Here’s a hot take: The science of Emotional Intelligence (EQ) is dubious, confusing, and anything but settled. When it comes to talent identification, that can be a problem.
First, what is Emotional Intelligence (EQ)?
We tend to measure EQ in the same way we do IQ: Using a test with a series of questions. But emotion and cognitive ability are totally different, and as sciencealert.com points out, ‘It’s much more difficult to measure EI scores as often emotion-based questions do not have one correct answer.’ Add to this the fact that many EQ tests rely on self-reported data, and you can see how IQ and EQ are not simply two equal sides of the coin that make up a person.
That’s not to say that Emotional Intelligence doesn’t exist, just that it’s a roundabout way of measuring personality traits and behaviours that other mechanisms, such as the HEXACO personality inventory, do more reliably and effectively. EQ also carries the issue of ranking certain traits as more desirable or ‘better’ than others – for example, extraversion, agreeableness, and openness.
The trap of over-weighting agreeableness
When we say someone has good or high EQ, what we tend to mean is that they’re friendly, kind, self-aware, and generally speaking, extraverted. They can adjust their tone and approach depending on who they’re talking to. They’re not known to be rude, or brash, or talk too much.
That’s an estimation of someone with good EQ, and this is the problem: It’s an empirical judgment. And while we think we’re describing someone who is emotionally intelligent, we’re really describing someone who is high in agreeableness, emotionality, openness, and other more valid measures of personality. Sounds like a great person, sure, but not necessarily a better type of person for every situation.
Consider this: Many studies have shown that disagreeable people tend to perform better over their career than people who are polite, kind, and friendly. A great proportion of CEOs, be they women or men, are high in disagreeableness. It’s easy to see why: though there are many downsides to disagreeableness, it pays, in many situations, to possess the ability to be combative, straightforward, and brutally honest. To think of disagreeableness as inherently worse than agreeableness is misguided and, at worst, discriminatory.
And even if that is not true, and all of the varied and ever-changing definitions of Emotional Intelligence lead to better job performance, how do we even measure it accurately?
How do you accurately measure Emotional Intelligence (EQ)?
In the context of hiring, EQ is often used as a gut-feel heuristic we apply to people with whom we gel. Even in structured face-to-face interviews, it can be very difficult to assign as score to the different measures of EQ.
Imagine someone is sitting across from you in an interview. By sight, they appear to be an average person in every way. So, by your questions and their responses, how do you measure their:
- Self-awareness. What are the repeatable, verifiable signals of someone who is aware of themselves? Is it their propensity to correct statements mid-sentence? Is it the way they ask you where to sit before committing to a position? Is it the way they maintain eye-contact? How much or little eye-contact do you need to be satisfied of above-average self-awareness?
- Adaptability, teamwork, or ability to influence. Sure, you might ask them about a time where they had to apply abstract thinking to resolve a stressful situation, but how do you rate their answer objectively? Experience may tell you that they have been adaptable, and that’s all well and good, but how do you rank that against someone who is, personality-wise, highly geared for adaptability, but has not had the opportunity to prove it? In other words, how do you assess potential fairly, from candidate to candidate?
The alternative to measuring EQ badly
Again, aside from face-value judgments of agreeableness and social tact, it’s near-on impossible to assess EQ in any fair or meaningful way. That’s not even accounting for the many biases we, as humans, bring to the hiring process. You might, with some accuracy, be able to appraise a person’s EQ once it’s been proven, but that’s not useful at all in recruitment. In hiring, you’re hedging against unknowns, hoping for the best.
That’s what makes accurate personality assessment so critical – and why we built our Ai Smart Interviewer. It finds you the people you need based on an accurate, HEXACO-based assessment of their personality. One interview, via chat, is all it takes.
We look at the critical power skills – communication, emotionality, empathy, openness, and so on – and profile all candidates fairly against one another. So you’re ranking suitability on objective and repeatable measures. No guesswork involved. No bias.
You bet it works. 94% of the 2+ million candidates we’ve interviewed found their personality insights accurate and valuable. On average, 80% of the candidates who experience our interview process recommend you as an employer of choice, even if they don’t get the job.
Someone with an ostensibly high EQ is, in most cases, someone you might want. But appearances can be deceiving, and humans, by nature, are not good at objectively assessing personality. We’re just not, period.
Get the help you need, and you’ll quickly hire the people you want.
