Candidate experience: Everybody’s talking about it, few companies are actively investing in it.
According to a Sapia-sponsored Aptitude Research report from earlier this year, 68% of companies admit they have no plans to address the interview portion of their candidate experience throughout 2022 and 2023. Despite this, 50% of these companies know they’re losing talent due to their application and interview processes. What’s more, according to Forbes, companies that prioritize candidate experience can see their average quality-of-hire improve by 70%.
Why the unwillingness to address such an important facet of recruitment? In most cases, the teams responsible for enacting change to candidate experience are steeped in the everyday throes of talent acquisition, and don’t have time right now to examine their processes. Statistically speaking, this is probably where you’re at. Totally understandable; the 2023 labor market is tough. If your house is on fire, you’re probably not focussed on how well you treat the visitors at your doorstep.
Recently, on our Pink Squirrels! podcast, we sat down with Lars van Wieren, CEO at Starred, a candidate experience measurement tool. Lars offered some practical tips on getting started with candidate experience: Benchmarking it, measuring it at different stages of the process, and setting your business up to review and act on the findings.
Step 1: Benchmark your candidate experience
As the saying goes, what gets measured, gets managed. Lars recommends starting with a basic benchmark for your candidate experience. This need not be difficult, and you don’t necessarily need a fancy tool to start gathering these data.
Simply ask your candidates: How likely are you to recommend our company to a friend or colleague? This is, in essence, a Net Performer Score (NPS) question, and the scale (1 to 10) should reflect that.
Ideally, you should be gathering feedback on your candidate experience at each stage of the application process, but to begin with, ask the question at the very end. And to get the best, least-biased data, you need to ask all applicants whether or not they’ve been shortlisted or hired – if you only ask those who have been shortlisted, or the few people who have been successful, you’re likely to get magnanimous results that don’t reflect your true candidate experience.
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 the health of your candidate experience. Once you’ve got the number, you can start to make small changes to your application experience and see how that affects the overall number.
For example, you might consider making the following changes to improve your candidate experience:
- Maybe we don’t need to ask for cover letters. Most candidates don’t like writing them (statistically speaking), and they’re a time-suck for our team.
- Maybe we can shorten the content on our careers page. It’s long, and difficult to navigate.
- Maybe we should consider using Easy Apply functionality.
- Maybe we are running too many interviews in our process. Let’s do fewer, and see how that works.
At the same time, you might consider looking at your candidate abandonment rate – we’ve got a post on measuring and improving it here. Candidate experience scores and abandonment rates are almost always linked. Improve one, you improve the other.
Step 2: Gather more data on each step of your job application process
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:
- Boosts quality of hire from 36 to 58%, on average
- Boosts candidate experience from 34 to 44%, on average
- Improves first-year retention from 35 to 50%, on average
Feedback is critical. And, to make it as accurate and indicative as possible, your feedback should ideally be gathered at each stage of the application process: Application, screening, interviewing, assessment, offer, and rejection.
By doing this, you’ll know exactly where your candidate experience is lacking – and you can make fast, effective changes.
Multi-step candidate experience feedback may not be easy to do with your current setup, but it is relatively simple to configure if your ATS/chosen software solution has the capability.
Step 3: Get your team around the candidate experience data
Generally speaking, the task of improving candidate experience is that of your entire talent acquisition or recruitment team. But it’s a good idea to appoint an internal candidate experience champion – someone who is responsible for collating the benchmark data and regularly reporting on it.
What’s the reporting cadence? Depends on the amount of applications you have, and the length of your application process. A monthly score update check-in works best for most. Monthly measurement will likely give you an insightful trendline.
While the task of improving candidate experience is never done, it needn’t require an overhaul to your entire recruitment business. Start small, make iterative improvements over time, and focus on making at least one more candidate smile.
(P.S. Candidate experience is a natural benefit of using our Ai Smart Interviewer – in fact, more than 90% of the 2 million candidates we’ve interviewed have loved our process, and 84% of them are highly likely to recommend you to a friend or family member. Reach out to us here to find out more.)
