Ep 33: Getting Started With Recruiting Analytics

Talk of analytics and data has been everywhere this year with their importance empathized by numerous, blogs, whitepapers and conference presentations.  Very often though getting started with analytics can be difficult and with a seemingly endless array of technologies on the market many Talent Acquisition professionals are confused about how to get started.

My guest this week is Matt Bradburn, Talent Acquisition Manager at Lyst where he has spent the last few months implementing recruiting analytics.

In the interview we discuss:

•    How data can’t predict the future but can help you to make incremental gains by understanding what has happened in the past

•    Why you should ignore some of generic advice on analytics and focus on what is useful for you

•    How Lyst have used metrics to reduce interviewing time while simultaneously raising quality

•    How his team measured the engagement of their sourcing outreach and now achieve a 60% response rate from the messages they send

•    Why you don’t necessarily need complex technology to be successful with analytics

Matt also gives us his views on the future and talks about the one metric which he would love to able to measure

You can subscribe to this podcast in iTunes

Recruiting Future Podcast

One thought on “Ep 33: Getting Started With Recruiting Analytics

  1. Interesting to discover your Podcast, if I may add a few comments:

    For the lack of “evidence” of how HR Analytics actually work: we are at a stage where providers must understand that they can only run algorithms on quality data (and not on millions of resumes that are carefully and intentionally biased) – this structured data is often called a data lake.

    Also, your example is for a company that is very used with metrics due to their domain of activity. So they started by defining the metrics even at Lyst – and this is something most HR Tech consumers have no idea about. Starting to track interview scores is a great idea. Also, defining a max. number of metrics is important.

    Going with Google docs (as the “data lake”) was probably best, given their budget – I doubt any ATS could have provided them with data structured any better. (and as I understand this was more like an experiment / discovery stage). 60% response rate from engineers, and saying openly “there are too many data silos at the moment” – that is saying a lot about the quality of their people.

    For tracking these metrics, and getting value from them on the long run, they should probably implement a real data lake (simply put: a database) and to constantly spend time on tracking metrics as they come in on a recurrent basis. This how HR analytics can tackle retention, engagement etc..

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