For more than a decade, most of what we do online is being collected, classified and analysed. We live in “the big data era” where supercomputers are processing massive amounts of data and providing insights, processes optimisation and much understanding of human behaviour. 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 data recollection and analysis that I assume we are all doing manually or with the help of a powerful ATS.

• Ratio 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)

Of course, it’s very interesting mixing all 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 information can be a powerful asset to leverage improvements into the process.

But what about all those very much important parts of 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.

In my experience with data analysis, people behaviour and recruitment, it’s very important to start always with a questions that we (or as a team) want/need an answer 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 analysing their experience to build actionable insights.
• Why our candidates get rejected? -Categorising reason of rejection (we could mix this with our hardcore ATS data)
• How to objectively compare candidates and get different insights from our interviews? -Standardised 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 behaviour in general.

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

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

At 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 analyse it to be better.

This article was originally published on Carolina Amieva’s LinkedIn in September 2020.