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Why strange job interview tasks live on, and how hiring Ai fixes them
It is widely thought that Thomas Edison invented the concept of the job interview back in the early 1900s. To screen candidates, he would ask them to join him at a restaurant and eat a bowl of soup while he watched. He could pick out the losing candidates by their tendency to season their soup before eating it. According to Edison, premature salt-and-peppering speaks to a person’s over-reliance on assumptions. If you’re a true visionary, he posited, you leap into your soup face-first.
The soup test is definitely out there. And, given what we now know about psychology and candidate experience, it is not, strictly speaking, scientifically valid. But this exercise was first tested more than 100 years ago, so maybe we can forgive Edison for filling the holes in his data with social experiments.
Funnily enough, though, things haven’t changed much since Edison souped up his hiring game. Initial face-to-face job interviews remain the predominant tool of hiring managers. There are benefits to in-person interviews, but the deficits certainly outweigh the benefits. Simply put, the practice is infused with all manner of biases, unfairnesses, inefficiencies, and oddities. In the early 1900s, we had soup – now we have inscrutable corporate-isms, and bizarre group tasks with arbitrary scoring criteria.
We’re naming names: The ‘pen-drop’ test
Let’s say you’re looking to fill a position where quick thinking and adaptability are the two most important skills. You want your candidates to think fast, and think smart, especially when faced with sudden adversity. How do you find these people?
There is no perfect answer. People are people, after all. But there are far better ways to find out than dropping a pen in the middle of an interview to see whether or not a candidate picks it up for you. The ‘pen-drop’ test assumes that the quickest candidates are the most adaptable, and are the highest in empathy. But we have more reliable predictors for these, predictors subject to far fewer variables. The quickest pen picker-upper on a given day may not be the best lateral thinker, or the most open – they may have merely been the shortest candidate, or the most flexible candidate, or the candidate closest to the pen. Because you don’t have a control, or any way to account for variables such as these, can you really trust the findings?
Yes, the pen-drop test is an extreme example of a screening exercise that is only tenuously related to its desired outcome. But we have all, at some point in our working lives, participated in strange tasks and odd jobs during interviews. The greater point is this: Even the best-planned exercises are not a viable substitute for sound scientific measurement.
The fairest way to conduct job interviews AND get candidate data you can trust
The HEXACO personality inventory has at least three major dimensions relating to the test of a quick-thinking, empathic person: Extraversion, agreeableness, and conscientiousness. If you can assess a candidate using the HEXACO inventory, you might learn that the candidate is:
- High in trait extraversion, which makes them more likely to take the lead in social situations, and interpret social cues
- Low in trait conscientiousness, which makes them more likely to go with the flow, and adapt to unexpected situations
- High in trait agreeableness, which suggests they are more ‘willing to compromise and cooperate with others’.
And that’s only the start of what you might learn. By using an Ai-based recruitment or hiring tool, with a HEXACO personality modelling function, you have a simple, trustworthy, accurate, and fair way to sort your quick thinkers from your leaders, your leaders from your long-term planners, and so on.
That’s the essence of what a smart interviewer can do, and why we developed the world’s first smart interviewer. You no longer need to think up some strange post-interview exercise where you pull unsuspecting candidates into an impromptu indoor hockey game. You can simply:
- Entrust your first interviews to Smart Interviewer, our quick-learning Ai smart interviewer
- Receive a full report on each candidate, including their responses and where they sit on the HEXACO personality scale
- Instantly match candidates against the values you desire most (for example, ‘quick-thinking’ or ‘adaptable’.)
(We’re not the fun police, of course. If your approach to offering first-rate candidate experience involves a blind-folded three-legged race, count us in. Just make sure you have a smart interview waiting at the finish line. Fun, then statistical validity. Best of both worlds.)
We all want a world filled with better, fairer, simpler interviews. How will you go about it? Data, or gut-feel? Soup, or science?
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Hiring automation: It’s Time To Use Your Words
This Recruiter Says How You Write and What You Write Can Tell an Employer a Great Deal About Your Fit.
Barbara Hyman believes the most important skill for people looking for a job in the post-COVID world will be the ability to write.
“People who think clearly, write clearly,’’ says the chief executive of the artificial intelligence-powered recruiting firm Sapia, which judges its candidates on the most basic of skills.
