The Big Data Picture
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.
In researching this series of posts, I spoke with most of the Horsefly Analytics users. Folks who use labor market insights in their day to day work. In particular, I wanted to explore the data they really used, and how they used it. Obviously the answers were a bit different, whether the employer was an RPO, a private sector organisation or a government department. The answers varied if the user worked in marketing, recruiting, sourcing or leadership. There was however, one common theme that all of the users echoed, they wanted to establish themselves as an expert voice to be listened to. It is the one key skill that the modern recruiter needs above all else, and labor market insights brought credibility to an argument or position. It demonstrates research, analysis and the facts behind any recommendation. The modern recruiter, or marketer needs to be able to paint the picture of the now, and pick out the trends that are important, in order to issue a credible call to action.
I always start by picking out the inputs that will produce the desired outcome ie: the hires needed. The key metrics to measure in any hiring project are:
- Activity - the volumes needed
- Efficiency - the conversion of the activity
- Result - Outcomes
- Applicants: 220
- Video interviews: 40
- Submissions to hiring manager: 20
- Hiring manager interviews: 10
- Hires: 2
- This results in efficiencies of:
- 1/110 applicants to hire
- 5.5 applicants to video interview
- 2 video interviews to 1 submission
- 2 submissions to 1 interview
- 5 interviews to 1 hire
The base numbers enable you to forecast what activity is needed if you are going to need to repeat the same hiring. I find it useful to track the numbers for all roles in order to benchmark one against another, to identify which roles are going to be hard to fill, and progress against the needed activity. Used correctly, this will help you to set an attraction budget by role, appropriate to the job in hand.
This is where labor market insights come into play. When we consider activity, efficiency and result forecasts, you should really focus your efforts in improving in one area. This is either going to be by attracting more applicants and improving volumes, or attracting better qualified applicants and as a result, reducing the number of candidates needed to get to a successful hire. This data gives a micro view of the numbers needed to get the roles filled. Unless actions are taken to change the numbers for activity or efficiency, there is no reason to believe that the outcomes of any hiring projects are going to be any different.
When you know the number of candidates needed to fill a single, or a number of roles, you can match this against the labor market data on a macro level (the whole market view), and then drill down on a micro level by location, job title, demographics, diversity, salary levels, hiring landscape (competitor employers for the same talent), etc. This will give you the all important context of your story, in order to get your “ask.” At the top of the post, we spoke about the need for recruiters and talent acquisition leaders to develop a reputation for being an expert voice, inside and outside of the organisation.It is using credible, explainable data in the story telling that establishes expert voice, because the contextual data shows that the story is more than gut feel or at best, educated guesswork.
So where do you start with your story? Like all good stories, with “in the beginning.” This is outlining the job at hand. These are the roles we have to hire for now, and these are the metrics for activity and efficiency that demonstrate what we are going to have to achieve to get the roles filled. This demonstrates the ask, alongside the activity needed.
Next you need to bring the ask into context, in relation to the market. Here, I would typically show the labor market insights to show the level of complexity. This might include things like the number of people in the location who share the same job title, the companies they work for, the average salary levels against the proposed salary levels you are hiring at, the competition for the same talent in the location etc. The more specific you can make the data you are showing, against the role, or roles you are hiring, the better you can build an understanding of the complexity of hiring. When the need, and the market position is properly understood, it is much easier to move to your own recommendations on what can be adjusted to get the job done. A very real example I was given of this was a government department who were able to use this data to recommend that they had a much better opportunity to base a department in Manchester, where there was a wider candidate pool and less competition, compared to the proposed location in London, and they could demonstrate the difference this would make in some nice graphs and charts.
Sometimes you are going to win the changes you need because of the credibility of your argument, based on the relevant data you can show, other times it may not be possible, for any number of reasons. Another real benefit of presenting your case in this way is the defensive one, ensuring leadership understands the complexity of hiring. When the chiefs can see in data terms how difficult their ask is, and the genuine hurdles that stand in the way of success, then it is possible to balance expectation with reality. That could win you a bit of extra budget, or it could prevent you getting fired!
In the next post in this series, I will be talking about the banana effect, which is one of my favourite data stories. This will bring the whole series together.
In the meantime, be good to each other.