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Contact Centre recruitment & retention – this will blow your mind!
Imagine being able to dial-up (or down) any chosen metric such as NPS, retention, absenteeism, staff turnover or any performance data point simply through smarter, predictive, data-driven hiring.
Predictive Talent Analytics turns the imaginary into reality, presenting a variety of businesses, including contact centres, with the opportunity to improve hiring outcomes and raise the performance bar. With only a minor tweak to existing business processes, predictive talent analytics addresses challenge faced by many contact centres.
Recruitment typically involves face-to-face or telephone interviews and psychometric or situational awareness tests. However, there’s an opportunity to make better hires and to achieve better outcomes through the use of Predictive Talent Analytics.
Many organisations are already using analytics to help with their talent processes. Crucially, these are descriptive analytical tools. They’re reporting the past and present. They aren’t looking forward to tomorrow and that’s key. If the business is moving forward your talent tools should also be pointing in the same direction.
Consider a call-waiting display board showing missed and waiting calls. This is reporting.
Alternatively, consider a board that does the same but also accurately predicts significant increases in call volumes, providing you with enough time to increase staffing levels appropriately. That’s predictive.
Descriptive analytical tools showing the path to achievement taken by good performers within the business can add value. But does that mean that every candidate within a bracketed level of academic achievement, from a particular socio-economic background, from a certain area of town or from a particular job board is right for your business? It’s unlikely! Psychometric tests add value but does that mean that everyone within a pre-set number of personality types will be a good fit for your business? That’s also unlikely.
The simple truth is that, even with psychometric testing and rigorous interviews, people are still cycling out of contact centres and the same business challenges remain.
With only a minor tweak to talent processes, predictive talent analytics presents an opportunity to harness existing data and drive the business forward by making hiring recommendations based on somebody’s future capability.
Telling you who is more likely to stay and produce better results for your business.
But wait, it gets better!
Pick the right predictive talent analytics tool and this can be done in an interesting, innovative and intriguing way taking approximately five minutes.
Once the tool’s algorithm knows what good looks like, crucially within your business (because every company is different!), your talent acquisition team can approach the wider talent market armed with a new tool that will drive up efficiency and performance.
Picking the right hires, first time.
Predictive talent analytics boosts business performance
- Volume & time – with the right choice of tool, your talent team can simultaneously engage hundreds or thousands of candidates and, within a few minutes, be shown which applicants should be at the top of the talent pile because the data is showing they’ll be a good hire.
- Retention – Each hiring intake is full of talent with the capability to perform for the business. An algorithm has effectively asked thousands of questions and subsequently identified the people who will be capable performers, specifically for your business.
- Goodbye generic – Your business is unique. If the algorithm provided by your predictive analytics provider is unique to your business, then every single candidate prediction is personalised. A contact centre has the potential to analyse thousands of candidates and pick the individuals who best fit the specific requirements of the business or team, driven by data.
Consider this. Candidate A has solid, recent, relevant experience and good academic grades, ticking all the right hiring boxes but post-hire subsequently cycles out of the business in a few months.
Candidate B is a recent school-leaver with poor grades, no work history but receives a high-performance prediction and, once trained, becomes an excellent employee for many years to come.
On paper candidate A is the better prospect but with the fullness of time, candidate B, identified using predictive talent analytics, is the better hire.
Instead of using generic personality bandings to make hiring decisions, use a different solution.
Use predictive talent analytics to rapidly identify people who will generate more sales or any other measured output. Find those who will be absent less or those who will help the business achieve a higher NPS. Bring applicants into the recruitment pipeline knowing the data is showing they will be a capable, or excellent, performer for your business.
Now that’s an opportunity worth grasping!
Steven John worked within contact centres whilst studying at university, was a recruiter for 13 years and is now Business Development Manager at Sapia, a leading workforce science business providing a data-driven prediction with every hire. This article was originally written for the UK Contact Centre Forum
You can try out Sapia’s FirstInterview right now, or leave us your details to book a demo
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Algorithmic Hiring to Improve Social Mobility
It is a widely held belief that diversity brings strength to the workplace through different perspectives and talents.
The value is greatest when companies harness the differences between employees from multiple demographic backgrounds to understand and appeal to a broad customer base. But true diversity relies on social mobility and therein lies the problem: the rate of social mobility in the UK is the worst in the developed world.
