It’s now well established that a wider talent pool means more opportunities for recruiting diverse candidates and this results in higher returns, increased productivity, and creativity benefit companies with a diverse workforce. The issue isn’t that we need these thighs to be proven anymore, but rather that nothing we’ve been doing to create the change we need has worked.
Though well-intentioned, DEI has not delivered. Companies have been motivated by the optics of their DEI programmes rather than taking consequential actions to bring about change. Unconscious bias training has been proven ineffective because it cannot address the systemic issues that lead to bias in the first place.
Companies have also spent large sums of money and resources improving their cultures that celebrate belonging, but neglecting their recruitment metrics because they excuse lack of diverse talent as a ‘pipeline problem’.
To address this we need to do something radical. Because what we are doing just isn’t working.
This is where Ai is, where the power of technology can really have a positive impact on the world.
You need to find undiscovered talent.
Undiscovered talent is the talent that you overlook when using traditional hiring practices that rely on CVs, which are limited in communicating real skills, and job interviews, which are beset with bias and limited in their insight. By using radical new talent intelligence that uncovers people for their job fit, based on science-backed insights, you start to uncover undiscovered talent. These are people who might have been dismissed because of things like age, past experience, ethnicity, gender or other preconceptions and biases that we have about who we think is a good fit for a job.
Our technology has uncovered some amazing talent for the companies that we work with, that they would have otherwise missed out on. This is a massive advantage when it comes to making an impact on this issue.
This is how we start to move the dial on Diversity, Equity and Inclusion.
Want to know if technology can give everyone a fair go?
The Workforce Science team are on the road again!
This time, we are heading to Sydney to host a session at APS’s 12th Industrial and Organisational Psychology Conference (IOP).
IOP is Australia’s premier conference for us organisational psychologists, so it has a permanent spot in our calendars. And this year, we got extra excited when the conference was announced.
The theme of the conference is set to;
‘From Ideas to Implementation: Embracing the Challenges of Tomorrow’.
With a theme this relevant to our day-to-day work, we couldn’t stop ourselves from hosting a professional practice forum. The forum’s theme is what Elliot and I spend most of our time thinking about; the robots that are coming for our jobs!
It is crystal clear that there is a real need to discuss how our roles will change in the (not so distant) future.
Leading researchers from Oxford University and Deloitte estimate that machines could replace up to 35% of all job types within the next 20 years. So, we will need to find ways to coexist and work with the machines. But how?
In the forum, we will discuss our view on how the role of organisational psychologists will evolve. We will also present our thoughts on how this shift will impact us, both negative and positive aspects.
If you are attending IOP, feel free to come along and add to the discussion!
Our presentation – “The robots are coming (to help us with hiring) for our jobs” – is scheduled for Thursday 14th July at 3.30pm.
We would love to hear your thoughts on the opportunities and challenges we face, as implementation of AI gets more widely adopted.
If you’re not attending the conference, but still would like to discuss this, don’t hesitate to drop us a line on LinkedIn (Elliot Wood/Kristina Dorniak-Wall). Elliot and I are always keen to chat about it!
Hope to see you at IOP!
An average time-to-hire of 40 days. Hiring costs in excess of $2,000 per candidate. An average turnover rate of 60-70%.
The challenges of hourly recruitment in the retail industry have been well-documented.
Despite this, many of the largest companies persist with old-school recruitment processes.
Given the break-neck pace and scale of the industry, it’s hard to diagnose and fix the problem.
Understandably, many HR leaders have been quick to layer on technology solutions that seem to make things easier; in actuality, these tech solutions have added complexity, making efficiency gains difficult and actionable insights hard to find.
Where recruitment is concerned, a HR tech stack tends to look like this: an unwieldy ATS, often coupled with a conversational AI or scheduling tool.
This stack is implemented across a decentralized system – hundreds of stores across the country – resulting in a situation where hiring managers are forced to use systems they don’t understand and don’t like.
