume, costs and demographics (age, tenure, gender). Predictive analytics look into implications about cause-and-effect relationships and provides insights into the “why.” For example, why do international assignees have a higher turnover rate, or what drives leadership readiness? The latter requires statistical modeling to produce meaningful or actionable insights.
Companies continue to struggle to provide meaningful metrics, let alone analytic insights, which often results in the lack of aligning mobility with their talent management objectives.
By beginning with descriptive analytics you can start that alignment process. For instance, one of my clients was able to demonstrate how “mobility” was supporting the company-stated talent objective of increasing diversity and inclusion, by analyzing and presenting demographic data on the mobile workforce. Another client was able to demonstrate that it cost less to fill open positions by offering a relocation package than the opportunity cost of not being able to fulfill client projects.
By using both a descriptive and predictive approach on analytics, the analysis should be able to answer the following questions:
By developing analytics, we have seen companies formulate the right set of actions and best practices necessary to optimize their mobile workforce.
Evidence suggests that more nimble companies (with higher mobile populations) enjoy higher revenue growth. To learn more, see our Whitepaper, Mobile Workforce Analytics: Proof that Mobility Matters.
Once you learn the proper way to use them, mobility analytics will help you manage your mobility program more effectively and add greater impact to your company’s business strategy.