It’s a fact: People lie on resumés, whether the format is a LinkedIn profile, or an old-fashioned document.

Checkster reports that 78% people who applied for a job in 2020 lied about their skills or experience.

Another poll by LendEDU found that 34% of LinkedIn users lie, to some extent, on their profiles. Of that number, 55% said they padded out their ‘Skills’ section. To win roles, it seems, many of us are not above a little trickery.

In 2023, when talent acquisition specialists and hiring managers have hours (and sometimes less) to assess candidates, interview them, and woo them, the risk of resumé skill-creep is magnified.

For a rushed and overworked hiring manager, who is fed up with losing talent and looking bad because of it, the process of vetting becomes less about careful analysis and more about keyword-matching.

All of this culminates in a series of statistics that should not be surprising: According to a 2022 Aptitude Research report of more than 300 HR leaders at major companies, 50% of companies have lost quality talent due to the way they interview and hire.

At the same time, 50% of companies do not measure the ROI of their interview process. One third are not confident in their interviewing game as a whole.

The process is broken.

The resumé is at the heart of a greater efficiency problem

That same Aptitude Research report examined the average company recruitment funnel, laying out the points at which candidates typically drop out. It found that, on average:

• 22% of candidates drop out at the application stage
• 24% drop out at the screening stage
• 25% at the interview stage

So you might be losing anywhere from 20 to 40% of your talent pool while you spend time vetting resumés and sifting through cover letters.

Aside from the fact that this is a massive time-waster and a prime source of frustration of hiring managers, enforcing the use of resumés is not an effective way to ensure quality of hire.

That’s for two reasons:

1. 78% of people lie on their resumés, as we mentioned above.
2. Even if they don’t, average humans cannot look at resumés and determine exactly how good a candidate will be at the job for which they’re applying. According to research by Frank Schmidt and John Hunter, past experience accounts for just 3% of on-the-job performance. Put simply, what has happened cannot predict what will happen.

Therefore, our over-reliance on resumés creates problems when we go to interview candidates. It’s a classic problem: Overworked hiring managers formulate questions on-the-fly after making cursory glances at candidate submissions.

It’s little wonder that 25% of candidates bail at this point – often, they’re just reconfirming information they’ve already told you about who they are and what they’ve done.

There is an alternative: Structured interviews. Schmidt and Hunter found that structured interviews are the best predictor (26%) of on-the-job success.

The biggest companies are starting to focus more on this.

According to the Wall Street Journal, employers like Google, Delta and IBM are combatting the tight labor market by easing strict needs for college degrees, focussing instead on interview and assessment processes that accurately measure soft skills and behavioral traits.

Getting started with structured interviews

In its simplest form, the structured interview is based around a predefined set of questions.

These questions are typically behavioural and situational in nature: It’s about giving candidates the opportunity to explore how they think, solve problems, formulate plans, and deal with success and failure.

Therefore, questions like ‘Tell me how you’d respond if [specific situation] occurred’ don’t belong in a structured interview.

Instead, you might ask, ‘Tell me about when something went wrong with work, and you had to fix it. How did you go about it?’

Importantly, the questions you ask must be the same for all candidates. A critical component of the structured interview is fair and balanced comparison of candidates.

If you ask each candidate something different – as so often happens in a fast-paced hourly hiring setup – you can never accurately compare one candidate against another.

In that uncertainty, bias creeps in. It becomes a case of ‘I like this guy, he leans forward when he speaks.’

We’ve developed a handy tool to help you get started with structured interviews today: Our HEXACO job interview rubric. It comes with step-by-step instructions to help you figure out what skills and traits you need based on your open roles and company values.

From there, we’ve supplied you with more than 20 science-backed questions and a scorecard. It’s something simple enough for a busy hiring manager to use.

Remove the resumé entirely, and succeed

There is a possible world in which the resumé serves hiring managers as a kind of back-up validation document, used purely to verify the veracity of a candidate’s skills and experience.

In this world, the first stage of your recruitment funnel is the actual candidate interview.

That’s what our Ai Smart Interviewer can do. It’s a conversational Ai that takes candidates through a chat-based interview, using questions tailored to your open roles.

Candidates give their responses – with plenty of time to think – and Smart Interviewer analyses their word choices and sentence structures using its machine learning brainpower. 

A candidate may be able to lie about their years of experience, or their knowledge of CSS, but our Smart Interviewer can accurately determine their cognitive ability, language proficiency, and personality traits.

Then it can make recommendations to you on the best candidates, according to the criteria you’ve set – and, at this point, you haven’t even looked at a single resumé.

But, as with traditional processes, you have the final say in who you hire.

In 2023, the name of the game is efficiency. Success will be measured in time saved NOT having to screen, review resumes and cover letters, compile candidate feedback, communicate with candidates, or improve hiring manager interview techniques.

When you’re saving that much time and money, your recruitment (or HR) function has more bandwidth to focus on long-term talent acquisition and people initiatives.

