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.
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.
‘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.
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.
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.
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.
‘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 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
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.
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.
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.
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
If done effectively, interviews are a great means of assessing a candidate. We trust them to enable us to determine if our candidates have the attributes, traits, behaviours, skills, experience and personality to meet the role requirements.
Here’s the problem. It is physically impossible to interview every candidate. So, we rely on CV screening as the first step. A recruiter on average spends six seconds looking at the resume. In six seconds, a snap judgement is made on shortcuts (biases).
At the starting block, the process has failed. You cannot possibly pick qualities like grit and initiative from a CV, right? Then, of the people who applied for the job, around 13% of applicants may get an interview. During C-19 times – you can more than half that number.
In this way, you realise the value of interviews without investing one-minute of your time in them.
Imagine this. Everyone has already been interviewed before you have read one CV. A pre-qualified, pre-assessed, high-quality shortlist before you have read ONE CV. That’s the dream! Because now you are not wasting time reading resumes of people who either can’t do the job, won’t do the job, or they just don’t fit. And, instead of flicking through 100 resumes for a puny 6 seconds each, you can take the space to consider the best. The best? Those candidates who have already been pre-selected for that grit and initiative you so badly want in your team.
You can try out Sapia’s FirstInterview experience here.
Time to hire measures recruiting efficiency. It is the number of days between the first contact with a candidate to the day the candidate accepts the offer. Screening is your first time-to-hire bottleneck.
Even if you’re using an ATS you may be able to easily rank resumes, but you still have to consider them. And there’s your block.
A new generation of interview automation is here so that you can have every candidate interviewed in a flash. Of course, it integrates and works seamlessly within your ATS. It saves recruiters from screening resumes and boosts the efficiency of your recruiting process.
Reducing time to hire is great for candidates who get the job faster (or can move onto the next job). It is terrific for recruiters who get the reward of quicker placements and attaining their metrics. It is a relief for hiring managers who get their team to a full complement and can get back to their actual job.
Interviewing automation makes your recruiting process much faster – usually around 90% faster.
Hiring managers want their best team. They want people who can do the job, who will do the job and who will perform. With interview automation, Ai assesses traits, communication skills, optimism and temperament prior to you getting involved.
As a Recruiter, you get a complete picture of a candidate beyond what is written on their CV. You learn a lot of information about the candidate. Ai will rank and grade all your candidates for you. It pre-qualifies those who are a fit to move forward.
Have you ever thought to yourself: “If only I could hire 10 more Julie’s!” (*insert name)? With Ai, you can. And, as far as quality goes, this is the distinction from all other forms of pre-employment.
AI learns what a successful hire looks like and pin-points more like them. AI bases this learning on your historical recruiting decisions and then applies that knowledge to new candidates to automatically screen, grade, and rank them.
Interviewing automation gets you to the best of your talent pool much quicker resulting in, on aggregate, much better quality in your hires.
Diversity and Inclusion have been on the HR agenda for a long time. And in more recent years, it’s made its way onto the Business agenda too. In 2020, global management consulting company McKinsey again confirmed that companies with both ethnic and cultural diversity and gender diversity in corporate leadership are outperforming non-diverse companies on profitability. They found: “The most diverse companies are now more likely than ever to outperform non-diverse companies on profitability”
Diversity improves employee productivity, retention and happiness. Settled then. We want businesses that are diverse and fair.
Here’s the King of Recruiter biases: The Dunning-Kruger Effect. It’s where we lack the self-awareness to accurately assess our own skills meaning that we overestimate our ability. You think you are a brilliant totally unbiased Recruiter, right? You may well be, but it’s not uncommon to think you’re smarter or better than the average person. Haven’t we all skipped over candidates who don’t have the requisite ‘Big 4’ employer on their resume, or the ‘right kind of degree’?
Even when we don’t mean to be, human bias is pervasive. We keep these biases alive, through our relentless refusal to admit our shortfalls. And unfortunately, this isn’t great when it comes to hiring for diversity.
The reason for this is you can test, adjust and get rid of biases. The good news is Ai doesn’t resist stubbornly while claiming absolute fairness and denying any bias. This means that undesirable machine learning biases will tend to decrease over time. In Sapia’s case, its blind screening at its best. Nothing that typically influences human bias is introduced into the algorithms – no CV’s, no socials, no videos, no facial recognition – it’s just the candidate and their text answers. Much fairer for candidates of course and a richer experience where they can just be themselves.
Interviewing automation makes your recruiting process much fairer and your hiring decisions far more diverse.
Your ability to hire cost-effectively will be hampered if you don’t have the right tools. Make sure that all your recruitment technology is pulling in the same direction – to make hiring as seamless, streamlined and stress-free as possible – rather than working against you. The money you invest in the right technology will soon pay off when it comes to time and efficiency savings.
Significant costs are borne by an organisation when an employee voluntarily leaves.
These include replacement costs such as costs associated with advertising, screening and selecting a new candidate. A study conducted by the Australian HR Institute in (AHRI) 2018 across all major industry sectors in Australia (Begley & Dunne, 2018) found that on average companies face an annual turnover rate of 18%. Within the age group of 18 to 35 it worsens significantly, at 37%. That is, more than 1 in 3 people in the youngest age group leave an organisation within a year.