The firm, which has big-name backers including Myer family member Rupert Myer, former Aconex founder turned venture capitalist Leigh Jasper, fund manager Dion Hershan and former JB Were partner Sam Brougham, gives every job candidate a first interview by asking them five text-based behavioural questions on their phone that take around 20 minutes to answer.
Then the company’s predictive models assign a “suitability” score to each candidate using over 80 features extracted from their responses and the system specifically precludes the use of names, gender and age to determine the recommended shortlist, removing unconscious bias from the recruitment process.
The company’s technology has already been used by 400,000 candidates looking for roles in retail, healthcare, customer service, hospitality, contact centres and graduate and trainee roles across 34 countries.
Its clients include Qantas, Bunnings, the Iceland Group grocery chain in the UK and Spanish telecoms giant Telefonica.
But Hyman says her biggest target client in the post-COVID world is government.
She believes the economy can only be sustainably reactivated through large-scale job security and that requires redeploying existing skillsets to meet in-demand industries.
“This requires a sophisticated and scaleable solution to find jobs for those whose industries have been decimated by the pandemic and have no jobs to return to. Our solution can immediately activate these job seekers into the new economy, steering them to the jobs they will be good at, she says.
She claims if the government activated this sort of technology for a range of growth industries the economic and social impact would be unprecedented.
“In a healthy economy, the cost benefit in Australia alone is $1bn net benefit (cost) for every 100,000 workers that get back to work one month earlier through reduced welfare payments and increased consumer spending. That is significantly higher when accounting for government subsidies as a result of COVID,” she says.
“A big part of getting back to work is the confidence and the mindset. We are exploring different avenues to allow people to use our chat bot to find their true role in the new economy. This is the vision we are trying to sell to government – you have your own personalised career coach that helps you find the ideal role.”
Hyman said one of the company’s big-name backers Rupert Myer, the chair of the Australia Council for the Arts and an emeritus trustee of The National Gallery of Victoria, had given her “amazing introductions” into the government and university sectors.
“When I came into the business in February 2018 it was running out of money. I had to get a bunch of the existing investors to support me,’’ says Hyman, a former chief human resources officer at REA Group and a human resources and marketing director at Boston Consulting.
Her data science leader at Sapia is Sri Lankan-born Buddhi Jayatilleke, who has a diverse background in machine learning, software engineering and academic research.
The firm has raised $4m in the past 2 years, including bringing in Australian global recruitment and talent management firm Hudson as a strategic investor last year.
“That gave us credibility because the number two recruitment firm in the market believes in what we are doing,’’ Hyman says.
“Whether you like it or not, there is enormous amount we can learn about you in 200 words. Just the very fact we don’t use any secret or behavioural data, you have to build trust from the beginning with your candidate. The completion rates are 95 per cent, the engagement rates are 99 per cent. But the key point is when we give you back your feedback. It is effectively a public service we are performing with this feedback.”
One of the firm’s initial backers was Rampersand, the venture capital firm which has a focus on early growth stage tech businesses.
Rampersand co-founder Paul Naphtali says the firm invested in Sapia for its ability to put data at the centre of a company’s people strategy.
“It’s a massive challenge for a start-up to aggregate the data and build the algorithms that can identify an individual’s suitability to a role quickly and accurately. It was a bold and ambitious plan from the beginning, and Sapia is now well on its way to becoming that data-centric engine,’’ he says.
“The company started with working to turbocharge the recruitment process by quickly identifying the right talent for the right roles.
“It’s taken time to build the tech and the data sets, but it’s paying off as a number of Australia’s leading companies now have Sapia as a default part of the process.”
He says the firm is now entering a new phase “where it also powers internal people management as well as for job seekers, which is obviously very relevant in the current environment”.
Recently in London Sapia was awarded the TIARA Talent Tech Star which honours the businesses globally in the talent acquisition industry.
Source: DAMON KITNEY, The Australian, October 30, 2020
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AI makes resumes redundant. Now it’s disrupting your interviews
Previously, I’ve written about how the world of recruitment has evolved.
It seems that using AI could consign fantastical or over-optimised resumes to the dustbin of history, along with the Rolodex and fax machines.
But how do we go about selecting the perfect (or as close to perfect as possible) candidates from AI-created shortlists?
