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Written by Nathan Hewitt

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!


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Lever + Sapia = Faster, fairer hiring

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. 

Hiring is more complex than ever

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:

  • Find the right people, ensuring a diversity of candidates
  • Fill roles quickly and efficiently
  • Interrupt bias in hiring and promotion
  • Ensure every person hired amplifies the organisation’s values
  • Create a candidate experience that is engaging and rewarding

Simplify Lever ATS hiring

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:

  • Reduce your screening time by up to 90%
  • Increase your candidate satisfaction to 99%
  • Achieve interview completion rates over 90% and
  • Reduce screening bias for good!

Sapia + Lever

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.

We’ve created a quick, easy and fair hiring process that candidates love.

  1. Create a vacancy in Lever, and a Sapia interview link will be created. 
  2. Candidates receive the link. Every candidate will have an opportunity to complete a FirstInterview via chat, automatically sent to them after completing their application in Lever.
  3. Candidates receive feedback. Every candidate will automatically receive a personalised personality profile and coaching tip after completing their first interview! No more candidate ghosting.
  4. See results as soon as candidates complete their interview. Each candidate’s scores, rank, personality assessment, role-based traits and communication skills are available as soon as they complete the interview inside Lever. 

By sending out one simple interview link, you nail speed, quality and candidate experience in one hit.

Integrate Lever and get ahead.

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.

Experience a Sapia FirstInterview for yourself

 

The interview that all candidates love.

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.

Together, Sapia and Lever deliver an approach that is: 

  • Relevant Move beyond the CV to the attributes that matter most to you: grit, curiosity, accountability, critical thinking, agility, and communication skills
  • Respectful Give every single person an interview and never ghost a candidate again
  • Dignified Show you value people’s time by providing every single applicant with personal feedback
  • Fair Avoid video in the first round of interviews and take an approach that’s 100% blind to gender, age, ethnicity, and other irrelevant attributes
  • Familiar Text chat interviewing is not only highly efficient, but it’s also familiar to people of all ages  

There are thousands of comments just like this…

  • “I have never had an interview like this in my life and it was really good to be able to speak without fear of judgment and have the freedom to do so.”
  • “The feedback is also great. This is a great way to interview people as it helps an individual to be themselves.”
  • “The response back is written with a good sense of understanding and compassion.”
  • “I don’t know if it is a human or a robot answering me, but if it is a robot then technology is quite amazing.”

Test drive it for yourself here (it takes 2 minutes!)  

Recruiters love using artificial intelligence in hiring.

Recruiters love that Sapia TalentInsights surface in Lever as soon as each candidate finishes their interview.

Together, Sapia and Lever deliver an approach that is: 

  • Fast Ai-powered scores and rankings make shortlisting candidates quicker
  • Insightful Deep dive into the unique personality and other traits of each candidate 
  • Fair Candidates are scored and ranked on their responses. The system is blind to other attributes and regularly checked for bias.
  • Streamlined Our stand-alone LiveInterview mobile app makes arranging assessment centres easy. Automated record-keeping reduces paperwork and ensures everyone is fairly assessed.
  • Time-saving Automating the first interview screening process and second-round scheduling delivers 90% time savings against a standard recruiting process.

HR Directors and CHROs love reliable bias tracking.

Well-intentioned organizations have been trying to shift the needle on the bias that impacts diversity and inclusion for many years, without significant results.

Together, Sapia and Lever deliver an approach that is: 

  • Measurable DiscoverInsights, our operations dashboard that provides clear reporting on recruitment, including pipeline shortlisting, candidate experience and bias tracking.
  • Competitive The Sapia and Lever experience is loved by candidates, ensuring you’ll attract the best candidates, and hire faster than competitors.
  • Scalable Whether you’re hiring one hundred people, or one thousand, you can hire the best person for the job, on time, every time.
  • Best-in-class Sapia easily integrates with Lever to provide you with a best-in-class AI-enabled HRTech stack. 

Getting started is easy!

Let’s chat about getting you started – book a time here

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Ai is only as good as the experts behind it

Ai ethics in recruitment | Sapia Ai recruitment software
There has been some negative media attention lately surrounding the use of Artificial Intelligence (AI) in the recruitment space with warnings ranging from the fact that AI produces a shallow candidate pool to more serious things like amplification of bias.
There are many instances of AI being used in a way that has harmful outcomes, but it is important to clarify that this is about how AI is being implemented and not an issue with the use of AI itself.

