The marriage of behavioural science, data science and AI technology

The introduction of artificial intelligence (AI) technologies into the world of HR and recruitment is not just an idea anymore, it is a reality. Neural networks, machine learning and natural language processing are all being introduced into different areas of HR.

These developments contribute to the function’s increased accessibility to data-driven insights and analytics, enabling better-informed people decisions.

In recruitment and talent acquisition, AI technologies have the potential to make a significant impact by identifying candidates who can perform well in individual business environments.

However, pre-hire assessment is a complex area, and without incorporating validated behavioural science we only end up with a 2D view – instead of the 3D view we actually wanted. This is why the marriage of data, computer and behavioural sciences is essential.

By bringing together organisational psychologists, data scientists and computer scientists we truly leverage the power of artificial intelligence – and change the way candidates are recruited. It takes the recruitment process beyond the technical excellence necessary to collect and report on data and insights.

By merging these scientific areas we get:

• Computer science expertise providing the critical ‘how’ for collecting quality data.
• Data science brilliance then revealing the ‘what’ of unseen connections within that data.
• Well-constructed behavioural science explaining the ‘why’ behind those connections.

Through the combination of all three disciplines, we can access a whole extra world of meaning, enabling us to get closer to the core of what’s happening in organisations.

Behavioural science is the key to success

A recent Industrial & Organisational Psychology article pointed to the disruption taking place in the talent identification industry through new digital technologies. The authors noted that although big data is attractive, the data is often thrown together and interrogated using data science until correlations are found. This has become known as ‘dustbowl empiricism’.

My favourite for this at the moment has to be the strong correlation between the number of people who have drowned by falling in a pool, and the number of films Nicolas Cage has appeared in any given year. Who knew how dangerous Nicolas Cage could really be?

Despite the evident danger of watching Nicolas Cage films (particularly near water), I believe there is more value in explaining behaviour than in just predicting it.

For example, is there a correlation between owning a certain type of car and being a high performer?

Perhaps, but I don’t think to look for the best candidates in car parks is very useful. After all, people change cars, and so might the correlations change between particular car models and performance. To cite another famous example, as often as people change their eating preferences, so goes the link between curly fries and intelligence.

Understanding why data is linked can suggest better ways to improve performance than just updating the carpool or changing the canteen menu.

Linking a vehicle preference to well-established behavioural science may suggest that a client considers how a candidate is innovative elsewhere in their lives, such as in their adoption of other new technologies. Or they may look for other ways the candidate demonstrates a penchant for reliability (perhaps through previous work choices).

The scientific approach

This is where organisational psychologists come in.

They have an intimate knowledge of the theories that can help interpret and explain the links between personal attributes and performance, or other variables that matter. They know how to use these theories to solve real problems and they know how to design studies and measurement tools to ensure that scientific knowledge is applied correctly in an organisational setting.

I learned a lot of organisational psychology models and theories during my Masters and PhD studies. We focused on these and the research behind them when I taught MBA and Master of Organisational Psychology programs – sometimes noting gaps in current models and theories – and designing studies to help extend or debunk what we knew.

While completing my MBA and later in a corporate role, I became skilled in applying that knowledge to the problems managers and executives face.

As an organisational psychologist I often find that it isn’t just knowing behavioural science that matters, it is knowing the behavioural science detail to understand what is most relevant for a role or business problem.

For example, consider sales performance.

Thanks to the popularity of some psychometric instruments, ‘extroverted’ or ‘introverted’ are understood as reliable ways to describe elements of a person’s personality, and many people are convinced that being extroverted is important in a sales role.

However, the research on sales performance says otherwise. An International Journal of Selection and Assessment article shows that across a range of studies there isn’t a strong link between ‘extraversion’ (broadly) and sales performance, despite this being such a common view.

Knowing the detail matters here.

A broad description of extraversion may not do a candidate justice, particularly when we’re focused on understanding performance in a particular role.

Instead, we might be interested in a candidate’s level of dominance, their sociability, what they would be like in a group setting, or presenting to a group to make a sale.

Perhaps we’d be interested in whether they are independent, adventurous, or ambitious, all of which (as potential elements of extroversion) may have different implications for sales performance.

We might also focus on the particular nature of the sales role – many roles are becoming more formalised and structured, with down-to-the-minute journey plans and call times. No wonder then that the Journal of Selection and Assessment article found another personality factor, conscientiousness, to be relevant for predicting sales performance.