It should be so easy to learn how to conduct an interview that adds the human element to the AI selection. The web is awash with opportunities to earn recruitment qualifications from a variety of bodies, both respected and dubious. There are so many manuals, guides and blog-posts on the best ways of interviewing. People have been interviewing people for hundreds of years.
And yet…
Why are there no perfect interviews?
We’ve all heard about bizarre interview questions (no explanation needed). We’ve felt the pain of people caught up in interview nightmares (from both sides of the desk). And we’ve scratched our heads and noses over the blogs on body language in face-to-face interviews(bias klaxon).
Even without the extremes, people have tales to tell. Did you ever come away from an interview for your ideal job, where something just felt wrong?
It’s clear that adding human interaction to the recruitment process is by no means straightforward. Highlighting these recurring problems doesn’t solve the underlying question, which is:
“We’ve used an algorithm to better identify suitable candidates. How do we ensure that adding the crucial human part of hiring doesn’t re-introduce the very biases that the algorithm filtered out?”
Start by asking better questions
Searching for “Perfect interview Questions” gives 167,000,000 results. Many of them include the Perfect Answers to match. So it’s not simply about asking questions that, once upon a time, were reckoned to extract truthful and useful responses.
Instead we want questions that will make the best of that human interaction, building on and exploring the reasons the algorithm put these candidates on the list. Our questions need to help us achieve the ultimate goal of the interview: finding a candidate who can do the job, fit with the company culture AND stay for a meaningful period of time.
It’s generally agreed that we get better interview answers by asking open questions. I’d expand on that. They should ideally be questions that don’t relate specifically to the candidate’s resume, or only at the highest level, to get an in-depth understanding.
We should try to avoid using leading questions that will give an astute candidate any clues to the answers we’re looking for. And we should probably steer clear of most, if not all, of the questions that appear on those lists of ‘Perfect Interview Questions’, knowing that some candidates will reach for a well-practised ‘Perfect Answer’. We want them to display their understanding of the question and knowledge of the subject matter. Not their ability to recall a pre-rehearsed answer.
And so, we need to remember that we’re looking for the substance of the answers we get, not the candidate’s ability to weave the flimsiest material into an enchanting story.
Five new interview questions to ask your AI-shortlisted candidates
So, here are some possible questions to get you thinking.
- We use an algorithm as part of our hiring process to ensure we’re not missing the right candidates. Considering everything you know about us and this rlie, why do you think the algorithm has suggested you might be a good hire?
- If asked, what would your colleagues in your current or previous role say about your work-related exchanges with them?
- Tell me about any of your previous personal or career achievements that you feel relate to this role
- We strive to be as diverse and inclusive as possible. Tell me about the places where you’ve most enjoyed working and the communication styles of people you’ve worked best with. Please expand on your reasons
- Can you recall a time you’ve needed to make a business decision with incomplete information? How did you move forward?
Of course, you’ll need to frame and adjust those questions to match the role and your company.
How AI for HR helps interviews – and interviewers – get closer to perfection
AI equips recruiters with impartial insights that resumes, questionnaires and even personality profiles can’t provide. Well-constructed, supervised algorithms overlook all the biases that every human has. And that can only be a good thing.
Statistically robust AI uses an algorithm, derived from business performance and behavioural science, to shortlist candidates. It can predict which ones will do well, fit well and stay. We can trust it to know what makes a successful employee, for our particular organisation and this specific role. It can tell us to invest effort with the applicants on that shortlist. However unlikely they seem at first glance.
So we can use all of our knowledge and skills to understand a candidate’s suitability and look beyond things that might have previously led us to a rejection.
AI is the recruiter’s friend, not a competitor. It can stop us wasting time chasing candidates who we think will make great hires but instead fail to live up to the expectation. And it can direct us to the hidden gems we might have otherwise overlooked.
Technology like AI for HR is only a threat if you ignore it.
Don’t be that company that still swears by dated processes because that’s the way it’s always been done. The opportunity here is putting technology to work, helping your organisation evolve for the better. The longer the delay, the harder it will be. So don’t be left at the back playing catch-up.
There are very few businesses these days that communicate by fax machines – and that’s for a reason. In a few years, you’ll look back and wonder “Why didn’t we all embrace Artificial Intelligence sooner?”