A comprehensive guide to replacing manual CV screening in your organization
Any time you’re looking for a new product to solve your problem, we realize that the comparison process can be a little confusing, especially if you’re not an expert in the field. There’s a lot of buzzwords floating around and it’s hard to separate the wheat from the chaff.
This is a definitive guide to using Alooba Assess skills assessments vs manual CV screening. This guide provides an in-depth analysis of the pitfalls of manual CV screening and the upsides of progressing your hiring to objective skills-based hiring with Alooba Assess.
As you will see in this article, manual CV screening introduces five fundamental problems for organizations when hiring:
Instead of manually screening hundreds of applications, would you consider progressing to objective skills-based hiring? With Alooba Assess, you’re giving all your candidates a fair chance by committing to structural change in your process, focusing your hiring on answering the fundamental question: 'Who is the best person for the job?'.
In this article, we delve into all the details of manual CV screening, answer your most frequently asked questions and hopefully cover off some areas you might not have thought much about. Got questions? Feel free to contact us here.
Looking for a blow-by-blow of Alooba Assess’s functionality? Check out a full rundown of the features and capabilities of Alooba’s various products here.
Looking to assess your candidates for data roles? Get started now with Alooba Assess.
Alooba has several products. Alooba Assess is used by organizations to assess the skills of data candidates and is the focus of this comparison article.
Alooba Assess is unique, being the only skills assessment platform tailored specifically to data skills - the skills needed for data analyst, data science & data engineering roles. That is the fundamental difference between Alooba Assess and all other products that you might be evaluating.
Fun fact, the first CV was invented by Leonardo da Vinci way back in 1482
A CV (curriculum vitae) is a summary of the candidate’s work experience, education and other information that is written by the candidate themselves. These are normally 1-4 pages and presented in a Word doc or PDF. It’s intended to be basic evidence from the candidate that they’re suitable for the position and deserve an interview.
Almost all organizations, for all roles, will ask for an up-to-date copy of the candidate’s CV as part of the initial job application.
Note, in some countries, a resume (or résumé) is considered to be a mini-CV, but for the purposes of this discussion, we’ll treat CVs and resumes as the same thing.
Screening is one of the stages of the hiring process. After an organization has defined the job requirements and started sourcing candidates, the next step is then to decide which of these candidates should go to the next stage of the hiring process.
Depending on the role, organization and labor market conditions, there could be 10s, 100s, or even 1000s of candidates for one position, so this is a very important step. In traditional hiring, organizations normally screen candidates based on their job application, with the main component being the CV. The idea is basically to look at the applications and determine which seem to be the most suitable, by comparing them to the role requirements.
The job application itself might include some structured questions that are also used as filters. For example, organizations might ask the candidate if they have working rights in the location of the role. Organizations might then choose to first filter out these candidates prior to reading the CVs, for example.
Manual CV screening means a human reading a CV themselves to try and determine if they want to bring the candidate to the next stage, based on how suitable they think the candidate is.
In most cases, this is a process that lasts anywhere from a few seconds to a couple of minutes per CV, and will involve the person quickly scanning the CV, looking out for particular keywords and experiences.
One dirty little recruitment secret is that not all CVs even get read! With 100s of applicants it’s a fundamentally unscalable process, so this should not be a surprise, even if it is a bit depressing.
For example, for a product analyst role, you might be looking for some evidence that the candidate uses SQL, either Python or R, that they’ve done some dashboarding in some tool (e.g. Looker, Tableau etc.) and that they’ve been involved in experimentation and user behaviour analytics. You might also be on the lookout for the candidate having a certain amount of experience in a role, that they’ve worked in certain types of organizations or have a particular education level.
If this sounds a little wishy-washy, that’s because it is. Manual CV screening is not really scientific, to say the least. The outcome varies a lot depending on the person that does it and how they felt at the time. Check out our experiment results here to show how truly random the manual CV screening process is. It’s almost like flipping a coin!