Don’t struggle in 2023 – speak to our team today about how we can solve your hiring challenges.

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.)

Navigating HR tech products as a CHRO

Having been a CHRO of a listed company in my last role, I can empathise with the confusion and exhaustion that comes from navigating the myriad HR tech products flooding the market whilst trying to manage many ongoing HR change initiatives.

Last year, as CEO of an HR tech start-up I did what most do in that role — I spent a whole lot of time talking to customers, CHROs, heads of talent, recruiters and business owners, listening to their challenges to build a product that works for them. There are a few themes I picked up on through these conversations.

• Improving procedural fairness — ensuring the most important processes (recruitment, promotion and REM ) is fair in both perception & application
• Solutions which deliver integrity of both recruitment and promotion decisions (as more companies aim to promote from within)
• A strong desire to leverage automation for low value, manual and/or repetitive HR tasks
• Make everything simpler for the HR team and employees
• More insights to make better, fairer decisions
• The challenges of knowing what data matters and building the internal capability to make use of it

What is the ‘right’ tech stack?

‘What’s the right tech stack for my team and our company?’ and ‘how do I integrate all these technologies?’ are questions every CHRO of any sizeable company is grappling with. And the answer is more complicated than committing to a new HRIS.

Whilst I am not a tech expert, I spend many hours a week thinking about one critical part of the HR function that is ripe for technology innovation — recruitment. In that vein, I am sharing some things I have learnt which I hope will be useful to your investments in your tech stack in 2019.

1. Decision-making transparency to enhance organisational trust

There are HR tech products that give you insights on engagement hot spots, employee sentiment, and screen applicants for roles by scraping and analysing people’s personal profiles or communications. If you believe (as I do) that transparency enhances trust, especially when it comes to anything coming out of HR, these tech products could undermine organisational trust and maybe even your employer brand. Look beneath the hood of a tech product to validate how it works. AI and the concern of algorithmic bias is one every CHRO needs to be ready to talk about. Understand the source data and how it will be used in the solution. For candidate selection, any front end testing needs to not only be valid but feel valid to the user. That’s why we use relatable and valid questions to assess candidates in building our predictive models. No CVs, no video and no games.

Any extra discretionary effort by employees is going to be heavily influenced by how much trust your people have in you. Better to invest in tech solutions that allow for more transparency around how decisions are being made, that use reliable, objective and valid data.

2. Create and source forward-looking data for prediction

Think of the people analytics generated by HR today — turnover reports, engagements stats, culture diagnostics, exit survey analysis, 9 box talent management. All of it is backward-looking reporting on the past performance of talent. Much of it also subject to the vagaries of human analysis, therefore biased insights. How many of your organisations use data to validate the placement of people on the ‘potential axis’ of a 9 box? Or use NLP to extrapolate the key themes from engagement surveys and exit survey verbatim?

A bigger challenge for all of this backwards analytics is connecting the dots — how does a culture survey actually move you towards and predict a different culture? My colleague who spent his early years building up the data science team for a leading engagement survey platform and led the benchmarking analysis for their clients observed that year after year the same companies were in the top and the bottom quartile of engagement.

Changing culture is hard unless you change the people — the people you hire and the people you promote.

Predictions versus Psychometrics

The best investment you can make to change the culture and help the organisation move towards forward-looking predictive analytics is to start to capture data from the outset — from your applicant pool, through to the people you hire.

Having a data DNA profile of your applicant and hired pool means you can better target your employer branding, you can identify with high accuracy the profile of the stronger performers, the people who are high flight risk in the early months, the talent that moves fastest to productivity. Knowing these profiles means you can seamlessly feedback into your recruitment a better hiring profile.

This is the power of predictive analytics over psychometric testing which has no feedback loop back to the business on whether the person with the high OPQ test was any good in the role.

3. Data authenticity matters 

‘Garbage in garbage out. This is usually a reference to a data quality issue.

Data can take many forms- it’s not always hard numbers (more on that later), it can be data that is structured and regulated by you vs data that is unstructured and not regulated by you, such as CV’s. The former is always better — closer to the objective source of truth, usually owned by you, and less prone to gaming.

CVs are a source of bias

CVs are a poor man’s data substitute and rarely indicative of anything. A CV is a highly gameable type of data and relying on CV data to select talent exacerbates the risk of bias, as was experienced by Amazon when they built their hiring models around a 10-year database of CVs (mostly male).

I won’t spend time on the risks of bias in CV screening as enough has been written about that, other than to share this from a blog post which quotes academic research that ‘both men and women think men are more competent and hirable than women, even when they have identical qualifications ‘, and that ‘resumes with white-sounding names received 50% more calls for interviews than identical resumes with ethnic-sounding names’. https://www.lever.co/blog/where-unconscious-bias-creeps-into-the-recruitment-process.

Removing bias in the screening process is no longer about social justice, now it’s about commercial outcomes — McKinsey has documented each year since 2014 that companies with top quartile diversity experience outsized profitability growth https://www.mckinsey.com/business-functions/organization/our-insights/delivering-through-diversity

4. Think broadly about ‘what is data’

There are a plethora of surveys that make the point that HR functions are starting to invest in the power of people analytics.