The root cause of the UK’s lack of social mobility can be found in the very place that it can bring the most value – the workplace. Employers’ recruiting processes often suffer from unconscious human bias that results in involuntary discrimination. As a result, the correlation between what an employee in the UK earns today and what his or her father earned is more apparent than in any other major economy.
This article explores the barriers to occupational mobility in the UK and the growing use of predictive analytics or algorithmic hiring to neutralise unintentional prejudice against age, academic background, class, ethnicity, colour, gender, disability, sexual orientation and religion.
The government wants to promote equal opportunity
The UK government has highlighted the fact that ‘patterns of inequality are imprinted from one generation to the next’ and has pledged to make their vision of a socially mobile country a reality. At the recent Conservative party conference in Manchester, David Cameron condemned the country’s lack of social mobility as unacceptable for ‘the party of aspiration’. Some of the eye-opening statistics quoted by Cameron include:
- 7% of the UK population has been privately educated.
- 22% of FTSE 350 chief executives have been privately educated.
- 44% within the creative industries have been privately educated.
- By the age of three, children from disadvantaged families are already nine months behind their upper middle class peers.
- At sixteen, children receiving school meals will on average achieve 1.7 grades lower in their GCSEs.
- For A levels, the school one attends has a disproportionate effect on A* level achievement; 30% of A* achievers attend an independent school, while children attending such schools make up merely 7% of the general population.
- Independent school graduates make up 32% of MPs, 51% of medics, 54% of FTSE 100 chief executives, 54% of top journalists and 70% of High Court judges.
- By the age of 42, those educated privately will earn on average £200,000 more than those educated at state school.
Social immobility is an economic problem as well as a social problem
The OECD claims that income inequality cost the UK 9% in GDP growth between 1990 and 2010. Fewer educational opportunities for disadvantaged individuals had the effect of lowering social mobility and hampering skills development. Those from poor socio economic backgrounds may be just as talented as their privately educated contemporaries and perhaps the missing link in bridging the skills gap in the UK. Various industry sectors have hit out at the government’s immigration policy, claiming this widens the country’s skills gap still further.
Besides immigration, there are other barriers to social mobility within the UK that need to be lifted. Research by Deloitte has shown that 35% of jobs over the next 20 years will be automated. These are mainly unskilled roles that will impact people from low incomes. Rather than relying too heavily on skilled immigrants, the country needs to invest in training and development to upskill young people and provide home-grown talent to meet the future needs of the UK economy. Countries that promote equal opportunity for everyone from an early age are those that will grow and prosper.
How are employers supporting the government’s social mobility policy?
The UK government’s proposal to tackle the issue of social mobility, both in education and in the workplace, has to be greatly welcomed. Cameron cited evidence that people with white-sounding names are more likely to get job interviews than equally qualified people with ethnic names, a trend that he described as ‘disgraceful’. He also referred to employers discriminating against gay people and the need to close the pay gap between men and women. Some major employers – including Deloitte, HSBC, the BBC and the NHS – are combatting this issue by introducing blind-name CVs, where the candidate’s name is blocked out on the CV and the initial screening process. UCAS has also adopted this approach in light of the fact that 36% of ethnic minority applicants from 2010-2012 received places at Russell Group universities, compared with 55% of white applicants.
Although blind-name CVs avoid initial discriminatory biases in an attempt to improve diversity in the workforce, recruiters may still be subject to similar or other biases later in the hiring process. Some law firms, for example, still insist on recruiting Oxbridge graduates, when in fact their skillset may not correlate positively with the job or company culture. While conscious human bias can only be changed through education, lobbying and a shift in attitude, a great deal can be done to eliminate unconscious human bias through predictive analytics or algorithmic hiring.
How can algorithmic hiring help?
Bias in the hiring process not only thwarts social mobility but is detrimental to productivity, profitability and brand value. The best way to remove such bias is to shift reliance from humans to data science and algorithms. Human subjectivity relies on gut feel and is liable to passive bias or, at worst, active discrimination. If an employer genuinely wants to ignore a candidate’s schooling, racial background or social class, these variables can be hidden. Algorithms can have a non-discriminatory output as long as the data used to build them is also of a non-discriminatory nature.