Ultimately, only one candidate will get the position, and not every candidate can get interviewed. Because of this, the initial set of applications has to be cut down to a manageable set of candidates that can be taken to the next stage of the hiring process, which for most organizations is some type of interview. Manual CV screening is trying to produce this shortlist of candidates.
Automated CV screening basically does a similar thing to manual CV screening, but automatically. Automated CV tools are a fairly blunt force instrument. They’ll rank CVs based on the keywords you are looking for. For example, they’ll look for ‘Python’, ‘SQL, ‘databases’ etc. and rank the candidates who have lots of those kinds of words in their CV. This is easily manipulated by candidates using ‘keyword stuffing’ - filling their CV with words relating to the job description so their CV ranks better. Automated CV screening doesn’t make the process any more accurate, because it’s still based on a fundamentally flawed dataset - the CV.
Contrary to common belief, automated CV screening is rarely used in practice.
Note, Alooba Assess is not an automated CV screening product. The easiest way to think about it is that automated CV screening is like a faster horse, while Alooba Assess is like a car. A very different solution to the screening problem of ‘Which of these candidates should we bring to the next stage of the hiring process?’, one that is based on data and not gut feel.
Objective skills-based hiring is a more modern way of hiring, based on making data-informed decisions. Traditional recruitment has a lot of gut feel and intuition based decisions at every stage of the hiring process, from the initial screening, through to interviews and final offer decision. Alooba is basically the opposite of this approach.
Instead, with objective skills-based hiring you directly measure the candidate’s ability to do their job. For example, your data engineering role might require someone who understands how to build data models in relational databases, how to create and maintain data pipelines and who can write SQL & Python to accomplish these. You can assess these skills directly, telling you whether or not the candidate can do the job.
Manual CV screening introduces five fundamental problems for organizations when hiring:
Let’s unpack each of these one by one.
Increased hiring costs
As you’ll see from our Manual CV Screening Cost Calculator, CV screening is very expensive for organizations. On average you expect it will cost 5-10K USD per hire. This shouldn’t be a surprise really - hiring a recruiter for a similar role would cost 20-25K USD.
The costs of manual CV screening come in two forms:
Reduced hiring efficiency
It helps to first think, what would a perfect hiring process look like? A perfectly accurate hiring process would choose the ‘best’ candidate, as quickly, easily and cheaply as possible, and leave all other candidates no worse off (ideally better off) than before they applied to the role.
Selecting CVs is like making a prediction. You’re basically saying, ‘I think this application looks suitable. I’ll bring them in to the next stage of the hiring process’. Each person you bring into the next stage of the hiring process, costs on average $1-2K USD. So making accurate predictions is extremely important. Without it, you’ll be riddling yourself with burdensome hiring costs.
A 100% accurate screening process would basically mean:
So you have the dual goals of preventing your hiring process being clogged up with dreamers who cost you a lot in failed interviews, while at the same time not accidentally filtering out hidden gems.
At Alooba, a dreamer is a candidate who stands out on paper as having strong skills, but they have low self awareness. A hidden gem is the opposite of a dreamer - these candidates don’t stand out on paper, but are actually great. Unfortunately, organizations that only use CV screening miss out on these hidden gems.
Fundamentally, CVs are just a low quality dataset to make screening decisions on.
CVs are autobiographical
CVs are someone’s own interpretation and summary of themselves, their strengths and their work experience. Quite obviously, this is not exactly going to be a neutral account of reality. We aren’t the best judges of ourselves, and more than 80% of candidates on Alooba overestimate their skills.
We should be acutely aware of the Dunning-Kruger effect. The Dunning-Kruger Effect means that actually there could be a negative relationship between how relevant someone seems on a CV, vs how competent they actually are.
And, let’s be honest, apart from perhaps lacking a little self awareness, we’re also prone to exaggerate or straight up lie. In fact, 60% of candidates lie about skills they have, 50% lie about having a longer tenure than what they did and 40% make up an inflated title!
