Decentralized recruitment, while enabling larger companies to hire efficiently, suffers in a labor-short market.
Under ordinary circumstances – like, say, the world before COVID and the Great Resignation – it’s ideal to let local hiring managers build their own workforces. Generally speaking, the decentralized approach is better for productivity, candidate experience, and the overall satisfaction of hiring managers, who look favourably on the trust and autonomy they get from head office.
However, when good candidates are hard to come by, the dearth of talent puts stress on the joints of such a sprawling network. We hear this frequently from companies who come to us to help improve efficiency, diversity, and quality of hire.
These challenges (and others) have effected a drop in confidence in the way companies interview and process candidates. An Aptitude Research and Sapia.ai report from earlier this year found that 33% of companies aren’t confident in the way they interview, and 50% have lost talent due to poor processes. Meanwhile, 22% of the average talent pool is drained at the application stage.
Statistically speaking, roughly one in five people, at minimum, are bailing out of your application process at the very beginning.
As with many things in business, the answer to alleviating organizational pain lies in small, iterative improvements. Our recommendations do not include haphazard technological upgrades, nor do we advocate for widespread process changes. These will more than likely cause your decentralized hiring network to fall apart.
Here are some good places to start.
This is particularly important for the retail and hospitality industries, but certainly applies to any companies that hire entry-level team members at volume. Given the average level of job experience at this level of employment, most resumes and cover letters aren’t useful in gauging candidate quality. On the contrary – they take up precious hiring manager hours, are cumbersome for candidates to write, and are the main cause of the 22-24% candidate drop out rate we mentioned above. That’s not even accounting for the fact that anywhere between 60-80% of resumes contain falsifications.
Decentralization, almost by definition, makes capturing useful information difficult. But if you use an ATS as a tool for centralization, consider adding a candidate NPS measurement step to your application process. It can be as simple as a Net Promoter Score scale (1 to 10). If you hire at volume across multiple localities or regions, asking this one simple question can help you produce meaningful insights about how candidates find your process. What gets measured, gets managed, and though there are many other data points you might want to collect, this is a good (and relatively easy) place to start. If you’re keen to learn more about this, check out our podcast episode on candidate experience with Lars van Wieren, CEO at Starred.
Quantitative data is gold, but qualitative data is platinum. Make a habit of interviewing (not surveying, interviewing) your hiring managers on the ground. You’ll uncover invaluable insights that may enable you to make fast changes at scale. We help our clients collect qualitative feedback from hiring managers as a matter of course, leading to increases in productivity and hiring manager satisfaction.
Here are some useful questions to ask your hiring managers:
This kind of bottom-up research aims to understand how hiring managers are actually behaving and interacting with systems. Some may be breaking from established protocols, but if you ask them why and how, you might uncover tactics and efficiencies that can be brought back to the rest of the organization, thereby improving the way all hiring managers operate. Two adages apply here: ‘Necessity is the mother of invention’, and ‘People will always find the path of least resistance’.
This fact-finding method is better than surveys because surveys impose a limited scope in which potential problem areas are preset. “We’re asking you about these things,” you’re saying, “and therefore, we’re suggesting they’re most important.” As a result, other problems and possible solutions are likely to be excluded from discovery. You’ll always learn more by having real conversations, because they can go in any conceivable direction.
Again, incredibly useful for retail, but applicable in a wide range of industries and contexts. Think about the universal touchpoints you have with customers (a.k.a candidates) across your decentralized network. In retail, some good examples might be your receipts and carry bags. These provide you invaluable real estate to advertise your jobs and employer brand. Consider putting a URL or QR code on these assets, and you might drastically increase the amount of people who know about and apply for the jobs you advertise. This tactic has the added benefit of capitalizing on active and loyal customers; after all, if they’re buying from you, they’re a prime target for recruitment marketing.
The best part about this manner of advertising? You already own the space, and the design can be centralized and rolled out at scale.
We’d be remiss if we didn’t point out that Sapia’s Ai Smart Interviewer is a dynamite solution for the inevitable pain points of decentralised recruitment. Our technology can be rolled out across your entire company, and takes care of the application, screening, interviewing, and assessment stages of your process.
Hiring managers save time – as much as 1,600 hours per month, for some of our customers – but they still get the option to approve and interact with short-listed candidates. Better still, our platform captures vital data on diversity and candidate experience, enabling you to see exactly how your network is performing, individually and collectively.
Best of all, Sapia tech integrates directly with the leading ATS platforms, and can be rolled out in as little as four weeks.
Woolworths Group, Australia’s largest private employer, uses Sapia to hire more than 50,000 candidates per year, nationwide. To see how they flourish in a labor-short market, check out our case study here.
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.
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.
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
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 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:
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.
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.
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.
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.
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.”
‘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.
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.
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!
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?
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!
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:
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.