When AI is used appropriately, responsibly, and following regulatory guidelines it is an incredibly powerful tool that can create fair outcomes for candidates who are selected without bias – in a way that no other tool at our disposal can.
This is why we think it’s worthwhile that more people better understand AI and some of the differences in the way it is used and implemented.

There is no unified Ai

Most media articles refer to AI as if it represents a singular master algorithm and fail to identify how varied the implementations of it are. Almost all AI we have today falls into the category of “narrow AI”, in other words algorithms, mostly machine learning, built to solve a specific problem. E.g. classify sentiment, detect spam, label images, parse resumes. These purpose built AI are highly dependent on the nature of the underlying training data and the expertise of the developers in making the right assumptions and tests of validity of their models. When built in the right way and used responsibly, AI has the ability to empower humans. This is why at Sapia.ai we have made various conscious design choices and adhered to a framework called FAIR™ that tests for bias, validity, explainability and inclusivity of our AI based tools.

Text over video

The biggest cause for alarm is when AI is applied to analysing video, which can lead to irrelevant inputs like clothing, background, and lighting being used as predictors of personality and job-fit. Video and speech patterns also make it nearly impossible to remove demographic information like race and gender as inputs.

Additionally, analyzing facial expressions is problematic, especially when evaluating certain candidates like those with Autism Spectrum Disorder or other forms of neurodiversity.

This is why Sapia.ai does not, and will not, use AI scoring for video interviews or even voice transcriptions from videos or audio given the word error rate introduced in transcribing speech. Instead, we opt for text – which we implement in a friendly no pressure environment that feels like you are texting a friend.

It’s worth noting that no data other than the answers given by the candidate are used in the ‘fit score’ calculation – that is, we never use demographic data, social media, CV or resume data (which also contain demographic signals, even when de-identified), or behavioral metrics such as time to complete.

Even a candidate’s raw text itself contains gender and ethnicity signals that can introduce bias, if not mitigated. This is why we only use feature scores (e.g., personality, behavioral competencies, and communication skills) derived according to a clearly defined rubric in our scoring algorithms, which our extensive research shows contain significantly less gender and ethnicity information than raw text.

Aim to uncover hidden talent while measuring potential

Another common concern is that AI will result in more uniformity rather than diversity in the workforce as algorithms narrow the pool in order to search out an employer’s ideal candidate. There are several things worth noting here.

First, identifying what the ideal candidate is – that is, what knowledge, skills, abilities, and other characteristics are important for success in the role – is what a job analysis is for and should, legally, be what your selection tool is designed to measure.

This is also not specific to AI, as all selection systems are designed to identify which candidates have a profile of traits and characteristics that indicate they will likely be successful in the role. This doesn’t automatically mean that every hire is going to be exactly the same, though. When you focus on the traits and characteristics that will set someone up to be successful, considering potential more than background or pedigree, you’re more likely to uncover hidden talent and hire more successful people from a broader, more diverse range.

Relying solely on past data to build your model also runs the risk of introducing historical data biases. This is actually why it is so important to consider the ideal candidate profile and use that to inform your scoring model. We strongly believe in keeping the human in the loop, which is why our scoring models are centred around the human-determined (via job analysis) ideal candidate profile and then optimized to ensure all bias constraints (e.g., 4/5ths rule and effect sizes) are met.

Using this approach, Sapia has helped clients achieve their DEI goals and increase their diversity hires, including impressive statistics like hiring 3x more ethnic minorities, 1.5x more women, and 2x more LGBTQ+ candidates in just 3 months.

Keep Ai processes transparent

Lastly, it’s worth acknowledging that there is often a “black box” mystery of how AI recruitment tools work. People don’t trust what they don’t understand. While we don’t expect everyone to be an expert in AI or Natural Language Processing, we do strongly believe in building trust through transparency and work hard to make sure that our models are easily understood and open to scrutiny. From third-party audits to detailed model cards to in-depth dashboarding and reporting, we aim to maximize transparency, explainability, and fairness.

We believe a fairer future can only be achieved when AI is used responsibly. AI is not the enemy, rather it’s the experience and motivation behind those promoting it that can make the difference between what is good AI and what is harmful AI.