Predictive analytics is an objective way of analysing relevant variables – such as biodata, pre-hire attitudes and personality traits – to determine which candidates are likely to perform best in their roles. By blocking out social background data, informed hiring decisions can be made that have a positive impact on company performance. The primary aim of predictive analytics is to improve organisational profitability, while a positive impact on social mobility is a healthy by-product.
An example of predictive analytics at work
A recent study in the USA revealed that the dropout rate at university will lead to a shortage of qualified graduates in the market (3 million deficit in the short term, rising to 16 million by 2025). Predictive analytics was trialled to anticipate early signs of struggle among students and to reach out with additional coaching and support. As a result, within the state of Georgia student retention rates increased by 5% and the time needed to earn a degree decreased by almost half a semester. The programme ascertained that students from high-income families were ten times more likely to complete their course than those from low-income households, enabling preventative measures to be put in place to help students from socially deprived backgrounds to succeed.
What can be done to combat the biases that affect recruitment?
Bias and stereotyping are in-built physiological behaviours that help humans identify kinship and avoid dangerous circumstances. Such behaviours, however, cloud our judgement when it comes to recruitment decisions. More companies are shifting from a subjective recruitment process to a more objective process, which leads to decision making based on factual evidence. According to the CIPD, on average one-third of companies use assessment centres as a method to select an employee from their candidate pool. This no doubt helps to reduce subjectivity but does not eradicate it completely, as peer group bias can still be brought to bear on the outcome.
Two of the main biases which may be detrimental to hiring decisions are ‘Affinity bias’ and ‘Status Quo bias’. ‘Affinity bias’ leads to people recruiting those who are similar to themselves, while ‘Status Quo bias’ leads to recruitment decisions based on the likeness candidates have with previous hires. Recruiting on this basis may fail to match the selected person’s attributes with the requirements of the job.
Undoubtedly it is important to get along with those who will be joining the company. The key is to use data-driven modelling to narrow down the search in an objective manner before selecting based on compatibility. Predictive analytics can project how a person will fare by comparing candidate data with that of existing employees deemed to be h3 performers and relying on metrics that are devoid of the type of questioning that could lead to the discriminatory biases that inhibit social mobility.
“When it comes to making final decisions, the more data-driven recruiting managers can be, the better.”
Bias works on many levels of consciousness
‘Heuristic bias’ is another example of normal human behaviour that influences hiring decisions. Also known as ‘Confirmation bias’, it allows us to quickly make sense of a complex environment by drawing upon relevant known information to substantiate our reasoning. Since it is anchored on personal experience, it is by default arbitrary and can give rise to an incorrect assessment.
Other forms of bias include ‘Contrast bias’, when a candidate is compared with the previous one instead of comparing his or her individual skills and attributes to those required for the job. ‘Halo bias’ is when a recruiter sees one great thing about a candidate and allows that to sway opinion on everything else about that candidate. The opposite is ‘Horns bias’, where the recruiter sees one bad thing about a candidate and lets it cloud opinion on all their other attributes. Again, predictive analytics precludes all these forms of bias by sticking to the facts.
https://sapia.ai/blog/workplace-unconscious-bias/
Age is firmly on the agenda in the world of recruitment, yet it has been reported that over 50% of recruiters who record age in the hiring process do not employ people older than themselves. Disabled candidates are often discriminated against because recruiters cannot see past the disability. Even these fundamental stereotypes and biases can be avoided through data-driven analytics that cut to the core in matching attitudes, skills and personality to job requirements.
Once objective decisions have been made, companies need to have the confidence not to overturn them and revert to reliance on one-to-one interviews, which have low predictive power. The CIPD cautions against this and advocates a pure, data-driven approach: ‘When it comes to making final decisions, the more data-driven recruiting managers can be, the better’.
The government’s strategy for social mobility states that ‘tackling the opportunity deficit – creating an open, socially mobile society – is our guiding purpose’ but that ‘by definition, this is a long-term undertaking. There is no magic wand we can wave to see immediate effects.’ Being aware of bias is just the first step in minimising its negative effect in the hiring process. Algorithmic hiring is not the only solution but, if supported by the government and key trade bodies, it can go a long way towards remedying the inherent weakness in current recruitment practice. Once the UK’s leading businesses begin to witness the benefits of a genuinely diverse workforce in terms of increased productivity and profitability, predictive hiring will become a self-fulfilling prophecy.