CVs are unstructured
Each CV is uniquely unstructured. There is no universal format. This makes it impossible to easily compare one CV to another. This creates an apples-to-oranges comparison.
For example, one candidate submits a 1 page CV with just the last 4 years of their experience, very briefly summarised. Another presents a 4 page CV with all their work history, and a lot more details about what they’ve done. Which candidate is better for the role?
CVs contain a lot of noise
While CVs do contain some useful - albeit quite biased - information about a candidate’s work experience and history, they also contain a lot of noise. Noise is basically information that is at best, not helpful to your hiring decision, and at worst - as we see below - unhelpful to your hiring decision.
For example, these are all examples of noise that you get from CVs:
Beyond these obvious sources of descrimination, there are a myriad of other things like the hobbies section, that are entirely irrelevant.
The end of result of all this noise is that you will make a worse hiring decision than if you were not exposed to it.
CVs omit crucial information
CVs also don’t tell you a lot of really important information, that you’d normally try to coax out later on in the hiring process. For example, there’s no (validated) quantification of the candidate’s skills, their personality type, intelligence, how they communicate with others, experience in dealing with certain problems etc.
You’re only human
In addition to these issues, whomever is doing the screening is only human. We come with our own biases and shortcomings - we’re not robots.
It’s very subjective
Something about it generally being unclear what good looks like any way.
There’s a whole range of biases that you’re subject to when you manually screen CVs. They include these biases:
We unpack these below in more detail.
Increased time to hire
CV screening is a manual process, and it’s this fundamental fact that makes it slow. When applications come in on a Friday night at 10pm, nobody is there to screen the CV, and nor should they be! People doing the screening take weekends off, don’t work 24/7, have sick days and even when they’re working, have a million other priorities. The typical talent acquisition worker, in particular, is probably one of the most overworked and underappreciated people in large companies. They’ll be scheduling interviews, haranging hiring managers for interview feedback, keeping candidates warm and spacing out new roles to hire for. It’s an unforgiving role at times, and reviewing CVs often falls to the bottom of the daily ‘to-do’ list.
For fast growing organizations, like hyper growth tech companies, establishing scalable processes across the organization will be essential to prevent the wheels falling off the bus.
The hiring process is no different. You need to work hard to avoid bottlenecks that break parts of the process. CV screening is fundamentally unscalable because it’s manual - the more applicants you get, the more people you need to read the CVs.
Increased bias & descrimination
If you’re currently using manual CV screening in your organization, it’s important that you understand the biases that are in your current hiring process.
The aggregate impact of all these biases is hard to measure, but it’s clear that your shortlisting decision is highly compromised. This reduces how accurate your decisions are (even based on the limited dataset available), which leads to you screening out some excellent candidates, and screening in some candidates who aren’t actually suitable.
The knock-on effect is that you will have a higher failed interview rate. In other words, you’ll advance too many weak candidates to the interview stage, wasting precious time, slowing down the hiring process, and adding to the hiring costs.
Let’s see a breakdown of some of the biases that can exist with manual CV screening.
The halo effect is basically when you fixate on one particular positive/good aspect about someone, which then clouds your judgement about everything else. For example, you might notice from the candidate’s education section that they have a 1st class honours degree, or that they topped their university entrance exams. Or you might notice that they have worked at a top tier company known for stringent recruitment like Google or Facebook.
This then means you might not see negative things from the candidate’s CV that would otherwise be red flags. You ignore these things because the halo effect has given you blinkers; you are seeing the candidate through rose-tinted glasses.
The horn effect is pretty much like the opposite of the halo effect. Instead of initially zeroing in on a positive aspect about a candidate, instead you focus on a negative/bad aspect. For example, you might notice that the candidate had a large career gap recently, that they went to a lower quality university or that they’d only been in their current role for 3 months.