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Hiring automation: It’s Time To Use Your Words

This Recruiter Says How You Write and What You Write Can Tell an Employer a Great Deal About Your Fit.

Barbara Hyman believes the most important skill for people looking for a job in the post-COVID world will be the ability to write.

“People who think clearly, write clearly,’’ says the chief executive of the artificial intelligence-powered recruiting firm Sapia, which judges its candidates on the most basic of skills.

The firm, which has big-name backers including Myer family member Rupert Myer, former Aconex founder turned venture capitalist Leigh Jasper, fund manager Dion Hershan and former JB Were partner Sam Brougham, gives every job candidate a first interview by asking them five text-based behavioural questions on their phone that take around 20 minutes to answer.

Then the company’s predictive models assign a “suitability” score to each candidate using over 80 features extracted from their responses and the system specifically precludes the use of names, gender and age to determine the recommended shortlist, removing unconscious bias from the recruitment process.

 


The company’s technology has already been used by 400,000 candidates looking for roles in retail, healthcare, customer service, hospitality, contact centres and graduate and trainee roles across 34 countries.

Its clients include Qantas, Bunnings, the Iceland Group grocery chain in the UK and Spanish telecoms giant Telefonica.


But Hyman says her biggest target client in the post-COVID world is government.

She believes the economy can only be sustainably reactivated through large-scale job security and that requires redeploying existing skillsets to meet in-demand industries.

“This requires a sophisticated and scaleable solution to find jobs for those whose industries have been decimated by the pandemic and have no jobs to return to. Our solution can immediately activate these job seekers into the new economy, steering them to the jobs they will be good at, she says.

She claims if the government activated this sort of technology for a range of growth industries the economic and social impact would be unprecedented.

“In a healthy economy, the cost benefit in Australia alone is $1bn net benefit (cost) for every 100,000 workers that get back to work one month earlier through reduced welfare payments and increased consumer spending. That is significantly higher when accounting for government subsidies as a result of COVID,” she says.

“A big part of getting back to work is the confidence and the mindset. We are exploring different avenues to allow people to use our chat bot to find their true role in the new economy. This is the vision we are trying to sell to government – you have your own personalised career coach that helps you find the ideal role.”

Hyman said one of the company’s big-name backers Rupert Myer, the chair of the Australia Council for the Arts and an emeritus trustee of The National Gallery of Victoria, had given her “amazing introductions” into the government and university sectors.

“When I came into the business in February 2018 it was running out of money. I had to get a bunch of the existing investors to support me,’’ says Hyman, a former chief human resources officer at REA Group and a human resources and marketing director at Boston Consulting.

Her data science leader at Sapia is Sri Lankan-born Buddhi Jayatilleke, who has a diverse background in machine learning, software engineering and academic research.

The firm has raised $4m in the past 2 years, including bringing in Australian global recruitment and talent management firm Hudson as a strategic investor last year.

“That gave us credibility because the number two recruitment firm in the market believes in what we are doing,’’ Hyman says.

“Whether you like it or not, there is enormous amount we can learn about you in 200 words. Just the very fact we don’t use any secret or behavioural data, you have to build trust from the beginning with your candidate. The completion rates are 95 per cent, the engagement rates are 99 per cent. But the key point is when we give you back your feedback. It is effectively a public service we are performing with this feedback.”

One of the firm’s initial backers was Rampersand, the venture capital firm which has a focus on early growth stage tech businesses.

Rampersand co-founder Paul Naphtali says the firm invested in Sapia for its ability to put data at the centre of a company’s people strategy.

“It’s a massive challenge for a start-up to aggregate the data and build the algorithms that can identify an individual’s suitability to a role quickly and accurately. It was a bold and ambitious plan from the beginning, and Sapia is now well on its way to becoming that data-centric engine,’’ he says.

“The company started with working to turbocharge the recruitment process by quickly identifying the right talent for the right roles.

“It’s taken time to build the tech and the data sets, but it’s paying off as a number of Australia’s leading companies now have Sapia as a default part of the process.”

He says the firm is now entering a new phase “where it also powers internal people management as well as for job seekers, which is obviously very relevant in the current environment”.

Recently in London Sapia was awarded the TIARA Talent Tech Star which honours the businesses globally in the talent acquisition industry.

Source: DAMON KITNEY, The Australian, October 30, 2020

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