Data is everywhere in the world of modern business. Streaming services use it to recommend us films and TV shows, financial authorities analyse it to spot fraud, and medical companies track it to better understand the effectiveness of treatments. However, recruitment seems to be lagging behind, with few businesses asking how to use data to fill a role.
We want to change that. In this guide to data-driven recruitment, we will focus on how you can use all the available information to help you better understand the labour market, what candidates are looking for, and where and how you can find the most suitable candidates for your roles. By merging data analytics and recruitment, you can unlock new ways of hiring that are quicker, cheaper and more effective.
Recruitment has traditionally been described as a ‘people business’. Both in-house and agency recruiters will tell you they know the people you want to know thanks to their deep sectoral knowledge, but this is simply outdated. Treating recruitment as a people business results in only accessing candidates who see your job advert or message you on LinkedIn; a fraction of the available talent pool.
Hiring managers end up frustrated, being given candidates who are a poor fit or have salary requirements in excess of what the business can afford. However, many of the problems with this old-fashioned approach can be solved by switching to data-driven hiring.
Imagine if you could access every possible candidate for your role, not just the ones actively looking for a new job who happen to see your advert. You’d be able to put together a longlist of candidates, each of whom may be interested in working for your company and are happy with the salary you are offering. That means you’d be able to take your pick of some of the most elite talent around.
With data-driven recruitment, you don’t have to imagine this. All these things are possible thanks to a combination of labour market data analytics, smart candidate matching and harvesting, and intelligent automation. While it may seem like the future of recruitment, it can be delivered for you today.
In recruitment we often talk about ‘purple squirrel’ candidates. These are roles for which the ideal candidate has a combination of skills, experience and salary expectations that would be impossible for anyone to have. In other words, the hiring manager might as well be asking for a squirrel with purple fur: something that doesn’t exist.
Data-driven hiring uses data to analyse the existing talent pool and understand what a strong candidate looks like. Instead of looking for purple squirrels, you will be able to have a solid understanding of the job market and what you should be looking for in an ideal candidate, while still delivering on your must-have criteria.
No matter what some recruiters might say, every single role is fillable. You might have to adjust your expectations, but you will be able to quickly hire candidates that meet your needs, fit in with your company and want to work for you at the salary you’re offering. All this is possible by combining recruitment with data analytics.
If you find yourself being told by recruiters that “these candidates are the best we can find” or “you’ll need to increase the salary you’re offering to fill this role”, you only need to ask for one thing: to see the data. Don’t accept any answers around what the recruiter thinks or feels; the data is out there, and using it to drive your hiring decisions is the best way to step into the future of recruitment.
More than any other reason to embrace data, the cost of hiring means businesses can’t afford to make mistakes due to not considering all of the available information. A substandard hire not only incurs all the standard costs of recruitment, but adds a number of others and means you potentially have to either invest in upskilling them or start the whole process from scratch.
The cost of all the in-house resourcing time, advertising costs, and agency or search fees that are required to make a hire can add up to a large recruitment budget. This is a significant cost already, but substandard hires only increase it.
Here are some of the possible outcomes of a poor hire:
- The new recruit performs poorly, putting your business behind where it should be and forcing other team members to pick up the slack.
- They take extra time to onboard and train.
- They decrease morale, leading to the potential loss of talented staff.
- They make a mistake that costs your company money or even clients.
If you decide to let the poor hire go and start again, you will have to incur all the costs of recruitment again, along with the associated time and resources from your HR department. You might have to buy new equipment for each new hire, or take time to get them set up. All of this adds up to the point where a poor hire could have an astronomical effect on your business.
Another cost that sometimes goes hidden is the price of overpaying for a hire. Even if the candidate is a great fit, you could still be losing money on their salary compared to other talented options. This is often the case with small talent pools, as companies have less choice and so are more likely to need to offer a higher salary to attract the limited talent to which they have access.
A lot of this comes down to the use of data. Without analysing the market and uncovering the true size of your talent pool, you are more likely to make a substandard hire and less likely to find candidates whose salary expectations match yours. The more information you have, the more likely you are to make the right decision when it comes to your recruitment.
