What is the "Complexity to Fill" score and how is it calculated?
Horsefly Talent Analytics Insights
There is a lot of talk about the latest phase of recovery from the covid period. Have you noticed how everything is preceded by “Great.” Currently, the most popular term and discussion point goes under the label of “the great resignation”. It was where they were moved to post “the great rehire”, which followed “the great lock down.” Everything, it seems, has to be great, but is this great resignation really what it seems, or is it best described as “hyperbole.”
If you read my last post on this blog, you will understand why this is labeled part 2. It’s not a sequel, or a prequel, this is the second part of a trilogy of blogs outlining how to tell data stories to get favourable outcomes. How do you use numbers to convince the chiefs to do what you want in a credible way, and if necessary get the money you need to do it. This could be something as simple as changing the salary, location or expectation listed on a job description, or something more challenging like restructuring the way you have traditionally hired. The data is the ammunition you bring to the fight, and you need to be able to present it in a credible way. This blog series deals with the “how to.”. If you missed the first one, you can read it HERE.
I was asked to meet a friend who is a talent acquisition leader for “coffee” recently. These requests have been coming back fast since the lifting of restrictions, and I have to admit that I have really missed meeting folks reasonably randomly for “coffee”, in and about town.
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It doesn’t seem possible that anyone can make a comment in any of the recruiting Facebook groups without being asked for the source and validity of the data. This is understandable, given that so much data is shared in a debate, often without reference to where that number has come from. Recruiting, in particular, has also suffered from urban myth syndrome. Popular truths, that said enough times by enough people become accepted law, even though they don’t necessarily stand up to a data sniff test. I often find myself asking: “Do you think that, or do you know that?”
Increasingly, talent acquisition leaders are being asked to become an expert voice within their organisations. I once heard a half joking quote in a conversation at a conference, when we had those, that has stuck with me for a number of years since:
“In God we trust, everyone else bring data!”
Google tells me that this can first be attributed to American Management Theorist W. Edwards Deming.
Whilst there is some dispute in who said the exact quote first, I think you get the point. This was quoted in a book in 1986 when we were first being asked to show some proof behind our proposals and statements. Jump forward to today, and we have a very different challenge. In a world where every click, task and action is digitally recorded, we often have the problem of too much data to navigate. Developing an “expert voice” around your area of interest is less about bringing data, and much more about defining the right data, applying context and being able to define and communicate meaning. This is a critical skill in establishing a credible expert voice in an organisation. The art of “big data analytics” is really being able to turn lots of numbers into meaning, and communicating why this is important, to someone who is probably not an expert in the discussion topic, and be sufficiently credible to get their support.
Over the last few months I have been speaking to the users of Horsefly Analytics, to understand how and why they use the labor market data available. One of the questions I always ask is what makes them confident enough to present Horsefly’s data to support their expert voice. They are, after all, staking their reputation and in some cases, careers, positively and negatively on actions resulting from their recommendations. The source data has to be right.
The answer that I’m continually getting is that although there are sometimes surprises (and that’s healthy), the numbers, trends and conclusions feel right to someone working in the market day to day, and come from a broad enough range of sources to present real trends, whilst picking up on variances globally.
My challenge was that I was hearing about trillions of data sources, and that felt too big to imagine. Kind of like trying to picture infinity. I asked Horsefly CEO Will Crandle to break down for me how they acquire so many data points, extract what they need and derive meaning in a fast time frame. Answers to queries need to come quickly. This is Crandle’s answer to my question on why I should trust the data:
As part of the work I’m doing with Horsefly Analytics, I get to ask for labor market reports that provide background data for some of the questions I have. Just recently I have been digging into the job market as we exit lockdowns across the globe. I understand that each nation will have a different pace to this, but at some point soon we will be able to pick up trends, and link job openings to exit from lockdown restrictions, in order to forecast what might be coming down the road.
I was prompted into this research when trying to understand the trends impacting jobs advertised on job boards and aggregators. Whilst I understand that not all jobs are advertised, this data does give a good indicator of how the market is moving. The value of the job board market is forecast to continue to grow year on year in most analysts' forecasts. I found this forecast a bit strange. Not because I believe that the much hyped “death of the job board” might come about, but the wider adoption of programmatic technology, means job ad buying has, and will continue to get much smarter. The trends I’m seeing today is that this is resulting in advertised jobs staying public for much shorter periods of time, with companies buying visibility only until a fixed number of applications have been achieved. My question was that if hiring companies were paying for jobs to stay live for shorter durations, then the spend would be less. That is, after all, one of the main reasons for adopting programmatic advertising. Even if the number of jobs advertised remained at a similar level, I would expect a forecast of market value to decline, as spend per job decreases.
I have had quite a few discussions with recruitment leaders, job board heads and others on this topic. One of the themes I’m constantly hearing is that companies are now advertising more roles, as the cost per role decreases. This is certainly what I was hearing from the job distribution companies, and aggregators. Companies are using job boards to attract candidates for a broader range of jobs, relying on programmatic technology to ensure they are spending in the most efficient way. This should not come as a great surprise, as it has marked a decrease in the cost per application, and improved the ratio of applications to hire (by reducing total volumes.)
At the same time, relying on traffic from other sources via aggregation, has seen organisations reducing the number of job boards they are using in any one campaign. I’m tracking the data on this at the moment to prove the theory, and will share it with you over the next few months, when I have a conclusive trend line. Early indications are that both of these conclusions look to be correct.
To start off this research, I wanted to understand how the advertised jobs market has been performing over the last few months as we come out of COVID restrictions across much of the globe. I asked Horsefly to come up with this data, and think some of the trends provide some new insights into what is going on. A snapshot of this data for a few selected countries in Horsefly's database is below and at the top of the page.
I have been working with data companies like Horsefly's CEO, Will Crandle, over the last three months to try to properly understand labor market insights, and what is actually useful to recruiters when it comes to filling roles. I know that there continues to be a lot of talk about how recruiters are, or could use data to perform better, beyond adopting a rinse and repeat approach to hiring. We understand and acknowledge that data in decision making is important, as we try to move from reliance on “gut feel” based on experience, to something a bit more provable.