Any leader with P&L accountability knows that tracking margin (ie the difference between your revenue and what it cost you to earn that revenue) is pretty damn important to your economics. Margins of 60%+ for tech companies is what gives them insane valuations because the cost of adding the 10,000th customer is not much greater than adding the 1000th customer. If you believe that the economics and ROI of your talent business model are as important as your core business model, then you may find applying these two business metrics a useful lens to analyse the ROI of your talent business model.
Applying these metrics to your talent business model can help identify where to invest your HR budget to drive better ROI.
3 factors drive your CAC:
1. Direct recruitment costs i.e. how many recruiters do you have on the tools
2. The productivity and speed of your recruitment team (that is, it’s scaleability) i.e. the amount of candidates they screen in an hour, day, week, month
3. The layers of assessment in your recruitment and their costs i.e. are you doing CV screening, phone screening, video screens, 1:1 interviews, panel interviews, group assessments, coffee chats?
All of these layers of assessment, some with some science behind them, most with no science, add to your CAC. We analysed CAC for our customers comparing their ‘old’ recruitment process and the impact of using our AI to do their screening and assessment.
The results are stunning.
PHAI (PredictiveHire AI) can screen 100,000 applicants in around 6 hours, what it would take a team of 5 recruiters 476 working days to do. A massive 600 x faster. This is based on conservative assumptions like every recruiter screening 7 hours a day, CV screening of 10 minutes per applicant, and 10% of those CV screened with phone screens of 30 minutes in duration. Those speed differentials compound when the numbers grow because humans can’t scale but technology can.
You can see what the scale of impact is when you look at the cost and time differential for 1000 applicants and 100,000 applicants. The case for AI in recruitment is a no brainer for enterprise and government, and compelling even for smaller businesses with more modest volumes.
Today we’re pleased to announce that our AI Smart Interviewer is now available on the SAP® Store, the online marketplace for SAP and partner offerings. Sapia.ai integrates with SAP® SuccessFactors®, delivering access to the world’s only smart interview platform powered by deep learning AI to SAP customers.
Sapia.ai CEO Barb Hyman said the partnership enables seamless integration with the SAP SuccessFactors platform. “In enabling SAP SuccessFactors customers to access our world-leading AI-powered recruitment solution, we’re removing friction in bringing an AI-powered approach into their HR processes. Sapia.ai customers are reducing their time to hire by up to 83%, and by using the AI’s recommendations, are hiring candidates better suited to the role, reducing employee churn by up to 62%.”
Global brands trust Sapia.ai to accelerate and enhance their recruitment and promotion processes. A conversational, Natural Language Processing (NLP) based chat AI interviews, assesses and screens for the best talent at scale via an easy-to-use platform, generating insights for both candidates and hiring teams.
In addition to improving diversity outcomes by eliminating unconscious bias in candidate screening, it also allows companies to reallocate thousands of hours spent screening talent toward higher-value tasks.
SAP Store, found at store.sap.com, delivers a simplified and connected digital customer experience for finding, trying, buying, and renewing more than 2,300 solutions from SAP and its partners. There, customers can find the SAP solutions and SAP-validated solutions they need to grow their business. And for each purchase made through SAP Store, SAP will plant a tree.
Sapia.ai is a partner in the SAP PartnerEdge® program. The SAP PartnerEdge program provides the enablement tools, benefits and support to facilitate building high-quality, disruptive applications focused on specific business needs – quickly and cost-effectively.
Visit the Sapia.ai listing on the SAP Store here.
Remote work is not going away. The bonus of remote work becoming ‘a thing’ is it enables you to go further afield for talent. Broadening the candidate pool means you get even more diversity and can interview the world to find the best talent.
For fully remote businesses like Github, Automattic, with over 1000 remote workers spread across 75 countries, remote work is all about unleashing productivity.That comes from asynchronous work that needs asynchronous communication. Forcing people to do video meetings risks drowning out those team members who don’t thrive in a live group setting. The introverts. The deep quiet thinkers. The ones who prefer to reflect on an issue and not be forced into making a contribution because everyone else is on Zoom right now.
Make it the way you do things. The way you define a business problem, debate the key issues, and fast track from idea to execution.
Jeff Bezos cottoned on to this years ago. This new superpower, how you write, whether via text, Slack, Wiki or on Google docs also impacts your hiring processes. At what point do any of us test for written communication skills? If you want to hire people who can work autonomously, be productive and who can collaborate, you need to test their text communication.
What may not be known to many people, is that testing for all of this – written fluency, clarity of thought, can all be done via text analysis in the hiring process. Testing should not be just limited to the skill of writing, but also to the motivation behind expressing something in writing. This requires more effort and thinking than speaking it out. If someone is not motivated to express themselves in writing when a job is on the line, you can assume what it might be like once they are working in a role.
The power of Natural Language Processing (NLP) based machine learning models that can tell you all of this immediately is here today. From just 300 words, we can infer writing skills, personality traits and job-hopping motive. This really means there is no excuse for not hiring for the key skills required for remote work right now.
“Language is a mirror of mind in a deep and significant sense. It is a product of human intelligence. By studying the properties of natural languages, their structure, organisation, and use, we may hope to learn something about human nature; something significant, …” (Noam Chomsky, Reflections on Language, 1975)
In other news:
Check out the best job advertising platform you have never heard of: Jooble. Jooble is represented in 71 countries and available in 24 languages.
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.