Everywhere you turn, you can see data being used to make better business choices. In many industries, few decisions are ever made without first consulting the data. However, recruitment is still lagging behind in this crucial area. Instead of innovating and using data, many recruiters are content to sell hiring managers the idea that they have found the very best candidates available, when in reality they’ve barely scratched the surface of the talent pool.
The methodology many recruiters still use is simple: put out a job advert and call a few candidates they already know. Even with better, more dedicated methodologies, recruitment without data is completely hamstrung, as there is no way to gain vital information. For example:
How many possible candidates are out there?
Without utilising data, there is no way of knowing how large your effective talent pool is. How many people have the skills or qualifications you need to do the job? What about if you were to hire internationally? Knowing the size of your talent pool gives you an idea of how many options you will have, and potentially how competitive you will need to be.
Will candidates be attracted by what you are offering?
What is the average salary for the type of role for which you are hiring? Could you access the same skills and qualifications for a lower salary by hiring from locations with a lower cost of living? Are candidates looking for benefits beyond salary, and do you offer these? Again, these questions are impossible to answer without using data.
Where do candidates currently work?
Which companies are candidates currently working for, and where are they based? Maybe there is a big community of people in your industry located somewhere you weren’t aware of. Maybe most of your talent pool works for startups, or multinational corporations. All of this information will help you understand where to look and what to offer in order to fill your next role.
The answers to all of these questions can be uncovered, but the only way to do so is by using data. Without combining your recruitment with data analytics, you are making your job much harder and significantly less effective.
So why use data-driven talent acquisition? Hopefully by now you should understand the disadvantages of leaving data out of your recruitment process, but the advantages are just as important to understand. Here are the main reasons to utilise this methodology:
- Using data gives you a full understanding of your talent pool, so you can reach every possible candidate for your role.
- Data can show you the salary expectations of candidates by looking at what they are typically being offered, showing you what a new hire is likely to cost.
- Recruiters can uncover locations where candidates have a lower cost of living, giving you the ability to hire elite talent at a lower cost.
- Analysis of data can give you indications of how well a candidate will fit with your company culture, even to the point of showing you which universities will produce the best fits for your business.
- By providing you with more information about your talent pool, data-driven recruitment will ensure you have a wider range of options and a better chance of finding the right candidate for your role.
How does data-driven recruitment actually work? A good example can be found in a simple place: job requirements. When companies write job adverts, they will include the requirements they believe the role requires. However, you’ll note we used the word ‘believe’. As we’ve already mentioned, without data hiring managers will be relying entirely on what they think and feel, not what’s factual.
With any role, there will always be requirements the business is not willing to move from. These are your ‘must-haves’, and they can be anything from the years of experience a candidate must have in their industry to the university from which they graduated. However, in our experience these must-haves are few and far between.
When you use data to analyse your talent pool based on your job requirements, you might find the number of potential candidates is simply too low. You might even have a purple squirrel situation, with no candidate exactly matching the precise requirements you need. However, this is just the preliminary phase of recruitment; it’s not too late to re-analyse the data with new parameters.
This is one of the strengths of data-driven hiring: you can adjust your search and your expectations before you commit to anything, preventing you from making a costly mistake and hiring someone who turns out to be substandard.
Each of your parameters can be adjusted, although some might be more important than others. Maybe a degree in a relevant field is a necessity, but five years’ experience? Five years is a relatively arbitrary number; would four and a half years be just as good? If so, adjusting that parameter could lead to a much larger talent pool for your role.
You might need to adjust a few different parameters in order to craft a talent pool that is large enough to suit your needs, or it might just take one. Whichever the case, data will have helped you find enough candidates to fill your role in a timely manner.
How many candidates would you need in your talent pool overall in order to end up with just three people to interview? You might be surprised to learn that the answer is 90. That’s what the years of data we have collected tells us: 90 candidates are needed in a talent pool in order to end up with a shortlist of five, three of whom go on to interview.
That means you should be aiming for at least 90 people in your talent pool for any role. This might seem high, but this is actually a conservative number. We are confident in the quality of our data and we make sure it is always up-to-date.
The numbers don’t lie here, and 90 is the number of candidates you need to start with. Data-driven hiring enables you to tweak your requirements so you can increase the size of your talent pool and still end up with candidates that will be an excellent fit for your role.
