The language candidates use in conversation can reliably indicate their propensity to ‘job hop’, new research shows.

Sapia, which uses text-based communication to interview candidates, has uncovered a correlation between candidate language and job churn that is “stronger than what you would find normally in traditional psychometric testing of job-hopping”, says CEO Barbara Hyman.

HEXACO Personality Model & Job Hopping

Similar to its recent study measuring candidate personality traits, researchers used data from 46,000 job applicants who completed an online chat interview and used the six-factor HEXACO personality model to analyse responses.

The HEXACO traits are honesty-humility, emotionality, extraversion, agreeableness (versus anger), conscientiousness, and openness to experience.

The ‘openness to experience’ trait has long been considered in organisational psychology circles as an indicator of job-hopping, and this has been reinforced by Sapia’s research, says Hyman

“Low agreeableness also correlates with people who may move and look for better opportunities,” she adds.

Analysing candidates’ responses to determine their job-hopping likelihood is especially useful for many entry-level roles, where people do not have prior experience on their CV.

“We know ‘flight risk’ or staff churn is a really big problem for our customers, particularly those who hire at volume into low-skilled roles. For them to be able to identify this upfront and avoid or minimise it was really valuable,” Hyman says.

And, from the candidate’s point of view, “we’re seeing a real craving and an appetite for understanding yourself and understanding where your strengths are best placed”, she adds.

The researchers also note further work is required to assess the true predictive validity of the outcome – that is, establishing the correlation between inferred job-hopping likelihood and actual job-hopping behaviour.

Addressing bias

Sapia has also incorporated the job-hopping measurement into its algorithms to provide this additional information to recruiters, says Hyman.

Importantly, however, “we don’t automatically discount someone who has a high job-hopping likelihood; it’s just another data point you get to look at”.

For some employers and roles, the ‘openness to experience’ trait is generally desirable, Hyman says.

“In investment banking, you want people who are comfortable with looking outside of the box and being really curious and questioning,” she says by way of example.

She stresses the intention is to allow recruitment decision-makers to use the technology as a “co-pilot, not an autopilot”.

Read more here: When used properly, data amplifies inclusive hiring.

Barbara Hyman, Shortlist, Thursday 27 August 2020 2:20 pm

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We agree with Katrina Collier: Recruitment isn’t broken, per se. It needs a bit of work, sure, but in the midst of the Great Resignation, dedicated talent acquisition managers all over the world are doing some of their best work. They’re finding top talent and helping businesses succeed.

Despite this, we can say that candidate experience is certifiably broken. Ghosting rates are up somewhere around 450% since the start of the pandemic. 65% people say they rarely receive notice of their application status (Lever), and 60% of people say they have bailed on a job application due to its length or complexity. 

Why candidate experience is important

Many mid-to-large sized companies spend in excess of $200,000 per year on sourcing and advertising (assuming a hiring rate of fifty people per year). Few invest in candidate experience. We tend to overlook the fact that the candidate journey from application to offer (or rejection) is just as important for the health of a recruitment funnel, over the long term, as good ads or recruitment strategies.

Good candidate experience, put simply, is your best chance at securing the talent you want. In the wake of the Great Reshuffle, employees have the power to choose when and where they work, and they know it. If you can’t reach them and woo them in a reasonable time frame, you’re at a supreme competitive disadvantage. They’re here today, gone tomorrow. That means that multi-round interview funnels and tedious psychometric games aren’t going to cut it anymore. Today’s candidate wants speed, perks, and flexibility. Your experience should be designed with this in mind.

There are a lot of ways candidate experience might be improved – this article offers some tips, including advice on a term we like to call the Gucci principle.

One easy place to start is with your job ads.

How to write a good job ad

Good job ads are concise and well-formatted. They put employee value proposition up front. They discuss the vision and purpose of a role, and not just day-to-day responsibilities. They avoid the term ‘competitive salary’ – in fact, they disclose salary ranges. They’re not necessarily short, either. Anyone who tells you that a job ad must be short to be good does not understand the anatomy of an advertisement.

Here are our top tips.

1. Make sure the spelling and grammar in your job ad is perfect, throughout

This seems like a minor point, but good spelling, grammar, and sentence structure is essential for your employer brand. It’s a matter of perception. Poor writing casts doubt on the legitimacy of your brand, and on your capabilities in general – after all, if you can’t write a clean job ad, how can the candidate be sure you can do other, more important things, correctly?

Have someone in your marketing team cast their eye over your ad before it goes out. Proof-reading should always be a part of your customer outreach. If you don’t have a marketer on which to rely, consider investing in editing software like Grammarly.

2. Keep the unique language of your brand

Funky company names are in vogue. Just look at ours. Because we’re called Sapia, we refer to our team (and even our customers) as Sapians. Therefore, we do the same with our job ads. It creates branding consistency, and works as an unconscious primer, suggesting to candidates that they’re joining a well-knit, stable, and purpose-oriented team. 

The same goes for language. If you’ve adopted or created certain words to make your brand stand out, they should also be used to make your job ad stand out. Look at this example from Gong: They tell the candidate that they’ll be creating edu-taining content. That’s a lot more interesting than “you’ll be writing content that is both educational and entertaining.” Had they chosen the latter sentence, you’d doubt their credibility, because that sentence is not remotely entertaining.