The bottom line is this: Retail companies are overstacked, overworked, and need to adopt different solutions to old problems.
One of the biggest challenges with recruitment at major retail companies is high turnover rates. Retail staff members move fast and often, and have a high likelihood of migrating to competing businesses.
This is partially a nature-of-the-beast problem, but if we better understand what makes people tick, we can better match them to the roles at which they’re likely to succeed, and therefore keep them longer.
For example, we know that the best retail cashiers are high in extraversion. They’re energized by being around people, have good interpersonal skills, and have a lower likelihood of experiencing negative emotion while on the job.
It makes sense, then, to prioritize extraversion when matching candidates to the role of cashier. That’s a personality trait – with attendant soft skills – that will predict success for that role.
When people are matched to the job for which they are best suited, they’ll experience higher levels of purpose and satisfaction. It’s obvious why – the daily activities will invigorate rather than drain them. People who have purpose stay longer.
Therefore, if you accurately match soft skills to roles, you’ll reduce churn. Our AI Smart Chat Interviewer is really good at this: Across the board, our skill-matching power reduces non-regrettable churn by a minimum of 25%.
If you’re keen to get started measuring soft skills, download our HEXACO job interview rubric. It features more than 20 interview questions designed by our personality psychologists to assess the skills of candidates that come your way. It will even help you figure out what soft skills are best for you based on the needs and values of your organization.
Chances are, when your employees or candidates leave, they’re probably staying within the industry – and that means they’re likely going to your competitors. It’s 2023, and the stock-standard advice would be to offer higher wages and perks.
That’s not always feasible, and besides, there’s no guarantee that doing so will markedly reduce the threat of poaching and abandonment. Money is important, but it doesn’t trump purpose and belonging.
The key to better employer branding is a system for active listening. Find out what your people, be they employees or candidates, think. Ask them often. It’s important to do this at the onboarding stage, but it should continue through to the point of highest churn – the six-month mark.
Our joint report with Aptitude Research uncovered some interesting data on the importance of two-way feedback between candidates and employers.
Gathering and acting on mutual feedback:
An NPS (Net Performer Score) framework is a good place to start. How likely are you to recommend our company to a friend or colleague?
The NPS tracking question is easily configurable and embeddable into automated emails, meaning it can be set up through your ATS with little additional work.
When you begin to analyze the data, keep things simple: Dump the data into a spreadsheet, and look at your average numbers. If your score is below 0, you’ve got work to do – if it’s 0 to +30, you’re doing well. 30+ and over, well done!
(If you’re reading this, it’s probably not likely that you’ll get a 30+ score on the first go-round. That’s okay – the goal is to find out how much work you’ve got to do.)
The benefit of benchmarking NPS is that it gives your business a single, easy-to-understand proxy for employee engagement. Once you’ve got the number, you can start to make small changes and see how that affects the overall number.
We hear it all the time: Sourcing is a big problem. When we ask customers about their current processes, however, a common problem emerges: We don’t really know how many people we’re losing from our recruitment funnel, and why.
This presents a great opportunity: Often, improving an application process means removing things, rather than adding them.
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:
Let’s say 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?
We’ve got an in-depth guide for measuring and improving your abandonment rate here.
To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.
By Jennifer Hewett, Australian Financial Review, 31 January
The online questionnaire wants to know whether I respect and comply with authority. I get five options – strongly agree, agree, neutral, disagree or strongly disagree. I tick “neutral”. Well sort of, sometimes, I think to myself.
Same choice for whether I am good at finding fault with what’s around me at work. I tick “neutral” again, guiltily acknowledging it’s just possible my editor might have a different opinion about whether I am far too good at that particular skill.
The choice seems less ambiguous when I am asked whether I forget to put things back in their proper place. I hover over “strongly agree” or “agree” and tick the latter – perhaps a little optimistically.