For example, you might be searching for someone who lives within 10 miles of your business so they are able to commute. However, is this necessary? Is it possible to do the job remotely? What about candidates who might be willing to relocate? By tweaking the location in which you’re searching, you could potentially see a massive increase in the size of your talent pool.
When we use data analytics to supplement our recruitment, we focus on a few specific areas. These provide the biggest impact on things like the size of the talent pool and the quality of candidates, and enable us to fill roles quickly, cheaply and effectively for our clients.
This is the first thing we talk to our clients about, because it’s an area that often needs some adjustment. For every role, there are requirements that are ‘must-have’ and requirements that are simply nice to have. The problem is that businesses tend to overestimate how many must-have requirements they have, which can shrink their talent pool drastically.
This can be a legacy issue, with some job requirements being set years ago. Often, requirements are simply added onto these job specs in order to bring them in line with the company’s current needs, but without fully analysing the spec overall. This is often the cause of job requirements that ask for five years’ experience with a tool that has only been around for two; often the tool has been updated to match modern requirements, but the time needed with it has not.
This is where we come in with a data-driven approach. A quick search would show immediately that nobody exists with five years’ experience using that tool, and we would be able to quickly spot why. However, we can also spot requirements that many businesses think are completely necessary, but in actuality are not.
One example is years of experience. This is extremely common; for example, you might decide that a senior IT developer in your company must have eight years of experience in the industry. However, if you were recruiting in Poland you would have just cut out 60 percent of your talent pool.
Not only that, but there’s no guarantee that eight years’ experience will produce the best candidates. There might be a few young superstars in your talent pool who are relatively new to the industry but have the skills, passion and raw talent that will be much more useful to your company than someone who has kept their head down for eight years, doing a solid job but nothing extraordinary.
This is why we do a detailed analysis of the job market before we start any part of the recruitment process. It gives us the insight to avoid cutting down on the size of your talent pool for arbitrary reasons, and ensures you have the best chance of finding the right candidate.
Another common issue is when companies hire to replace somebody who is leaving them. The person being replaced might have been with the business for years, taken on extra roles and learned new skills. When looking at the job spec for their replacement, it is easy for the organisation to simply list all the skills and qualities of the person leaving.
The problem is that this might lead to you looking for a purple squirrel candidate. Maybe someone who can do the role of the person leaving is out there, but not at the salary you are offering. Maybe there simply isn’t anyone with the exact combination of skills, and you might need to consider hiring two people instead of one. None of these situations are ideal, but they are vastly preferable to making a substandard hire based on little or no data and wasting money and resources.
When we use data, our steps are simple; here’s how we analyse the talent pool for any given role:
- We know from our existing data that roughly 80 percent of any talent pool will not want to move roles, and of the ones that do, roughly 75 percent will not pass our detailed screening process. That means we need to look at an average of 90 candidates in order to end up with a shortlist of five candidates.
- We use our data tools to analyse the talent pool based on the requirements of the job and create a Talent Intelligence report. If we don’t end up with at least 90 suitable candidates, we know we will need to change some things.
- We make recommendations to the client on what needs to be changed. While salary is often a good parameter to move, it is not always possible for companies, so we will look for nice-to-have skills that we can adjust in order to increase the size of the talent pool.
- Once we have identified a talent pool that is large enough, we will be able to find enough suitable, interested candidates for our client to make an informed hiring decision, knowing they are getting the best of the current talent pool.
Here’s an example of how our data can help: a client might be looking to hire a finance manager based out of The Hague, for a salary of €45,000 p/a. However, when they put out job adverts they have very little interest. Why?
Well, if we look at our salary data for finance managers in the Netherlands, we can see that the client is offering close to the average for the country overall. However, salary expectations in The Hague are much higher, with the average being over €51,000 p/a. We might not be able to increase what they can offer, but we could adjust where we’re looking. If we were to recruit for remote workers based out of Haarlem or Amersfoort, the client would be offering well over the average salary. This means they would be likely to have their pick of talent, and potentially make a significant saving on the role.
Culture or Company Fit
How can you guarantee that a candidate will be a good fit with your company? You can’t, of course, but we can get fairly close with data. For example, imagine two Java developers with the same level of experience and the same degree from the same university. One has been working for their entire career in a large accountancy firm, while the other has spent their time in a software development startup.