Or take this example from one of our own job ads. You might say that using a curse word (oh dear me!) in a job ad is inappropriate, but we don’t. We’re Sapians, and that makes us passionate humans. We understand that writing the way you speak is the quickest way to build rapport. Tell us that you don’t get that impression from this paragraph.

3. Clear categorisation and formatting of sections

A job ad doesn’t need to be short, but it should be formatted for scanning. Candidates should be able to easily read it, extract the main points, and make the call to apply, all within minutes. We like the following job ad section structure:

• Perks and benefits
• Responsibilities
• Qualifications

Each section can be as long as you need it to be (within reason), but it should also be set out in dot points. Easier to read, easier to digest. Many are the job ads that set out position duties and benefits in great big walls of text. Go with dot points, like Gong has, and you’ll stand out.

4. Make it as easy as possible to apply

Depending on the platform you use, it can be difficult to control how candidates enter your funnel. Regardless, you can make it easier by clearly sign-posting the action you expect them to take. If it’s a LinkedIn EasyApply button, great – but don’t confuse candidates by asking them, at the bottom of the ad, to email their CVs to you. This happens a lot.

Make sure you have a single call-to-action, and make it clear. Add it to the top and bottom of your ad. 

You know what they say about first impressions? That’s why it’s so critical to get your job ads right. Check out this post on LinkedIn for more tips on writing the perfect job ad.

AI is the fastest growing trend of any technology – EVER

It used to be all about Mobile First, now it’s about AI-first. Google now calls itself an ‘AI first’ company.

“How do you decipher the truth from puffery? Are there any shortcuts to really understand where AI is best applied in your business?”

I’m not a data scientist, but I spend my days talking to users and buyers of AI technology who are befuddled and increasingly cynical about the hype. Here is my 3-step guide to cutting through the noise.

1. Understand that AI is not the same as Machine Learning

Most products are using standard statistical techniques like regression. Without any machine learning baked into the technology, they are just matching tools. There are efficiency gains, but no ‘smarts’ and no learning in technology.

For example, in recruitment a stock-standard AI product would merely find you people with the same profile as those you have already hired, matching applicant profiles to hired profiles. CV parsers do this kind of thing. Now, that can be helpful to you if all you want to do is a short-cut to the same profile fast, and it can definitely save your recruitment team time.

But unless you know that these characteristics also match performance, you will not make a difference to your organisational outcomes.

If your Sales Director tells you that every new hire over the last year is hitting or exceeding budget, then absolutely keep using that tool. If she tells you that a third or more are underperforming or leaving the business, your AI tool is merely amplifying that bias and doing quantifiable damage to your company’s bottom line.

If you want both efficiency and business bottom-line impact – your AI needs to have machine learning baked in.

Takeaway: If the organisation selling you an AI tool has no Data Scientists, there is no machine learning in the product.

2. No data means no AI

If I imagine a Maslow’s hierarchy of AI it would look like the below:

First up you need to have the data.

About 50% of companies don’t pass this threshold, but assuming you have it the next step is understanding the data context: What is the business problem you are trying to solve?

Next is the housekeeping. This means consolidating, cleaning, categorising and cataloguing the data. And then finally the optimisation is at the top – this is where the magic happens. Optimisation is the last mile and is what gets you to the big savings, but you need everything underneath it in order first.

In recruitment a genuinely smart AI tool with machine learning baked-in works best in these conditions:

1. High volume recruitment where you can gather applicant data fast. Which is also why no one is selling AI tools for exec level recruitment yet.
2. Hiring for roles where you have a way of measuring performance AND that performance needs to be a whole lot better. For example, almost all retail businesses track turnover and usually also whether it’s regrettable or not. If that number is above your industry average, then AI works for you. If you have no way of measuring performance for that role, AI will not give you much return on your investment.
3. Recruitment for low skilled roles or roles where success is not about a specific qualification or vocational skill. It’s about soft skills – communication, learning agility, customer awareness and empathy and so on.

Takeaway: It’s critical to have a solid understanding of what you’re trying to fix, and the means to measure the changes you’re making. Ask yourself why you are considering AI if you can’t quantify the problem, to begin with.

3. Once you know the problem you want to solve expect a long term commitment to fix it

AI is about optimisation. For credit card companies it’s detecting fraud quickly. For online retailers better product recommendations. In recruitment, it’s finding the best new hires in a massive group of rookie players.

In each case, you are optimising for efficiency and accuracy, as the cost of getting it wrong is huge.

It means trusting the technology to find the patterns. You have to suspend theory, and your assumptions, a lot. You feed in a large amount of relevant and unbiased data and the machine learns on its own, finding the patterns. It is looking for the ‘signal in the noise’. Humans are unpredictable and more often than not unreliable.

The current hiring processing by humans is extremely resource-consuming and the result is not always satisfying. Using AI will free up your time if you allow it to, improving efficiency or outcomes, often both. But AI built just off CV data only adds bias and we’ve all see how badly that ends.

Takeaway: Predicting human decision making is not easy and not quick. The only way to get to the ‘answer’ is to start now and expect this to be a journey.

You can try out Sapia’s Chat Interview experience right now, or leave us your details to book a call

If you liked this article, suggested reading: 
A CV tells you nothing
To AI or not to AI