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Predictive Analytics: Your resolution will fail, here’s a promise you can keep
When technology is used in the right way it can enhance and improve our lives
During this seasonal holiday a great many of us will start to create plans for the forthcoming New Year. We’ll think about events, occurrences and happenings of the year gone by and many will commit to doing things better next year.
Even though studies have shown that only 8% of people keep their New Year’s resolutions , we still make (and subsequently break) them. But the intention was there, so good work!
Have you ever stopped to think about the processes your brain undertakes to enable you to set your goals for the New Year? No? Well, luckily I’ve done that bit for you. To make that resolution you combined your current and historical personal data and produced a future outcome, factoring in the probability of success, based on your analysis. A form of predictive analytics, if you like!
Predictive Analytics.
Using historical data to predict tomorrow’s outcomes
Thinking about those things you did (and didn’t do) this year and predicting/projecting for next year.
So now you know what it involves and we are (loosely) agreed that you’re on board with predictive analytics, when better than to tell you now that 2016 is going to be the year when we really start to see the benefits of predictive analytics within our jobs and people functions at work.
I think it’s now universally accepted that when technology is used in the right way it can enhance and improve our lives across every sector and industry. Most fields have seen significant developments over the last 20-30 years as technology is increasingly used to further our understanding of the way things work, enabling us to make better decisions in areas such as medicine, sport, communication and, arguably, even dating (predictive analytics is used in all of those sectors by the way!) so why not use it to help us find the right people for the right organisations?
Did you know you no longer need a top-class honours degree to work at Google?
Every employee is put through their analytics process allowing the business to match the right person with the right team, giving each individual the best environment to allow their talent to flourish.
Companies such as E&Y and Deloitte are using different methods to tackle the same problem – removing conscious and subconscious bias attached to the name and/or perceived quality of the university where applicants studied.
Airlines, retail, BPOs, recruitment firms a growing number of businesses within these sectors are using or on-boarding predictive analytics to achieve upturns in profits, productivity and achieving a more diverse and happier workforce.
Predictive analytics helps us make people and talent decisions to positively influence tomorrow’s business performance without bias, so I guess the question is this – if it’s already a proven science to achieve results, why isn’t everyone doing it? How long until everyone starts to use, and see the benefits, of predictive analytics?
Which logically raises the question: what are the benefits?
- Time efficiencies – wouldn’t it be great for all parties in an interview to know that the data is indicating this role and person are a good fit before the candidate walks into the room? Hopefully reducing the reliance on both parties to be having a “good day”.
- Diversity, inclusion removing the optics so often associated with a role. No more stated, or implied, previous sales / retail / PR experience but instead you can attract people from as broad a spectrum as possible knowing the data will help identify those candidates who have the foundation for success within your business and could well be your next superstars!
- Churn/attrition – wouldn’t it be great to know that you can fill your 10 / 50 / 2000 seasonal/part-time roles from a pool of candidates who will have a higher chance of staying with the business longer, becoming successful brand ambassadors for your company leading to happier staff and customers alike.
- Unique to your business – wouldn’t it be fantastic to know that all of these predictions are tailored purely for your business? For example, knowing that a candidate not overachieving in their previous role at one of your competitors isn’t reflective of their potential and that you can take advantage of their previous training and knowledge because the data says they’re going to be a better performer within your business.
Data can be big and it can be daunting, but it can also be invaluable if you ask and frame the right questions and combine the answers with human knowledge and experience. You will be surprised by the insights, knowledge and benefits that your business can obtain from even the smallest amounts of data. Data you probably already collect, even if it’s unknowingly or unwittingly!
List of Recruiting Resolutions
So as you start rummaging through your brain trying to remember where you filed your finest seasonal outfit(s) (that might just be me!), start prepping for the new year budgets, or start writing your list of resolutions let me help you frame a few questions:
- Does your sector suffer from a skills shortage?
- Would your company like to know which candidates from another sector have a higher likelihood of success post-training?
- Would your business like to see an upturn in performance or people metrics such as increased sales, decreased absenteeism, longer tenure for better performers or a more diverse workforce? Would your Finance, Talent or HR head of department like to see an improvement in the variety of measures that indicate a better, more productive and happier workforce?
Statistically, your personal New Year’s resolution is unlikely to be on course in 12-months time so instead, why not make a resolution to bring predictive analytics into your talent processes in the upcoming year?
You’ll see the benefits for years to come, and that’s a promise we can both keep.
Happy holidays!