On January 22nd of this year, corporate members of i4cp's evidence-based HR research working group met at the Hertz headquarters in Park Ridge, NJ. Jay Jamrog, i4cp's SVP of Research, asked the group this question about predictive analytics: "Is anybody here creating predictive analytics that aren't about turnover?"
The room was silent.
Here's the thing I often ask when discussing predictive analytics: What is the endgame? The concept is solid in theory - with the right data, reasonable assumptions about the future can be made and thus you can position your investments to get you out ahead of your competitors. Predicting the future gives you the ultimate in flexibility and adaptability.
But what's predictable?
Of course, when dealing with predictive analytics, we're really talking about probability rather than the pure statistical modeling - more Nate Silver
than SPSS. There's nothing inherently wrong with that, but it does mean there should be some discussion about what is realistic. Can certain statistics even be predicted? Looking at our latest research on analytics
, the top five metrics identified by respondents when asked: "What do you measure?" are outlined in the chart above.
Move from efficiency metrics to effectiveness metrics
Referred to as efficiency metrics, the metrics listed above are often the easiest to collect and generally measure how well HR executes its tasks. Efficiency metrics fall into the "necessary but not sufficient” category. They are worth paying attention to in order to manage HR better, but measuring them is not a driver of business success. Do most business leaders really care about how many trainings were completed last month? How long it takes to hire a new employee? How many people are leaving the company?
The better reward for your investment of time and money comes from effectiveness metrics, which measure the intended impact of HR programs and practices upon the broader organization. Looking at the top five most popular metrics, there is a marked lack of effectiveness metrics.
Here are three effectiveness metrics that, if you aren't doing so already, you should consider tracking:
- Quality of attrition
Knowing that 100 employees left your company last month is mildly useful. But how many of those employees were fired? Retired? Were many of them low performers, or did you lose some of your best employees? Tracking the quality of attrition enables organizations to gain a sense of the broader stories underlying talent management, including effectiveness of frontline leaders. Developing a more comprehensive understanding of when attrition occurs, and within which groups of employees, can empower an organization to take action in areas that ultimately strengthen it and contribute to better performance
- Quality of hire
Your company filled 100 positions in record time last month. Great. But are those new hires any good? Quality of hire communicates the value an organization is getting for effort and money spent on recruiting. In addition, it also reflects how effective your business is at assimilating its new hires into the organization and works with them to ensure appropriate alignment and development. Indeed, quality of hire is as much a post-hire measure of organizational effectiveness as it is a pre-hire measure of success. It is important to note that quality of hire should be defined based on the business as well as the job role. However, i4cp's research has revealed that certain metrics should be considered, including: first year or project (if contracted) retention rate, first year (or project) performance ratings, and hiring manager satisfaction. Watch i4cp's video on How to Measure Quality of Hire for more details.
- Quality of movement
Most frequently, internal movement is associated with an employee receiving a promotion. Increasingly, firms recognize that lateral or even downward movement can be useful. When any type of internal movement takes place, an organization needs to know whether or not the action has addressed a need and delivered value toward achieving company objectives. To truly assess the value of a move, the company must not only track it, but also look at the results that occur because of the move. Did productivity for that position rise? Has the promoted worker remained in the job for a specified period of time?
But what about predictive metrics?
Again, none of those effectiveness metrics are tied to anything predictive. So although some of the larger companies are working on predictive analytics, the best use of time for most organizations is in developing effectiveness metrics.
This is not to say that predictive analytics aren't worth keeping an eye on. As the techniques and models become more refined, there will be greatly accessibility to predictive models. Still, proceed with caution, as predictive models don't always guarantee predictability.
For instance, you may have heard about Big Data. Big Data has a lot of virtues, but one foreseeable issue with huge data sets is that everything can be correlated to everything. The result: making predictions based on those correlations leads to what I call "The world is flat" syndrome, in which assumptions are made based on goofy correlations with no real relation to what's actually causing the change.
There is a future in predictive analytics, but that future is too complex to predict accurately. My prediction? Effectiveness metrics will continue to have the biggest financial impact, at least for the foreseeable future. HR organizations should therefore make sure they are measuring the effectiveness of human capital and its impact on business performance. To get started, or improve existing efforts, we recommend you start small (e.g., focus on critical job roles within a function or division) and use the data you already have or have relatively easy access to. Then, pursue these four actions:
- Create company-wide standard definitions: Long before decisions can be made using data, the data itself must be reliable. Effectiveness measures need to be consistent across the organization and reflect accurate, up-to-date information in order to be relevant and usable. There are many techniques that can be used to ensure timeliness and reliability, including automation of processes and the formation of data councils.
- Standardize reporting: Having a familiar look to published reports is more important than many people realize. In addition, consistency in reporting will allow you to set internal benchmarks and see changes over time.
- Identify the components of the effectiveness metric: If you are measuring quality of attrition, for instance, you need to know your critical roles. Anything that is measured and tracked should be in alignment with broader, strategic talent management.
- Beware of false trends and generating causation from correlation: The desire to predict based on perceived trends can be strong. Remember that there are hundreds of factors, if not more, than can affect any given outcome.