Making data more visual has been a big driver behind the success of engagement analytics companies such as a Culture Amp, Glint and Peakon, transforming ugly engagement decks and the traditional circumplexes into insights-driven real-time dashboards. Visualisation offered by tools like Tableau is table stakes these days for HR.

Data doesn’t always look like data in a traditional sense. Take textual search data, human behavioural tracking data for example. Google has been making money off that data strategy for years and there are now books written about how google search terms are the most accurate mirror to our true beliefs and values (Read Everybody Lies for a fascinating insight into the power of text).

Tracking human behaviour has been mainstream in marketing teams for years, but has been slower to be leveraged in HR. In consumer marketing, no one cares why a person is more likely to buy an item, they are only interested in optimising for the outcome. There has been some interesting research applying consumer behaviour analysis to HR with fascinating insights, for example, that your choice of browser in completing an online assessment is a strong predictor of your performance in the role.

5. Provide data-driven actionable insights for the business

In consulting there is an often-used accusation of consultants ‘boiling the ocean’, which usually refers to those 100-page decks with chart after chart, visualising every data point possible as if the sheer weight of the deck is somehow testament to its accuracy.

Most junior consultants aspire to write the ‘killer slide’, the elusive one slide that crystallises the strategy in one data visual that will transform the company’s trajectory.

As HR teams start to produce more output on people analytics, there is a risk of ‘boiling the ocean’ on people analytics — quarterly engagement surveys, monthly churn data, diversity reporting. Figuring out the ‘so what’ of the data and using those insights to move the needle on business metrics that matter is harder, but also necessary. For HR integrating non-HR owned data is also important to get a fuller picture, especially for sales led businesses. For example, if sales drop off at the 2-year mark, what can HR do about that? What HR processes change as a result of seeing high correlations between sales trajectory in the first 6 weeks and tenure greater than 6 months.

In building predictive models for our clients, this is a sample of some of the actionable insights that were revealed through analysing both HR and sales performance data:

• We found that female sales recruiters had a faster performance trajectory, but with a noticeable slow down at 2 years — this led the client to change their targets at recruitment to hire more women and embed new development programs for women at the critical 2-year mark
• We observed a strong statistical correlation between outbound inside sales performance in the first 6 weeks and retention beyond 6 months, which led to changes in their onboarding but also more stringent probation processes earlier on to avoid holding onto people for too long
• We identified better team leaders based on the relative performance of new starters by team leaders, where performance is defined as speed to sales productivity and retention at the 1-year mark
• We reduced the complexity and time invested in KPI tracking for both recruiters and the operations team through knowing which KPIs truly mattered as lead indicators of performance

6. Think creatively about what business data can help in HR

HR’s role is very much one of building bridges across the organisation — taking a helicopter view of talent, ensuring that the needs of the business will be met in 3 years, 5 years by the people in the business, in enabling communication and collaboration channels across teams and geographies.

Building a single source of truth about their employee base often justifies HR’s biggest tech investment in helping achieve those objectives — the so called ‘one size fits all’ HR system. Yet it’s a big step to assume that even with the HRIS in place that HR has all the data it needs to do its job. Every function is making similar investments — sales & marketing into CRMs, operations teams into rostering systems, LTI and OHS data that might sit in the BU or a separate OHS team.

Last century, HRs accountability might have ended when they filled a role. Today, HR is accountable for ‘talent optimisation’ and that means ensuring people’s success through their career with the organisation, and often even beyond. Knowing how that talent is performing on the job– roster adherence, injury patterns, call centre metrics, sales performance — are integral to optimising that talent pool.

Capitalise on these various streams of data!

I encourage HR leaders to be expansive about what is performance data, especially objective performance data, and being relentless in sourcing that data from their non-HR colleagues internally.

7. HR as a data generator for the business

Data generated within HR can help drive broader organisation decisions. B2C companies with large volumes of sales and marketing applicants can leverage the power of those volumes for the benefit of the rest of the business.

Big brand companies can receive half a million-plus applications in one year, often engaging meaningfully with just a fraction. Technology allows you to test and engage meaningfully with every one of those applicants. Instead of thinking of that pool only as a candidate pool relevant to recruitment, for a B2C business, that pool is most likely also your consumer base and a rich source of data for your business.

Customer acquisition cost (CAC) for product and services like travel, retail, software, financial products range from $7 to $400, with companies committing substantial advertising budgets to reach that kind of audience, yet over in recruitment, they are engaging with them for free, at a point where the candidate/consumer is at their most willing and motivated to engage with you.

Imagine what consumer data you could capture from that applicant pool for the benefit of the business?

Transparency and authenticity, forward-looking predictive data, business impact first, think creatively and broadly, and HR as a data generator. These are 7 themes that can transform your organisation in by leveraging the data hidden within HR through the efficient use of technology.

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