They might be very similar on paper, but their personalities and expectations are likely to be extremely different. The first developer probably turns up to work in a suit, keeps strict nine-to-five hours, and is used to a formal environment. The latter might wear casual clothes to work, start at 11am and finish at 9pm, and be used to a much more relaxed culture.
The companies a candidate works for can tell you a lot about their personality and how likely they are to fit in with your company culture. This includes how long they stay at each business; someone who leaves a role quickly for one in a very different industry might not have been a good fit with the culture, for example.
We utilise data to solve this problem, and here’s how:
- We have built up lists of companies with similar cultures, using these when we search for candidates to match them up with our client’s business environment.
- We review the companies a candidate has worked with when we whittle the longlist down to a shortlist, building up a picture of their personality and how likely they are to fit in with our client.
- Whenever a candidate is an obvious fit, our automated processes immediately approach them.
- If a candidate is not completely the right fit, our automations take a different approach, sending them an invitation to apply for the role. As part of the application, they will have to justify why they are a good fit for the job, answering questions tailored to our concerns.
- We always run everything through one of our talented recruiters, who can make a final call on the suitability of any candidate. Sometimes they can spot candidates who will fit best with the client’s needs in a way that our AI can’t, no matter how advanced it gets.
As an example from our data, we can use IT developers in Belgium. As you can see, Brussels has the highest demand for developers by far. However, Brussels is in the bilingual area of Belgium. Most of the other cities are split between the Dutch and French-speaking areas of the country. If you were recruiting for a role in Brussels, this would have a big impact on the cultural fit of a candidate.
The different regions of Belgium might be in the same country, but they have different cultures. By analysing the data, we can ensure we understand the cultural makeup of the talent pool and get a better picture of which candidates would be a good fit.
Not all universities teach in the same way. Some are more theoretical, some are more practical and some differ depending on the course. This is what causes some companies to only hire from specific universities, often with extremely beneficial results.
For example, one of the strongest computer science courses in Ireland is known to be Trinity College’s. However, looking at site reliability engineers, very few of them comparatively come from this university, with the majority getting their degree from University College Dublin. In fact, nearly all of Google’s site reliability engineering team in Ireland came from a different university to Trinity College.
Why is this? One explanation is that Trinity College’s degree is a very theoretical course, while University College Dublin is very hands-on and focused on practical experience. This results in graduates with a very different set of skills, who are suited to different career paths. If Google limited its hiring to just Trinity College based on its reputation, it would have a very different workforce.
This is similar to the way we view culture fit. Our version of data-driven recruitment involves learning how the different universities teach, so we can categorise them in our AI-powered search engine. That means we can analyse how well their education is likely to fit each individual role, giving us a much more well-rounded picture of the overall talent pool.
How do the salaries your company offers compare to the wider job market? Do you think you’re offering more or less than the average for your industry? What about if you’re targeting candidates from a specific university, are you at the high end of the pay scale for them or the low end? This is a situation where data is needed to give clarity.
We are very used to using our data analytics to help with recruitment, and one of the main areas in which it comes into play is salaries. You might be used to recruiters telling you that you need to increase your salaries to attract new talent, but in our experience the data tends to tell a different story. In fact, in a lot of cases you might be able to decrease your salary offer.
For example, look at the average salaries for HR professionals in the UK. If your business is based in London, you would need to be offering an average of over £42,000 p/a. That’s to attract the average candidate; for the most elite talent, you might be looking at an annual salary of £70,000. However, is there any reason why an HR employee needs to be based in the same location as you? Could the job be done remotely?
Looking at the above graph, if you offered a salary of £42,000 p/a you would be well above the average wage in Wales or Northern Ireland. That means you would be able to attract much more elite talent at a lower cost.
Another example comes from Spain. If you were hiring an IT developer in Madrid, the average annual wage would be over €51,000 and the top of the pay scale would be €78,000. However, if you could afford to pay the average salary for someone in Madrid, you would be able to pay more than the top IT developer salaries in Barcelona, Valencia, Seville or Málaga, all of which are €46,000 p/a or less.