With this initial negative impression, the rest of your evaluation is basically tainted. You will be unable to clearly see all the good things about a candidate’s profile, because you have blinkers on from the horn effect.
Similarity attraction bias
The similarity attraction bias is basically looking to hire people who are similar to you and you feel comfortable with. For example, you might notice from the hobbies section that the candidate supports your favourite sports team, or in the education section you might see they are an alumni of your small town university too. You immediately latch on to this similarity and this, again, clouds the rest of your judgement about them.
You might feel that this is a little innocuous, but it’s a very slippery slope from ‘I like them because they went to my university’ to ‘I like them because they look similar to me’. You also need to think about the other candidates who’ve applied and haven’t been so fortunate - you’ve basically relegated them in your mind.
By the way, this is why you have to be very careful with ‘cultural fit’ interviews where you just look for someone like you. This is a great example of the similarity attraction bias in play.
The illusory correlation basically means that you think there is a connection between two things, when there isn’t - i.e. the correlation (relationship) is illusory (doesn’t exist). This is super common in CV screening, with people often having weird personal expectations about what should or should not appear on a CV, even when this has no connection to how suitable the candidate might be for the role.
Here are some common reasons that people give for rejecting CVs. It’s really important to realize that there is no evidence that candidates who do these things are less suitable to the role:
Every hiring manager and recruiter seem to have their pet hates and exclude candidates for the most bizarre reasons. What pet hates do you have that you think might get in the way of selecting the best candidate?
The beauty bias is probably quite easy to understand and hopefully drives home how real these biases are. The beauty bias means that we assume beautiful people are also more competent and more suitable candidates. Unless you are recruiting for a model, for 99.9% of roles the attractiveness of the candidate is fundamentally irrelevant.
In some geographies, it’s quite common to include a photo on the CV, and so this is how the beauty bias plays out in the manual CV screening stage. Having seen the candidate’s attractiveness, the rest of your screening decision is impaired.
This is really the fundamental issue with traditional recruitment. Every decision, from sourcing, screening, interviewing and hiring is driven by intuition and gut feel, not actual data. This comes into play in CV screening when you’ll make snap decisions to reject or pass a candidate without any real evidence or justification - you shortlist someone because you ‘just feel’ they’ll be a good candidate.
Unfortunately, this is clearly unfair and not auditable. There was probably some underlying reason why you felt that way about the candidate, and if you reflected honestly and dug into it, it was probably driven by one of these other known biases.
Confirmation bias is basically when you make a decision about something, and then ignore all future evidence that doesn’t support the decision you have already made. You’d feel more comfortable sticking with your initial decision, even if it’s wrong, because it’s too difficult to admit you were wrong in the face of the new evidence.
This is really common in the hiring process and especially in manual CV screening. For example, you might open up the CV and immediately notice a spelling error. You happen to be a stickler for such things, you immediately think ‘Oh well this candidate doesn’t have a good attention to detail. They can’t be a good fit’. Even if you then go on to read that they’ve had a superb career, have all the relevant experience you’re after and essentially ‘tick all the boxes’, you’ll ignore that. With confirmation bias, you’ve essentially already made your mind up, so any evidence that doesn’t fit with this initial decision is either ignored or twisted to support it.
Increased opaqueness & lack of auditability
The world is changing, and recruitment (should) be changing with it. Candidates’ expectations about how they’ll be treated in hiring have increased to an all time high. Their relative power in the hiring process has also increased.
Unfortunately with CV screening, there is absolutely no audit trail for why a candidate has been rejected. Under freedom of information acts, employment acts and descrimonation acts across many countries, candidates can inquire and in somes litigate against companies who rejected the candidate arbitrarily.
How legal is your current hiring process?
The single biggest complaint of candidates in hiring is crap feedback or no feedback at all. Don’t be a ghost!
