Sapia (Formerly PredictiveHire) is a finalist in the Talent Tech Star Awards for excellence in Candidate Experience.

About the awards

“HR Tech is now a $400bn global industry that enables employers and recruiters to put the right people in the right jobs and perform better,” said Becky Wilson, Editor of TALiNT International magazine. “The Talent Tech Star Awards will highlight the valuable contribution of HR Tech to the UK economy in a campaign brought to life by interviews with finalists and panel of judges.”

“The TIARAs are distinguished by the rigour of its judging process and the quality of its judging panel,” said Alex Evans. “We assess the impact of Talent Tech solutions on clients, candidates and employees through 5 key metrics. These are excellence in delivery; innovation; sustainable value; business growth; and purpose.”

Sapia and Candidate Experience

Candidate interviews and assessment re-imagined! Through a smart chat interview of 5 free form questions, our AI uncovers soft skills, role-specific traits, and written communication skills of every applicant. This gives customers a bias-free ranked list of every applicant. We capture no sensitive information like gender, age and race.

Candidates love it – we have a 95% completion rate and a 99% satisfaction rating.

• A non-timed chat-based interview is fast and comfortable – no more intimidating, biased and unexplainable assessments
• Every candidate receives personalised feedback and coaching tips – no more black box recruitment!

Candidate Feedback:

This is amazing, the most I’ve ever got out of a job application when I’ve never expected anything. You guys are going above and beyond to support employers and employees. This feedback has greatly improved my application and interview skills and has given me new insights to aid me in future employment.

I found this tool very magical, if I can put it this way. It just has a way of showing the inside part of someone. The outcomes are exactly what I have been feeling about myself, what a junior friend told me not long ago.

Wow this evaluation hits the nail on the head. Each of these are a perfect description of who I am and my beliefs working in a team environment.

THE CANDIDATE EXPERIENCE SOLUTION OF THE YEAR

This Award recognises a solution that has best-enabled employers and / or agencies to improve candidate experience and success in talent acquisition.

The judging panel brings together expert perspectives from senior HR and Recruitment industry leaders, investors, and advisors. Together they make the TIARA Talent Tech Star Award a powerful and prestigious endorsement. The panel includes:

• Adam Hawkins – Head of Search & Staffing UK&I and EMEA at LinkedIn who works with recruiters and RPOs to make better use of their talent data and insight
• Simon Devonshire OBE – investor, NED and serial entrepreneur, co-founder of Wayra Europe and former Entrepreneur in Residence at BEIS who advises corporates on how to innovate and invest in talent
• Chris Gray – Sales and Marketing Director of ManpowerGroup UK and the Managing Director of ManpowerGroup Ireland who leads in the creation and delivery of innovative workforce solutions and services
• Umerah Akram – Head of London Stock Exchange Group’s ELITE, an ecosystem and platform for 1200 private companies from 42 countries to help them prepare and structure for their next stage of growth and investment
• Parveen Dhanda – Head of Programmes at Tech Nation and Programme Lead of the Future Fifty, which offers practical support to enable fifty of the most promising growth-stage digital businesses to reach their full potential in the UK
• Michael Rendell – Founder Partner at Nala Ventures & Investments LLP and former Partner and Head of Innovation and Transformation at PwC who led on embracing the impact of new talent technologies, including AI, machine learning, and elastic workforces

Are you interested in using an award-winning solution in your business to improve the candidate experience? Let’s chat

Sapia (Formerly PredictiveHire) was also named the Top 3 Best Conversational AI in HR Solution at CogX

Recruiters: The corporate hiring machine is evolving. Can you feel it?

As recently as a year ago, many top companies still selected candidates based on the most misleading of heuristics: The school they attended.

Harvard? Right this way! Community college? No thanks, we don’t take your kind around here.

This Pearson Hardman-style hiring strategy may have ‘worked’ in the past. Not any more, for two reasons: A) the talent isn’t out there, and B) everyday people expect a better standard of hiring fairness. They know that opportunity isn’t distributed equally, and that elite colleges are more a proxy for privilege than actual performance potential.

(Funny that it took a labor shortage to show companies that potential can come from anywhere. Psychologists and sociologists have known it, and have been saying it, for decades.)

Regardless, you’ve got LinkedIn CEO Ryan Roslansky telling Fortune that its company is favouring soft skills over college degrees, because such a practice creates a ‘much more efficient, equitable labor market, which then creates better opportunities for all’. He’s right about this approach. Even if you take away the benefits to diversity and inclusion, it makes sense purely mathematically: Now your hiring pool has increased from a few hundred thousand candidates to, at the very least, millions.

Soft skills are more useful, and fairer, than resumes

Resumes foster bias. Despite this fact, we insist on using them. Why? Because, until now, there hasn’t been a compelling reason not to. You could screen, interview, hire, and get warm bodies in seats with relative ease. Business could go on. Consequently, bias became a can that could be continually kicked down the road. Not anymore, for reasons discussed above.

The same is true for hire quality. Google ‘how to measure quality of hire’ and you’ll get a million different answers. Some advocate the for speed- or time-to-productivity approach; others say it’s about measuring ‘culture fit’. One or both of those might be true, but that’s beside the point: hire quality is nebulous not by its nature, but because the inputs (i.e. resumes) are messing with the outcomes.