In our experience, you don’t need to pay more to access talent; you just need to know where to look. There are always candidates available who meet your requirements and would love to work for you, and many of them are cheaper to hire than you would expect. It’s just a matter of understanding where to find them, and we can provide you with the data that will do just that.
It is a widely accepted fact that diverse workforces perform better than non-diverse ones. Research has shown that when companies embrace diversity, they are 25 percent more likely to perform better financially than their industry average, 1.7 times more likely to be innovation leaders, and the more diverse their teams are, the better and faster they will solve problems.
In fact, not diversifying your workforce can actually harm your recruitment. The younger generations entering the workplace are increasingly concerned about this issue, and one in three people would not apply for a job at a company with a lack of diversity among its employees.
Companies are working hard to achieve more diverse teams, but often recruiters push back on this. For example, many industries are male-dominated, so recruiters will sometimes say that looking for female candidates is pointless and your diversity quotas are simply impossible to achieve. However, the data once again points in another direction, which is why it is so important not to make recruitment decisions based on your instincts or assumptions.
As an example, let’s look at Java developers in the Netherlands. There is definitely a gender imbalance here, with 80 percent of the talent pool being male. However, there are around 10,000 Java developers in the Netherlands, which means there are 2,000 female candidates to choose from; hardly impossible to find someone who is the right fit.
When we look a little closer, you will start to see some trends in the data around these developers:
- 92 percent have more than eight years of experience, making this an extremely experienced talent pool.
- Two areas in the Netherlands have more female Java developers than there are jobs: The Hague and Amersfoort.
- Many of the female Java developers in the Netherlands are originally from eastern European countries.
- Most female Java developers in the Netherlands went to Vrije Universiteit Amsterdam.
Already with these four facts, we have uncovered ways of attracting this workforce. We could look to offer a working model that will be attractive to female Java developers in their 30s, given that most of the workforce is this age or older given the experience they have. We could recruit specifically in The Hague or Amersfoort, and we could look to attract specifically eastern European candidates and Vrije Universiteit Amsterdam graduates.
If we look deeper into the data, there is even more we could learn. For example:
- Which other universities and courses produce female Java developers?
- What job titles do female Java developers tend to have?
- What industries and companies do Female Java developers work for?
- What salaries do female Java developers have on average?
All of this data can be used to improve your recruitment. For example, knowing the job titles of female Java developers will ensure you’re able to use similar titles for your roles, increasing the chance you will show up in their searches. Diverse hiring is definitely possible, and data makes it so much easier.
Which companies are you competing with for talent? You might have a good idea of the answer to this, but with data-driven talent acquisition we can be certain. Understanding this is important, because it enables you to make intelligent decisions about where to look for talent, what to offer and how to attract the most elite candidates around.
For example, let’s say you’re recruiting legal regulatory compliance staff in Belgium. The top employers for this type of role in Belgium by far are the European Commission and the European Parliament. You might think the two organisations are too big to compete with; however, they are both based in Brussels. This is vital information you can use to secure the most relevant candidates for your business.
If you were to search for candidates in Antwerp, you would be less likely to be competing with the European Commission and European Parliament, giving you a much better chance of securing the talent you need. Even if you are based in Brussels, the two cities are less than an hour away on the train; definitely commutable.
Again, the data can answer even more questions. For example, what roles do people typically leave in order to fill positions like the one for which you’re hiring? For example, maybe junior IT developers typically look to move to a more senior position after four years. If you’re looking to fill that senior position, you could target junior developers with around four years’ experience and potentially fill your position before the candidates even look at one of your competitors.
How many candidates are there for each job in your industry? In some situations there might be hundreds of people for each job on the market, in which case you will be extremely likely to find elite talent that fits in with your company. However, you might find there isn’t much choice, with few candidates per job, leaving you settling for second-best talent.
Once again, data-driven recruitment is the answer. Looking at the IT QA sector in France, for example, we can look at the data and find out that the number of people per job is relatively low. However, demand is falling and the number of candidates per role is increasing. That means it might be better if you can wait a few months before hiring, by which time there should be more candidates to choose from.
Once again, location also can come into it. While across most of France there are not many available IT QA candidates per job, in Marseille there is an enormous number of candidates compared to the number of roles to be filled. Recruiting in Marseille could be the answer, especially if the job can be done remotely.