The candidate gets absolutely no feedback and doesn’t know what to do to improve next time. Did they lack experience? Did they not quantify their accomplishments? Did they have a name that was too hard to pronounce? Did anybody even bother to read their CV? How will the candidate ever know if all they get is generic ‘sorry, not sorry’.
Feedback is really important for all candidates, and there’s now an expectation that they will receive it and not be ghosted.
Unfortunately, manual CV screening and the way it’s done, doesn’t allow organizations to provide any meaningful feedback, even if they wanted to.
Yes, it is possible to use both methods in combination. We see some organizations, for example, use what we call a ‘triaging method’. So they will continue to screen CVs, and expedite candidates that on paper seem perfect. Their rationale is not wanting to miss out on a hot candidate that every other competitor will be going after too. They will expedite the 1%ers through a ‘fast-tracked’ hiring process, with the Alooba Assess test being a step after they’ve engaged them.
Meanwhile, the other 99% of candidates that have applied will go through Alooba Assess as the first step.
There are some benefits to this approach, but it has all the downsides of manual CV screening discussed in this article.
Doing the status quo and following the ‘we’ve always done it that way’ mentality is the easiest thing in the world to do. In fact, this is called the ‘default bias’. People simply repeat their usual behaviour because it takes real cognitive load to change.
Legacy HR tech has really let organizations down, as they have fundamentally not solved the screening problem. Organizations have been left with no other option other than the generic job ad and application form with the CV attached. That is, until objective skills-based hiring came along.
Who actually does manual CV screening varies from organization to organization. In larger organizations, normally the talent acquisition team are the initial gatekeepers, and they’ll do the first pass of the CVs. Having prepared a shortlist, they’ll then present this to the hiring manager for their review. The hiring manager will often then narrow the list down further, to the set of candidates that they actually agree to interview.
Some hiring managers in larger organizations do also report to us that they don’t necessarily trust that the best candidates are being selected, so might also take over the CV screening themselves. They realize it’s hard for someone in talent to spot what exactly they’re looking for, especially for roles in data.
In smaller organizations, the hiring manager may be responsible for the end-to-end hiring process themselves, and so do all the screening too.
If you use external recruiters, the recruiters will be responsible for establishing the shortlist and presenting it directly to the hiring manager.
One of the fundamental issues with CVs is that they are written by people about themselves. As we should all realize by now, we aren’t the best judges of ourselves. We all have a blindspot - or really several blindspots - when it comes to ourselves.
In fact, we measure ‘Self Awareness’ directly on Alooba. Before a candidate starts an assessment, they rate themselves on a scale of 1-10 for each skill that they are about to be assessed in. We then compare this self rating to their actual performance to determine their self awareness.
Basically, what we’ve found from more than 50 000 candidates is that people are lacking in self-awareness, with the average person overestimating their own skills. This has an important finding for manual CV screening. Candidates who claim to have ‘advanced SQL’ skills or ‘expert level Python’ skills are probably delusional. There is no validation to back up that claim, and no quantification. What does ‘advanced’ mean anyway? Further, the Dunning-Kruger Effect means that actually there could be a negative relationship between how relevant someone seems on a CV, vs how competent they actually are.
This lack of self awareness that we all have makes CVs a poor quality dataset to make screening decisions from.
‘The results have been remarkable’, Piers Stobbs, CDO at Cazoo
Yes, we have several case studies. For example, feel free to hear from Piers Stobbs, Chief Data Officer of Cazoo.
Cazoo are a hypergrowth technology company based in England. They’ve basically taken the traditional used car yard, and moved it online. Despite only being a few years old, Cazoo has already listed on the NYSE at a valuation of around 7B USD. Behind their astronomically fast growth has of course been data, and their data team.
Cazoo decided to take a data-informed approach to hiring data roles. Rather than relying on the gut feel and intuition-based approaches of traditional hiring, they opted for an approach that is a lot more objective, simple and scalable.
Stobbs says ‘scaling our organization has been one of our key challenges. I was very keen to identify a way for us to identify talent from a broad range of different backgrounds and improve the process.’