The business focus of pre-employment assessments

It’s the acceptance of how important behavioural science is to the new world of AI that has led me to Sapia, where we believe all people decisions should be based on science, data and analytics – not just gut feeling.

Sapia focuses on the things that matter.

We use validated behavioural science to build predictive models, centred on the issues your business wishes to address and their corresponding KPIs. The predictive model is based on your workforce data so it’s unique to your organisation, maximising predictive accuracy while also prioritising the candidate experience.

We use various techniques, including training a neural network to identify what drives performance in the organisation, based on the data we collect. We build our algorithms to achieve accurate predictions from the start, and the model improves over time through machine learning.

We’re now at a point where we can use behavioural science, data science and computer technology to understand the intricate links between candidate information and performance data. With that we can help reduce bias and level the candidate playing field and give managers a 3D view of their candidates, to enable them to make the best people decisions.

Dr. Elliot Wood is a registered organisational psychologist with a bachelor’s degree, various master’s degrees and a PhD in the field. He spent 12 years in academia, teaching master’s-level organisational psychology; supervising post-graduate research; and working on research grants and consulting projects. He then moved into organisational development–focused consulting in Australia and Asia, followed by an internal talent role in a multinational brewer. He is now Chief Organisational Psychologist at Sapia.

References

Tomas Chamorro-Premuzic, Dave Winsborough, Ryne Sherman and Robert Hogan, Industrial & Organisational Psychology, ‘New Talent Signals: Shiny New Objects or a Brave New World?’

Murray R. Barrick, Michael K. Mount, Timothy A. Judge, International Journal of Selection and Assessment, ‘Personality and Performance at the Beginning of the New Millennium: What Do We Know and Where Do We Go Next?’

In the candidate short market we’re in, it’s absolutely critical to keep talent engaged throughout the entire application process. You simply cannot afford to lose the talent that you’ve spent time and money attracting. This sounds obvious, of course, but abandonment is a key problem – and few companies know where, when, and why it is happening. 

Let’s start with the metric, and then talk about how we apply it to your wider talent acquisition journey.

Overall candidate abandonment rate = number of candidates still in the process at shortlist stage, minus the total number of candidates who landed on your careers page, divided by that total number again. Or:

At the very minimum, this is the metric you need to start tracking, because it is a generalized diagnostic for the health of your recruitment process. If you know that you had 100 visitors to your careers (or job ad) page, but your shortlist has only 10 candidates in it, you’ve lost 90% of your possible talent pool at one stage or another. Simple math, yes, but in our experience, many recruiters and talent acquisition managers don’t look at what their starting pool of candidate interest was – and therefore, what their theoretical talent pool might have been – and look only at actual applicants.

This poses another, related question: How do I know what my abandonment rate is at each stage of the application process? 

Let’s say, like the example above, that you had 100 visitors to your careers (or job ad) page, and 20 of them completed the first-step application form on that page. You’ve lost 80% of your possible pool right there. Not great, but at least you know – now you can examine that page to uncover possible issues preventing conversion. Is the page too long? Does it have too much text? Is the ‘apply’ button clearly shown? Is the form too long, requiring too much information to fill out? Are your perks/EVP attributes clearly displayed?

Without examining stage progression in isolation, you might never know why people aren’t sticking around.

To reiterate: As well as an overall abandonment rate, you need to measure the drop out rates at each of the stages of your talent acquisition journey. The next section can help show you what to focus on.

Where, when and why do candidates drop out of the application process?

Conventional wisdom tells us that the longer your application and interview process goes on, the higher your dropout rate will be. But that’s a generalized issue – it tells you nothing about how to fix the problem, beyond simply making it shorter. You need specific, localized data to diagnose and fix your leakage spots.

Data from a 2022 Aptitude Research report on key interviewing trends found that candidates tend to drop out at the following stages, in the following proportions:

• 22% of candidates drop out at the application stage
• 24% at the screening stage
• 25% at the interview stage
• 18% at the assessment stage
• 9% at the offer stage

Good to know, right? If you audit your own journey, looking at these stages and using these numbers as benchmarks, you can quickly identify your weak areas. 

