The Anatomy of a Reactive Recruiting Strategy (and How to Get Past It)
There’s an old saying, “What you don’t measure doesn’t get improved.” Since so many aspects of business are now digitized, that statement is more powerful than ever. At the same time, though, the (always) growing number of new variables makes the tasks of both measuring and improving much more complex. Any recruiter who’s been in the industry for a while can attest to that.
The business case for using data and next-generation tools like recruiting analytics to support decision-making is becoming stronger by the day. And yet, the complexity associated with actually doing so has left many recruiting organizations in a state of paralysis—wanting to become more data-driven but intimidated by starting the journey in the first place.
In this post, we’ll inspect the challenge of getting started with data, focusing first on understanding the differences between reactive and proactive data use, and then on how to start putting your data into action.
Reactive Vs. Proactive Recruiting Data Use
Last week we talked about Carnegie Mellon University’s Capability Maturity Model (CMM), which is a widely adopted framework for process improvement initiatives. In case you missed the post, the main takeaway is that regardless of the capability, most companies tend to follow a path toward maturity that begins in a reactive state and moves toward a proactive—even predictive—one.
What does this mean in the realm of using recruiting data? The reactive state is characterized by using data to make decisions after something has happened. For instance, when someone quits, a recruiter may be notified that he or she needs to fill that position, and so begins the process of creating the requisition, posting it, building a candidate pipeline, phone interviewing, and more.
The above scenario is very transactional—the data tells a recruiter someone has left and someone must be hired. This of course sounds familiar to many reading this article. As organizations’ capability maturity progresses, though, the idea is that the use of recruiting data will become more proactive, essentially minimizing the impact of someone leaving by empowering the recruiting organization to be more prepared.
Building on this example of an employee leaving, there are a few ways organizations may use data to become more proactive. First, the team could use data to monitor the strength of its talent pipeline for a particular type of position. If, for example, we’re talking about a software engineer position at a tech company, then recruiters would benefit from understanding and adjusting the strength of the talent pipeline in that area at all times.
Another example would be using assessments to determine how satisfied employees are with their current position and the company, and then analyzing that data to make projections on which types of positions are more likely to become vacant. This alongside talent pipeline management could drastically reduce the impact of a leaving employee, lowering the cost-per-hire, time-to-fill, and likely even the quality of hire.
Examples like this aren’t groundbreaking, and there are so many more like the above, but they’re frustrating for many recruiters to read about who are using data in a reactive way. According to Carnegie Mellon, the proactive state is typically characterized by using innovative ways to enable continuous improvement—often relating to technology. This is where recruiting analytics comes into play.
The First Step In Your Recruiting Analytics Journey
If you’re in the reactive state, you may be thinking that building a business case and getting budget for recruiting analytics will be a long shot at this point. But the reality is that the consumerization of analytics technologies—think Fitbit and Nest—has really accelerated the use of analytics in business functions, and we’re starting to see that materialize as wider adoption and more accessibility in recruiting.
Still, like we mentioned at the beginning of this article, “What you don’t measure doesn’t get improved.” And the first step toward becoming less reactive, and transactional, is to start measuring what’s important to your recruiting goals and your overarching company’s goals—even if that means simply creating a spreadsheet and reviewing the numbers with your team on some regular basis.
Once you start measuring talent acquisition performance indicators, that’s what really becomes the basis for your business case. By looking at current performance versus desired performance, you can create a gap analysis to determine where additional resources are needed and what it will take to get there. But that’s a topic for a whole different post.
If you’re new to the concept of recruiting analytics, the first step is to gain a better understanding. Read our whitepaper, Analytics in Talent Acquisition: The Hype, the Reality, and the Future.