Recruiting Metrics: Are you focusing on the right bullet holes?

The term “War for Talent” has been around for the last two decades, but many recruiters feel as though they’re in a real fight for their life. Many recruiters return from the talent battlefield beaten, exhausted and with torn armor, to face a barrage of data demands from their leaders. In these moments of exhaustion, recruiters tend to focus on the wrong data to win the war. A focus on the wrong recruiting data will give you the wrong solution.

During World War II, the Allies faced an incredible enemy who out maneuvered and outsmarted them often. The air bombardment strategy seemed to only put a minor dent in the enemy’s defenses while slowly depleting their reserve of fighter aircraft.

Senior officials engaged researchers at the Center for Naval Analysis to determine why bombers were getting shot down on runs over Germany. The naval researchers knew they needed hard data to solve this problem and went to work. After each mission, the bullet holes and damage from each bomber was meticulously reviewed and recorded. The researchers poured over the data looking for vulnerabilities and ways they could strengthen their armor.

Over time, the data seemed to tell a story:

Anyone looking at the diagram above would assume that strengthening the armor over the wings and tail would be the best solution. After all, that’s where most of the enemy fire was concentrated right? Before the planes were modified, a Hungarian-Jewish statistician who had fled Nazi Germany named Abraham Wald reviewed the data. 

Wald’s review of the data led to one major question: What’s missing in this data set? Answer: the bombers that DIDN’T make it back to the base. This was a critical piece of the data set to consider but how would they be able to retrieve the downed planes in the heat of war?

Wald made the assumption if the data was based on those bombers that survived, rather than looking at what areas of the plane were tattered with gunfire, he would focus on the areas that were not. These surviving bombers rarely had damage in the cockpit, engine, and parts of the tail. This wasn’t because of superior protection to those areas. In fact, these were the most vulnerable areas on the entire plane.

This story is a vivid example of survivor bias. Survivor bias is when we only look at the data of those who succeed and exclude those who fail.

Recruiting Data

It’s easy to focus on the areas you seem to get beat up the most in recruiting. And let’s face it, the metrics in our industry really haven’t changed in decades. Organizations love to track time to fill as a measure of success when they neglect to also track more vital leading indicators of success. It’s stupid to track Time to Fill if you are not tracking this metric. Why? Because you’re not looking at the data that’s missing?

The typical approach to metrics is reviewing the lag metrics versus leading measures. If you want to drive down your time to fill, you can focus on closing jobs quicker by reviewing those jobs that closed below your goal. Reviewing the “why” behind those roles closed above your goal might give you more insight?

• Hiring manager feedback is taking too long (Find ways to solve this here)
• There’s a talent shortage
• The compensation is off the market rate

These are just some of the reasons you might find when looking at the “missing” data to drive down your time to fill. This data may allow you to review the process to remove inefficiencies, conduct manager training on proper feedback times or implement some crazy new method like Sprint Recruiting to help address some of these issues! (See what I did there?)

When I first began exploring how to use AGILE in recruiting, I was focusing on the bullet holes in the strategy. It wasn’t until I stopped, looked for the missing data, that I realized I was going about the solution all wrong. When solving a problem, ask yourself if you’re only looking at the ‘survivors.’ Your solution might not be in what is there, but what is missing. Take the time to think outside of the box and see what the data is not telling you. I’ve often found the answer is hiding where you least expect it.