It’s quite common that organizations come to Alooba with multiple goals, such as streamlining their hiring process and promoting diversity.
Stobbs goes on to say ‘Rather than having to manually scan through vast numbers of CVs…so we started’
‘It’s gotten much easier to screen 100s of candidates because you just get everyone to take the test.’
The diversity of our pool has definitely improved so we just have many more candidates from just different backgrounds which I am a huge believer in. It makes the team much better, it makes our output much better and gives us more voices in terms of building the best product and service that we can.”
You can see the full testimonial from Cazoo here.
Like any stage of the hiring process, not everyone will complete it. As we’ve seen, manual CV screening typically removes 95-99% of all the applicants. This set includes some great candidates. These are the true own-goals/unforced errors - you had the candidate under your noses but couldn’t identify them because of your slow or biased processes.
The attempt rate of the assessments varies based on a number of factors. Depending on how strong you perform on these factors, you’ll find the attempt rate is 60-95%.
In addition to those factors, you’ll want to ensure that the assessment you’ve set up aligns well with the job description and that it’s not too long. We’ll help you with both of these. Nothing will annoy a candidate more than a job description that talks about XYZ and then being asked to do an assessment that tests ABC. As long as they’re well-aligned, the candidate feedback is generally very positive.
The attempt rate is driven firstly by these factors relating to your organization and the role:
Strength of your employer brand
How much do candidates really want to work for you? Is your organization known as a great place to work? Or is it more a place that people ‘have to’ work? The stronger the brand, the higher the attempt rate.
Your sourcing strategy
How are you getting candidates? If candidates only apply through your careers page, it’s likely they are already engaged with your organization specifically, and so they’re more likely to be an engaged candidate that wants to go through and complete the various stages of the hiring process.
If you source through LinkedIn and other jobs boards, be aware that they’re open to anyone, and you can apply with 1 click. These low barriers to applying mean that candidates often employ a spray-and-pray approach to applications, and many aren’t actually that interested in your role.
This is where providing them with a skills assessment actually helps you, because those who don’t complete mustn’t actually be that interested or realize that they aren’t that suitable, and so you can avoid chasing those who don’t want to be chased. This is a straightforward two-way qualification for both parties.
How attractive is your role?
There’s many things that go into this, including, obviously the remuneration and other working conditions and how you’ve pitched the role. Candidates in analytics that have been around a while can smell BS on a job description from a mile away. They know when a job description for a ‘Data Scientist’ is actually overselling a basic reporting role. Don’t fall into this trap. Keep an eye on your competitors and make sure you regularly benchmark your salary to market rates so you don’t fall behind.
How much have you de-risked the opportunity for candidates?
How much information have you actually provided to candidates? Most job ads read like a laundry list of endless tools and generic responsibilities, that tells the candidate absolutely nothing. Candidates want to learn basic information and will typically want to know the same 5-7 things.
Try to think of the typical questions that come up in the first interview. Why not be more proactive, and tell candidates that information before they have to complete an assessment or even apply? There’s no point stringing them along all the way into the interview process only to tell them crucial information. This is like telling your fiancé on your wedding day, ‘Oh by the way, I don’t want kids’. Better to figure that out earlier on, right?
On Alooba Assess, you can include all of these in the email invitations and landing page for the candidate. Video is a great medium for communicating these things to candidates in a more engaging and friendly way.
At minimum, make sure you transparently tell candidates these things before they need to engage with you further:
You should include the full remuneration details in the job ad itself. This will include the base salary, any bonus, share option scheme, health insurance, paid time off etc. This is not the time to be coy. Don’t be tempted to use vagaries like ‘competitive salary’. What does that mean? The answer to ‘How much is the salary?’ is a number, not a platitude.
