Recruitment

How the University of Cologne Uses Data to Make Recruitment Decisions

2 min read · By · Published 3 months ago

University of Cologne is one of the oldest and largest universities in Germany with over 48,000 students. We spoke to Dr. Maria Schmitz-Hüser, Project Manager, Strategic Personnel Management at the University of Cologne to learn more about how the university uses data in its recruitment. The interview has been edited for length and clarity.

Why do you think it’s important to take a data-driven approach to talent attraction?

Data is especially important for positions that need to be filled within a short period of time or that are difficult to fill.  It’s also very important in the cases in which we are very under pressure to find someone, for example, not just in the academic staff but also apprentice (Auszubildende) positions. We have a lot of these positions, like 12 in a year, and they’re not always easy to fill. But we can use our data to look for a strategy and see where the right place to advertise them is. 

Are there certain metrics that you found to be most crucial in determining if a recruitment is successful?

Dr. Maria Schmitz-Hüser

Yes, time to hire is the key metric actually. Sometimes we have to distribute the same position one more time, and another time, and even another time. If a position is not running that well, then it’s a position where we have to change our strategy and start a campaign or something to be more visible. 

So you keep track of your past recruitments and the different providers you’ve used to see which perform well which helps you then when you end up in a situation like that. Can you give an example of how you have adapted your strategy  in response to data?

Not every provider gives us data. This is a problem because then we can only judge a position by the applications we get and the number of clicks the job gets.  Academics Positions, for example, you support us with frequent reporting and this helps us because  we can see which jobs get many clicks and which jobs don’t. When many people click on the job but only a few of those people then click on apply, we’ve noticed that this is an effect of giving the position too broad of a title. When the title is very general, everyone clicks on “read more” and then realizes that it’s not what they’re looking for. So based on your data, we have changed the way we name our positions.

If an organization is new to using data in their recruitment decisions, what advice would you give them? What do you think should be the first step to a more data-driven recruitment approach?

The first step is to see how many positions you have to fill and then to determine how quickly you need to fill these positions. How many runs do you think you will need to fill them? How many applications do you generally receive per position? Based on that, you can identify the positions that are running well that you can just let run. But if you have a position that you know will need more runs and it isn’t getting a lot of applications, these are the KPIs which help you see what you have to change. Then if you change something in the next run, these KPIs will show what the effect was.  

Another thing is the quality of the applications. In an applicant tracking system, you can also track how the candidates perform and rank them based on how they really fit the position. Then you can track, for each position, how many applicants really fit the position which tells you that you managed to attract exactly the group of people that you were looking for. 


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