This is why measuring soft skills is so important. Here’s an example:

We know that conscientiousness (that is, the propensity for someone work diligently and systematically on tasks) is a good predictor of on-the-job success. We also know that structured interviews are the best explainer (at 26%) of employee performance (versus previous job experience, which explains just 3%).

We might construct a valid candidate interviewing and vetting process based on these two facts alone. Fundamentally, we know that if A) we look for conscientiousness, and B) we do it in a structured, fair, repeatable way, we’ll get good candidates. Hire quality will take care of itself. Good inputs, good outcomes. Voila.

(It’s not quite that simple, but you get the point: There are reliable, proven ways to ensure validity, and the two examples cited above are very real and useable.)

Instead we rely on unstructured interviews, unruly hiring managers, and resumes – none of which can determine how hard-working a candidate is. Bad inputs that create bad outcomes. Consequently, we regularly examine hire quality and wonder why we struggle to measure it, or worse, connect it to the wider financial outcomes of our business.

So how do you measure soft skills?

Let’s keep this as simple as possible.

1. Use structured interviews. Aside from the long-standing research above, an Aptitude Research report from this year found that structured interviews are 52% more likely to result in good hire quality.
2. Ask questions based on known personality traits and behaviors, because these reliably point to high job performance. You want to know things like conscientiousness, empathy, emotionality, and so on.

Our free job interview rubric contains more than 20 questions designed by our psychologists to help you uncover hire quality. Get it here, use it, and let us know how you found it.

Your companion in accurate measurement of soft skills

There are millions of ways to assess for soft skills in interviews, just as there are millions of ways to calculate quality of hire. You may get some success by going it alone, but humans are, historically speaking, terrible at accurately assessing personality traits (and therefore, hire quality).

Our Ai Smart Interviewer does this very thing. Using deep, always-evolving personality science, our platform interviews and assesses candidates for desirable soft skills and behaviors, and even matches the resultant talent profiles to your company values.

Of course, the benefit is that hire quality is achieved and proven for you – you don’t have to worry about biased interviewers, bad questions, enforcing consistent processes, and the other headaches of recruitment. With that time back, you can focus on your people.

Or, think about it this way: LinkedIn is getting really smart with its hiring. Other companies like Apple, Delta, and IBM are too. Will you be left behind?

AI used to inform decisions about people

I work with a team building a product-driven by AI which is used to inform decisions about people. This means I am often approached on social media or in-person by people who have a point of view about that, often with fear or frustration about being picked (or rejected) by a machine.

This week I received an email from a commerce/law graduate who had recently applied for a role at one of the big ‘accounting’ professional services firms. This student, let’s call him Dan, had to complete an online game in order to qualify for the next step which was a video interview.

To give himself the maximum chance of ‘doing well’ in the game, Dan created a dummy profile ‘Jason’ to see what the experience was like and get an inside read of the questions so that when he did it for real he would really nail it. This first time round he fudged the test as it was a trial run and he left most answers blank. When Dan went and did this for real, he was conscientious of course and wrote thoughtful answers and tried to pick the right behaviour in the balloon popping game!

Jason, who scored 44% received a video interview. Jason does not exist.

Dan, who scored 75% did not progress to the next round.

The machine picked the wrong guy

Let me be upfront and say that we too use machines to help identify the best-fit applicants for roles.

Every business like ours that works in this space recognises that this is new technology, and so still very much in the early stages of development. Like humans, machines will make mistakes. In our business, we call them false positives (people recommended who just aren’t right) or false negatives (people who are missed by the machine who could be right for the role).

Dan’s questions are legitimate…

• -how can Jason who scored lower get through the process?
• -how can Jason who fudged it and left most of the questions blank, get through?
• -how does the number of balloons you popped, or let pop, dictate whether you are going to be a good hire?

When you are rejected by humans, either you hear nothing or you may get an explanation like — ‘you aren’t a good culture fit’ when they reject you. Machines may give you a score.

For me what this reveals is that any business who uses AI and ML for candidate selection, it’s critical to have empathy for the person who is experiencing this, in this case, empathy for the candidate experience.

• What does it feel like to be judged based on how well you play an online game? Is that a human experience? How gameable is it? Is it fair to all, given that some people have grown up playing video games and others haven’t?
• How do you give feedback in a way that’s human and helpful and ideally developmental? The feedback given here was to say you didn’t make the % threshold.

An individual’s traits, strengths and weaknesses can be predicted.

Machines can make better selection decisions about people because they have access to a larger more comprehensive set of data, can process data faster, and if built with the right objective data, they can be far less bias than humans.

When used in recruitment, they need to work for both parties — the organisation and the candidate. Building trust in these technologies is critical in our space. It can’t all be about the organisation getting their efficiency gains.

This means :

• Making these experiences reflective of human experiences — it should feel like an interview not like a game.
• Giving the candidate something back, by way of qualitative feedback rather than making you feel like a number or a percentage on a distribution chart.

Recruitment wants to rise above being a process. So AI in recruitment should enable that if it’s to be trusted by candidates.

Suggested Reading:

https://sapia.ai/blog/culture-surveys-no-use/