Data-driven recruitment with a people-based approach: is that possible?

For more than a decade, most of what we do online is being collected, classified, and analyzed. We live in “the big data era” where supercomputers are processing massive amounts of data and providing insights, processes optimization, and much understanding of human behavior. Of course, all those operations are designed by people with big questions in mind.

Within recruitment, what are those pillars questions that we want to answer with data?

Let’s start with the basics, that I assume we are all doing manually or with the help of a powerful ATS.

  • Ratio for jobs ads/ views/ applicants /hired
  • Ratio between Sourced/Contacted/Interested/Hired
  • Reply/Open Rate
  • Basic Demographics
  • Pipeline speed Time per hire (and in-between stages)
  • Ratio for jobs ads/ views/ applicants /hired

Of course, it’s very interesting mixing these together to power our understanding of our processes

At the same time, with all that information we will be able to infer costs during each stage and per hire that can be a powerful asset to leverage improvements into the process.

But what about all those very much important parts of the recruitment that appear as not quantifiable like open feedback, interviews, candidate experience? Well, if we (and the whole team) document everything in a previously planned way, we can then build categories to measure and then quantify everything about our candidates. This is nothing new, Social Sciences do this all the time with different techniques. You can google if you are interested, it’s a very fascinating subject.

In my experience with data analysis, people's behavior, and recruitment, it’s very important to start always with questions that we (or as a team) want/need an answer to and then design a tool to provide that response.

  • What are candidates (hired and rejected) and team members thinking about our recruitment process?: Surveying and analyzing their experience to build actionable insights.
  • Why our candidates get rejected? Categorizing reason for rejection (we could mix this with our hardcore ATS data)
  • How to objectively compare candidates and get different insights from our interviews? Standardized main subjects to cover (even if they are open and with different paths) to compare later and also open reading our notes get common points that could provide new information regarding the position, industry, companies and people's behavior in general.

Of course, we are not data scientists (or maybe some are!) but we can be data-driven, insightful and at the same time have a personalized and very human approach in recruitment.

In conclusion, the way I see this magic combination working is a strong use of tools to automate and analyze those pipelines numbers and then a more meticulous and crafted approach to gathering insights and answers to questions we are asking ourselves as a recruitment team.

In the end, the improvement and full knowledge of the process should be our first priority to guide all our searches and decisions, don’t you think?

ps: This is a very extensive subject, and I don’t want to make a super long and boring article. I just want to encourage you to document your processes so you can measure and analyze it to be better.




I’m a data + people nerd. Studied Sociology and did a bunch of research. Im a Talent Connector, a Candidate Experience Advocate and a Data Driven TechRecruiter

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Carolina Amieva

Carolina Amieva

I’m a data + people nerd. Studied Sociology and did a bunch of research. Im a Talent Connector, a Candidate Experience Advocate and a Data Driven TechRecruiter

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