You should be crystal clear about where the role is based. Is it fully remote? Or is there an expectation that they will need to come into a particular office periodically? Is this paid for by the organization? Again, this is no time to be coy and say things like ‘flexible working arrangements’. What does that mean? Just be straightforward with candidates and if the working arrangements don’t align with what they want, they can disqualify themselves.
In recent times we’ve seen a lot of companies advertise to have a ‘work from anywhere’ policy that sounds great, but in the end is more like ‘work from anywhere your boss wants you to’. Please try to avoid this bait and switch trick.
OK so you’ve accurately described the Data Analyst role, for example, that you’re hiring. The candidate likes it and is keen, but what is the career progression? Where does this role go next? Is there a ‘Senior Data Analyst’ position? Giving candidates some indicative sense of where the role goes next will entice them to apply for the position. Granted, this is easier to elaborate in the interview stage, but again you’re looking for ways to educate the candidate on the opportunity and really sell it to them.
Mission, Values & Goals
What does this organization actually do? What is the mission of the organization? What are the main organization values? What are the goals of the organization? Nowadays especially, candidates are not in it just for the paycheck. Most candidates are looking for an organization that they can believe in. You’ll want to make this super clear in the job ad to attract the right candidates for your role.
What is the team the role is in? What is the team responsible for? Who are the teammates and the manager? You should clearly explain who this person will be working with.
The easiest way to do this is to list each teammate, their title and then a link off to their LinkedIn page. This immediately tells the candidate who they’ll be working with, answers basic questions like ‘How many people are in the team?’ and also gives an indication of the diversity of the team.
Candidates realize they will be spending 8-12 hours a day with these people, so this step is important.
Candidates always want to know about the tools that they’ll be using. Some will want to continue on and specialize in a particular stack, while others will purposefully be looking for a change to broaden their skillset.
Either way, you need to be clear about what technologies are actually used day-to-day. Candidates want to know this partly to cross-check if there’s any BS on the job ad. E.g. if you’ve described the role as an Advanced Data Scientist and then the main tools are SQL and Tableau, the candidate knows something does not - excuse the pun - stack up.
Candidates want to know what they’re getting themselves into. A lot of hiring processes are quite opaque and just drag on forever with more and more arbitrary steps.
You should clearly state:
Day In The Life & Specific Problems
Finally, what does a ‘day-in-the-life’ look like for this role? Can you accurately describe what the candidate will actually do day-to-day. Try to be realistic but also sell the role in a meaningful way. It’s very easy for generic job ads to include endless buzzwords and oversell the complexity of what’s being done. This is where candidates want to know the reality. If you are able to quantify this, it would be ideal.
Is it 90% reporting and 10% analysis? Or is it 90% analytics and 10% reporting? These two roles are very different to a candidate, and it’s essential you be as transparent as possible about this early on.
The last thing you want to do is oversell a role and then have the candidate drop out at the interview stage, or worse still, start the role and quit after a month because it’s not the right role for them.
As you have seen, manual CV screening is not a great option because of the high cost, slow turnaround time, small volume of candidates and relatively low quality final outcome. Not convinced? Feel free to explore our functionality here.
Ready to rock and roll with Alooba Assess? Get started here.
There are definitely some situations where totally replacing manual CV screening with Alooba Assess will not work very well. In this situation, we’d suggest a lighter touch approach, but to still use an assessment later on in the hiring process.
We would not recommend using Alooba Assess as a first step for:
Very senior roles who would not be accustomed to skills-based assessments (e.g. C-level, partner level etc.). Any roles where there are very few candidates (e.g. 5) and you can literally interview all of them. When your sourcing strategy is to headhunt candidates.
Also, if you’re in a very traditional organization that isn’t ready for the move to objective skills-based hiring, then Alooba Assess is probably not the best option for you right now.
The diversity of our pool has definitely improved so we just have many more candidates from just different backgrounds which I am a huge believer in. It makes the team much better, it makes our output much better and gives us more voices in terms of building the best product and service that we can.
Piers Stobbs, Cazoo (Chief Data Officer)