For example: You might be proud of your four-step culture-building interview process, in which candidates have a coffee meet-and-greet with the team they’re hoping to join. But if it’s cumbersome for the applicant and relies on several stakeholders to orchestrate, it may be dragging your process out unnecessarily, and doing more harm than good.

The most common job application abandonment stage: The interview

25% of candidates drop out here. Shouldn’t really be a surprise, should it? Job interviews are long, numerous, and in many cases, ineffective. According to Aptitude Research, 33% of companies aren’t confident in how they interview; 50% believe they’ve lost talent due to poor interviewing.

When asked about their top interviewing challenges, surveyed HR and TA leaders responded:

• Our interview process is too long (52%)
• We make candidates go through too many interviews (39%)
• Out interview process is inconsistent (38%)
• We don’t use objective data to drive decisions (32%)
• We have bias in the interview process (21%)

Let’s focus on that second-last challenge: lack of objective data. Almost a third of companies are approaching their interview and application process with assumptions and gut feelings; and half of them believe their interview process is too long. 

Despite this, 68% of companies say they have not made any improvements surrounding candidate experience this year. How many, then, are looking seriously at their entire talent acquisition journey to see where it’s failing? 

This is why we’re focusing on candidate abandonment rate in this post: It is a simple metric to show the health of your application process, easier to measure than many of the other recruitment metrics for which you’re responsible (the ever-nebulous quality-of-hire being a prime example). As the saying goes, what gets measured, gets managed.

Start here today, and see what you learn.

(P.S. Sapia’s Ai Smart Chat Interviewer combines the first four stages of your process – application, screening, interviewing, and assessment – together, resulting in an application process that can secure top talent in as little as 24 hours.

Because it’s a chat-based interview with a smart little Ai, your team doesn’t need to do anything – everyone who applies gets an interview, immediately. That maximizes your talent pool right from the get-go.

What’s more, our candidate dropout rate is just 15%, on average. That means that 85% of your starting talent pool will stick around.

Why do our candidates stick around? More than 90% of them love the experience. See how we can help you here, today.)

Graduate Recruiting

Article 2 of 2 |

With graduate hiring, trust in your process is even more important. You never know when a poor candidate experience might end up on Glassdoor or Whirlpool or other such sites. Given there is a lot out of our control now, what you can control is how you and your organisation choose to engage with your graduates.

1. Personalise your communications

Be 10x more humanistic in your communications than ever before, to soften the stress everyone feels right now. Use technology that humanises the application experience. Use your photo in emails, dial down the formal side of your comms, show care and empathy right up the top. These are unprecedented times that call for a whole new way of connecting.

2. Reduce the asymmetry in recruitment and give something back! 

It is now table stakes that every graduate experience is one that rewards both sides – offering personalised learning for the grad and some good quality data intelligence for you the recruiter. Intelligence that looks like this https://bit.ly/2R6LuIc

3. Use assessment tools that feel more human 

This means being mobile-first and using engaging and relatable assessments that everyone can do in their own time and untimed to take away the unnecessary pressure. Tools like FirstInterview https://bit.ly/39KoqFP

4. Remove bias from your processes

The right AI tool can ensure every graduate applicant has an equal and fair opportunity to be considered because:

• They don’t rely on the CV, which is a biased and inadequate reference point for the traits that matter the most, such as growth mindset, accountability, drive and grit.
• They remove irrelevant markers of job fit like your ethnicity, your gender and your age, unlike video interviewing, which bakes in biased selection.

5. Hire for values and train for skills 

We have all read the research saying Gen Z will have ten jobs in our lifetime. That means the real skills that matter is grit, drive, accountability, curiosity and even humility to know you won’t always know what you are doing and how to do it!

That means uncovering these traits and values has to be a critical ingredient in your assessment tool. Doing that via human interviews is no longer acceptable given the bias we all bring to those conversations. Still, more than that, few businesses will retain a human assessment process when Ai does it better faster and cheaper. 600x faster and at least 3x cheaper. Here are the 2 metrics that should matter the most to any recruiter.

There are AI tools out there to help you with this including the ability to run a virtual group assessment to deliver the same integrity of assessment days with a lot more efficiency > WATCH VIDEO HERE. 

It’s about time to look at AI for your graduate recruitment.

Get in touch here with Barb, Nick or Jess

To read the first article click here

https://sapia.ai/graduate-recruitment-during-covid-19-whats-different/