We’ve talked a lot about location already, but data-driven talent acquisition is often all about finding where candidates are based and hiring accordingly. The level of competition, average salary and number of candidates can all vary wildly between cities, giving you a lot more options if you’re prepared to offer flexible, remote or hybrid work models.
A good option for any role is to look at a map showing where talent is based. This will be a good indicator of whether recruiting for on-site roles is feasible or if you have to cast your net wider. It might be that there is simply not much talent in your current location, giving you a small talent pool and a poor chance of hiring the right candidate.
If you were recruiting for gaming developers in Poland, for example, you might struggle if you were based in Koszalin. There are very few eligible candidates in that location, meaning a tiny talent pool. However, if you were to recruit from Poznań you would find there are many more candidates and fewer jobs for them to take.
This doesn’t mean your entire business needs to relocate to Poznań; however, it might mean looking into remote working so you can access talent in the areas in which it is abundant. This gives you the best chance of hiring the best candidates for your role.
If you knew that talented employees were leaving certain companies, would you want to know? The chances are that your answer is yes. When elite individuals leave their positions and enter the job market, you are better able to recruit them to your organisation. Being able to predict when this happens would give you a huge advantage.
Data-driven recruitment allows this. We can spot companies that are losing a lot of staff, for example, which possibly indicates low morale or staff satisfaction. That means employees of that organisation might be more open to an alternative job offer, while also enabling you to attract them with job adverts that emphasise your positive company culture.
For example, right now in the Defence and Space sector in France there are a number of companies with surprisingly high attrition rates. Not all of these are in the same situation; some are shrinking, while others are still growing but see a large percentage of their workforce leave each year. In the latter case, this could indicate to us that there is an issue with their company culture, or that their hiring policy is not delivering the right candidates who will fit with their culture and stay long-term.
Another metric we can look at is attrition rate by region. At various times, different parts of the world have higher attrition rates than others, as can be seen in the graph below. For example, in 2016 we can see that someone in a job in the Asia Pacific region was much less likely to stay in it than someone in Europe, the Middle East and Africa.
This is true of different countries as well. Recent McKinsey data shows that Polish workers are much more likely to be thinking about leaving their jobs than those in Austria, for example. We can use this data, and the information we collect ourselves, to assess the likely length of time candidates will remain in their positions. This is known as “tenure”.
In the countries in which we work, the average tenure is 1.4 years. However, this ranges by country. In Sweden it is 1.7 years, while France and Spain have an average tenure of just one year. Other countries include The Netherlands and Poland at 1.6 years, the UK and Belgium at 1.5, and Ireland at 1.4.
By combining all of this information, we can build up a picture of how likely each candidate is likely to stay in their role. Knowing that an elite candidate is approaching the end of their expected tenure puts us at a big advantage and potentially allows us to reach them before they start actively looking for new roles, beating out the competition.
Size of potential Talent Pool in a given location
Using data you can understand both how many candidates of a given talent level there are, and what the demand is for this talent in that location.
You might be surprised by how many or how few candidates there are for a given skills set in a location. At least having the data will allow you to be better informed and make better decisions. This can be used to help decide where to locate certain roles and give yourself the best chance of filling the role.
For example, we can look at the Renewable and Environment sector across Europe. In the eight countries below we have 421,000 professionals, however they are not distributed equally:
- France has 117,000
- United Kingdom 102,000
- Spain 84,000
- Netherlands 41,000
- Poland 26,000
- Sweden 18,000
- Belgium 14,000
- Ireland 8,341
This tells us one side of the story, but we also need to consider the demand. If you were hiring in this sector, you might think of going to the UK over Ireland as it has more candidates to choose from. However, the UK has 1,637 job ads for this sector, which means there is an average of 62 candidates per job. Ireland has an average of 65 candidates per job, giving you slightly more choice despite the smaller talent pool.
It’s also worth considering remote working. If you allow employees to work from home, there’s no reason they need to be based in the same country as you, which means you can choose from a much larger talent pool. We frequently employ and payroll candidates from different countries to the company hiring them, making sure everything is fully compliant from a tax and HR point of view.