Imagine if you could predict those with a likelihood of churning before you had met them? Then think about the enormous savings that would be derived across your organization if you could do so.
If you haven’t yet automated your interviews, you are spending too much on hiring.
Chances are that reading CV’s and running interviews are not the hardest part of your job but are the most time-consuming. What if you could have available time for those high-value tasks. Like managing your stakeholders. Getting to know the business better. Improving your business partnership skills. Learning the essence of what Hiring Managers actually want. Networking and improving talent pools, particularly for those hard-to-fill roles.
So, if interview automation can take care of all of your first interviews for you then ask yourself:
Of how much value am I when buried knee-deep in screening? Visualise less of that and more of the buzz you get when you find the perfect fit. There’s no better feeling than knowing you’ve helped someone further their career AND helped your Hiring Manager find someone who ‘just fits’ and will perform. Nothing can replace the collaboration and empathy that you as a live person can extend.
According to this Sapia research paper published by IEEE: Structured interviews (where the same questions are asked from every candidate, in a controlled conversation flow and evaluated using a well-defined rubric) have not only shown to reduce bias but also increase the ability to predict future job performance. With interview automation, the questions asked in a structured interview are derived using a job analysis as opposed to interviewer preference and are typically based on past behaviour and situational judgement.
Interviewing automation frees up recruiter’s time to perform higher-value tasks with far greater output.
With interview automation you can move from an elongated process that leaves candidates in the dark, not knowing where they stand, to a super-efficient experience that feels empowering.
According to the Society for Human Resource Management (SHRM), 82% of candidates report the ideal recruiter interaction is a mix of innovative technology and personal, human interaction.
Improving your candidate experience is so much easier by adopting technology that is inclusive, personalised and relatable. Sapia’s interview automation offers a mobile-first, chat interview that interviews everyone in-depth and at scale. Giving every candidate personalised feedback.
Here is what interview automation offers above a manual interview process for candidates:
Interviewing automation enhances candidate experience, with no further time investment from you.
Download the 2020 Candidate Experience Playbook here
Gartner predicts by 2021, 50% of enterprises will spend greater budget on chatbot creation and bots than traditional mobile app development.
Businesses are adopting Sapia’s chat interviews across various job families – especially in front-line customer service roles. The quickest payback you will get on an investment in interview automation is to apply it to your high-volume roles first. Interview automation can truly enhance your high-volume recruitment process and help you make it more efficient (and pleasant) for everyone involved. This will help you get your time-back really quickly and release the budget for automation in other areas of recruiting.
The future of all first interactions between candidates and your business will be through automation. The only decision, for now, is where you will adopt interview automation first.
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You can try out Sapia’s FirstInterview right now, or leave us your details here to get a personalised demo.
If there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.
Interested in a demo of our Lever integration? Fill out the form below!
Like Sapia, the team at Lever like to make life easy for recruiters. Lever streamline the hiring experience, helping recruiters source, engage, and hire from a single platform. Now you can supercharge your Lever ATS by seamlessly integrating interview automation from Sapia. Integrating is easy, and secures fairer, faster, and better hiring results. In the war for talent, you’ll pull ahead of your competitors even faster with Sapia + Lever.
There’s a lot expected of recruiters these days. Attracting candidates from diverse backgrounds and delivering exceptional candidate care whilst selecting from thousands of candidates isn’t easy.
Recruiters are expected to:
A lot is expected from recruiters, from screening thousands of applicants to attracting candidates of diverse backgrounds and delivering a great candidate experience. But technology has advanced a lot and can now better support recruiters.
The great news is that when you integrate Sapia artificial intelligence technology with the powerful Lever ATS, you will have a faster, fairer and more efficient recruitment process that candidates love.
You can now:
Gone are the days of screening CVs, followed by phone screens to find the best talent. The number of people applying for each job has grown 5-10 times in size recently. Reading each CV is simply no longer an option. In any case, the attributes that are markers of a high performer often aren’t in CVs and the risk of increasing bias is high.
You can now streamline your Lever process by integrating Sapia interview automation with Lever.
By sending out one simple interview link, you nail speed, quality and candidate experience in one hit.
Sapia’s award-winning chat Ai is available to all Lever users. You can automate interviewing, screening, ranking, and more, with a minimum of effort! Save time, reduce bias and deliver an outstanding candidate experience.
As unemployment rates rise, it’s more important than ever to show empathy for candidates and add value when we can. Using Sapia, every single candidate gets a FirstInterview through an engaging text experience on their mobile device, whenever it suits them. Every candidate receives personalized insights, with helpful coaching tips that candidates love.
Test drive it for yourself here (it takes 2 minutes!)
Recruiters love that Sapia TalentInsights surface in Lever as soon as each candidate finishes their interview.
Well-intentioned organizations have been trying to shift the needle on the bias that impacts diversity and inclusion for many years, without significant results.
Let’s chat about getting you started – book a time here ⏰