And on it goes for 90 questions, with slight variations in the possible answers, as devised by an AI (artificial intelligence) algorithm. My responses to the bot will determine whether I get to the next stage of actually being interviewed for a job by a real person. AI approves who you should interview
I soon get an encouraging email from Michael Morris, chief executive of Employsure – a company which provides advice on workplace relations and health and safety issues to small businesses. If I ever give up journalism, Morris tells me, I can try for a new career at Employsure. AI has approved me. Despite my deep scepticism about the process, I can’t help but feel a little pleased by the bot’s assessment.
That is because my rather self-serving answers to random personality questions fit those of the best performers at Employsure. There’s no possibility of ageism or sexism or any other latest “ism” influencing that. No old schoolmates or university or sporting framework, no biases about looks or clothes or mannerisms or personal history.
Instead, I participated in what is a variation on a personality test – based on the algorithmic analysis provided by another company, Sapia, operating in Europe and Australia and with 20 clients.
Morris says Employsure tested the performance of employees selected by Sapia’s algorithm against the choices of Employsure’s own human recruitment team for much of last year.
The fast-growing company hired around 450 people in 2018 with a workforce now totalling more than 800. Morris wanted good people and those more likely to stay.
The experience convinced him that rather than using more traditional CVs to screen applicants, it was worth paying Sapia for its AI technology as Employsure continues to expand its numbers this year. Employsure now only interviews the 10-15 per cent of those who are graded “yes” or “maybe” by the bot.
“The overlay of AI made a significant difference in overall performance, productivity and tenure,” Morris says. “And it means the recruitment team can have a head start on engaging in better conversations with those who have interviews.” This is still a distinct minority view among Australian businesses which have been generally reluctant to embrace the promise of AI when it comes to hiring.
Read: The Ultimate Guide to Interview Automation
The trend to make greater use of AI in business generally is inevitable and accelerating. Just consider all those online “conversations” we now have about customer service and products as the ever-patient bot nudges us this way and that.
Just as inevitably, it is leading to community concerns about whether AI will be used to replace too many people’s jobs. According to a study by the McKinsey Global Institute, intelligent agents and robots could eliminate as much as 30 per cent of human labour by 2030. The scale would dwarf the move away from agricultural labour during the 1900s in the United States and Europe.
Of course, the record of technology shifts over centuries always ending up creating many more other types of jobs does not completely soothe fears that this time it’s different. Even if such alarm is overstated, dramatic changes in technology can certainly prove socially and economically disruptive for long periods. AI can also be scary.
But this version of AI is more about filling new jobs more efficiently. Many large global companies already use it to filter job applicants, especially those coming in at lower levels. Its advocates argue it efficiently eliminates bias or the tendency for people to hire in their own image.
Not that this always goes smoothly – even for the most digitally sophisticated businesses. Amazon abandoned its own AI hiring tool last October when management realised it had only introduced more bias into the process. Its AI system was based on modelling the CVs of those already at the company – who tended to be male. Naturally, that made prospective hires more likely to be male too. So much for gender-diversity targets.
Sapia’s chief executive is Barb Hyman, formerly a human resources executive for the online real estate advertising company REA Group. She says the system doesn’t work for those companies that don’t measure the performance of their existing employees but the data becomes more and more accurate as more information is added.
By matching responses of applicants against only those employees who are already doing well, it can be extremely efficient with immediate payback – especially for larger companies. The data can also be used to change the culture in an organisation by screening the types of personalities who are hired.
Not surprisingly, Hyman says the data demonstrates how different personalities are better fitted to different sorts of roles. So those who do well in caring jobs tend to be reliable and demonstrate traits of modesty and humility. Good salespeople are focused, somewhat self-absorbed, disorganised and transactional. Those who are involved in building long-term business relationships need to be more adaptable, resilient and open.
Sounds more like common sense than AI. But there’s less and less of that around anywhere. AI beckons instead.