One big advantage of doing this is that salary expectations are likely to be different for a role across different countries. For example, here are the average salaries for a Data Analyst role across Europe:
|Country||Gross Annual Salary||Total Cost in Local Currency||Gross Annual Salary in EUR||Total Cost in EUR|
|Ireland||EUR 50,030.00||EUR 50,030.00||€ 50,030.00||€ 55,558.28|
|United Kingdom||£ 61,521.00||£ 70,908.84||€ 69,159.45||€ 79,712.13|
|Poland||PLN 111,360.00||PLN 133,052.88||€ 23,750.30||€ 28,376.90|
|Netherlands||EUR 55,216.00||EUR 55,216.00||€ 55,216.00||€ 66,270.28|
|Sweden||SEK 805,104.00||SEK 1,063,887.72||€ 71,727.92||€ 94,783.28|
|Spain||EUR 54,511.00||EUR 54,511.00||€ 54,511.00||€ 72,156.88|
|Belgium||EUR 84,060.00||EUR 84,060.00||€ 84,060.00||€ 121,464.96|
|France||EUR 72,289.00||EUR 72,289.00||€ 72,289.00||€ 110,181.57|
Another consideration, if hiring remote workers from outside your country does not work for you, is to look to attract talent from a country where candidates are already happy to move to your location. If you can see a trend where talent from Poland is moving to Sweden, you can be more confident in targeting Polish candidates to come live in Sweden knowing that other Polish candidates see Sweden as an attractive place to work in. There will also be a growing Polish community in Sweden, making it much easier for candidates to settle there and reducing any possible churn.
Last year we saw 699 professionals from the UK move to Sweden, along with 459 from Spain, 303 from the Netherlands and 242 from Poland. If we look at this data in more detail we can see which universities and courses these candidates attended, for example, giving us a better idea of the type of candidate who looks on Sweden as an attractive place to live and work.
All of this data can be used to help you find willing candidates for hard-to-fill roles. A simple liaison with the careers office in one or two universities in Poland, for example, could lead to a steady flow of top-class polish candidates who want to move to your country and who already have a support network in place there.
What should you call your role? This might seem like a simple question, but it actually has a huge impact on how easy it is to fill the position. If you refer to your role in a way that is uncommon for your industry, you might find candidates never find or even notice your job advert.
For example, what would you call someone who works in IT as a developer? You might think of calling them a programmer or an IT consultant, for example. However, if you’re hiring in Sweden the most common job title is software developer or software engineer, and those titles are the ones candidates are likely to be searching for when they look for jobs. You may internally call them a programmer, or call employees in your country programmers, but in Sweden they are software developers.
This also applies to the keywords used in your job advert. You can’t be sure candidates will search for specific job titles, as many people look for keywords related to their preferred role instead. Sticking with the same example, the most common keywords for IT developers in Sweden after “software” are “design” and “designer”. This is despite the fact those words don’t appear in any of the most common job titles.
Without data, companies would have to guess what terms to include in their adverts. Without a significant amount of trial and error, this would be an extremely ineffective tactic. Instead, data can point us to the most common keywords used and searched for, letting us know what terms we should include in our job adverts to stand the best chance of them ending up in front of a candidate who is looking for our job.
If you’d like to take advantage of our recruitment powered by data analysis, we’d be happy to help. Our methodology is based around using data – in combination with machine learning and search engine specialists – to fill roles quickly, cheaply and effectively.
We would start by taking your job spec and organising a discovery call between you and one of our Talent Intelligence Team. On this call, we will work to fully understand the requirements of your role, working with you to identify the must-have and nice-to-have skills so we can tailor our search as accurately as possible.
Our Talent Intelligence Team will then do a full analysis of the data over a 48-hour period, searching for insights like the ones we have already provided in this guide. This will be summed up in a Talent Intelligence Report, which will summarise our findings and provide you with key recommendations on how to maximise your chances of filling this role. We will present this report to you in a meeting with you and your team so we can fully discuss our recommendations and answer any questions you have.
As an option, Allen Consulting is also able to go one or more steps further with these recommendations. This service can take our recommendations and use them to produce a longlist of candidates, complete with all the information you will need to reduce it down to a shortlist.
Alternatively, Allen Consulting can create that shortlist for you, using our automation technology to contact candidates and identify if they are interested in your role, what their current salary, visa and notice status is, and more. You’ll then have access to an expertly curated shortlist of talent ready to be interviewed for your role.
When we say our methodology is the future of recruitment, it’s because we believe all recruiters will eventually come to use these tactics. Using data has so many provable advantages that it will soon be impossible for recruiters to succeed without it. Here’s how we find the candidates that fit our clients’ requirements.
Our data-driven recruitment process involves searching 45 different international digital sources, from job boards to industry platforms to social media. This lets us create a detailed profile of all possible candidates – completely compliant with GDPR – and enter it into our CRM software.
From there, our AI matching software can take over. Its job is to refine the search by assessing how suitable each candidate is for the role, based on the requirements and needs you have already communicated to us. This lets us create a refined longlist for the position.
From here, our intelligent automations take over. They contact each candidate up to ten times using a number of different channels, including email, text and social media. We market the company, the role and the offer to each candidate and score them based on how they respond, giving us an even better idea of how well they will fit in with your organisation. In every interaction, we invite them to book into one of our recruiters’ diaries, which is an extremely important part of the process.
In over 80 percent of cases, they either book into our diary or share some important information with us on their situation. This could be what their ideal role looks like, what would encourage them to move or something else that we can use to improve our recruiting efforts. Everything is recorded in our databases so we can easily compare candidates. If the role is not for them, we can use this information to reach out for future positions.
By utilising all of this data about each of our candidates, we can ensure that our communications with them are as relevant as possible. This not only helps recruit them to the role, but ensures they stay an option for jobs that may open up in the future. This approach ensures that our unsubscribes are some of the lowest in the industry.
You might be wondering why we highlighted the importance of booking in with our recruiters. For a number of reasons, this is a crucial step in our data-driven recruitment process. It’s one of the reasons why our methodology works so well, and why we consider ourselves the future of recruitment. Here are some of the reasons it is so important:
- Commitment. A candidate booking time in a recruiter’s diary is a significant sign they believe they can do the role and would like to at least consider taking it. It means they are willing to take the time needed for us to perform a detailed screening, as well as changing how we communicate to reflect all this.
- Detailed screening. When traditional recruiters call candidates, they typically only have time for a two-minute conversation before having to rush off to the next candidate. Because our automations save us so much time, we have the ability to have meaningful, empathetic discussions with each candidate. This lets us much better understand their needs, quality and suitability for the role.
- Non-sales environment. Cold calling candidates means recruiters have to sell. They need to convince the candidate the job is right for them, the company is good to work for and the recruiter has their best interests at heart. Given the time needed for cold calling, this means many recruiters spend more time selling than they do understanding whether or not the candidate is right for the job. Our automated system ensures the candidate is already sold on the role, the company and us by the time we talk to them, so we can focus on their suitability for the position.
The result of all this is a completely different dynamic to traditional recruitment. We know our candidates want the roles, so we can focus entirely on how well they will fit the job rather than having to convince them to apply for it. The results of our methodology include our ratio of candidate submissions to interviews being much higher than the industry average, and our offer rejections sitting at an industry low.
Recruiters often talk about their deep knowledge of their specific sector or location. This can be extremely useful, but it limits them. Wouldn’t it be better to have a system that doesn’t require deep knowledge about an industry in order to work?
That’s why our approach to data-driven recruitment works so well. We can use the same approach for an HR job in Glasgow and a site reliability engineer role in Warsaw, and get the same excellent results for both. We don’t need intimate knowledge of your industry; we get that from our initial call with you to fully understand your requirements for the role.
A client in a new location and industry for us recently said we were “number one for volume of CVs and number one for quality of CVs… I don’t know what the so-called ‘local industry experts’ were doing”. We were able to provide that service thanks to our industry and location-agnostic approach.
Data-driven recruitment doesn’t rely on cold calling and doesn’t give up on candidates because they don’t return a call or respond to a mass email. Our automated systems provide us with a way of consistently contacting candidates and getting them interested in the role, ensuring we already have them hooked by the time we talk to them.
Letting candidates make the first move even has a beneficial psychological effect, letting them feel in control of the process and ensuring they feel positively about the job and the company. Our methodology is the future of recruitment, and we’re proud to be pioneers.
Posted in: Blog / Client