Written by Nathan Hewitt

How to assess, choose and use the best talent assessment tools

In recent years, a flood of pre-employment talent assessment tools has come into the market.

From automating initial candidate interviews to conducting online skills or personality testing, these tools help recruiters look beyond the CV to find the best candidates for every job.

In today’s competitive world of work, recruiters and hiring managers want to be sure that every decision is the right decision. As competition between companies for the very best talent has increased and as more candidates apply for fewer roles, just filling a role is no longer an option.  Reviewing CVs and assessing candidates is time-consuming and costly, and recruiters need to be confident that they are delivering value to their clients in both costs and the quality of candidates.

That’s why recruiters and employers alike are seeking ways to take the guesswork out of the process in identifying talent who will be the best fit for the team, work most productively and stay in the role longer. 

In this guide, Sapia explores the types of tools available, the insights they can provide and how they can benefit your business. We’ll also provide some guidelines for helping you to assess which tools could deliver the best return on your investment.

Why use talent assessment tools?

Pre-employment assessment of candidates is, of course, the very reason that recruiters exist.

Talent assessment tools have been developed to help make that process easier, faster and more cost-effective. The tools leverage technology to more accurately identify the best talent for a role and predict their fit and performance in that organisation.   

The benefits of candidate evaluation software can include:

  • Go beyond the CV – Leverage technology to focus on skills as well as things such as cultural fit, aptitude, cognitive abilities and more to identify candidates “most likely” to be successful.
  • Increase productivity – Spend less time on manual screening and more time on higher-value briefs and candidates.
  • Decrease costs – Automated processes can reduce the costs of manual talent review and assessment, dramatically reducing overall hiring costs. Redirect savings to investments in people or technology.
  • Remove bias from the process – Data-driven tools can take unconscious bias out of the assessment equation to focus on skills and fit. Sapia’s Ai-enabled text interview automation platform, for example, offers blind screening at its best to help build workplace diversity. 
  • Build deeper talent pools – Use technology to extend your reach to more candidates. The best tools integrate with your applicant tracking system to seamlessly run hiring processes and build ‘ready to go’ talent capabilities.
  • Fill roles faster – With the ability to screen more candidates in less time you can confidently begin interviewing sooner.
  • Be successful – With the right talent assessment tools working for your business, you are more likely to achieve the best talent outcomes every time.
  • Improve the candidate experience – Provide an engaging and enjoyable experience for candidates.  Some tools including Sapia’s platform automate personalised feedback that is always appreciated by candidates.
  • Know what candidates want – Using data to understand candidates, their motivations and their expectations can help managers be better prepared for onboarding. Building profiles of successful candidates will also provide insight for the next similar brief.
  • Make hiring decisions with confidence – Objective evidence and data-driven findings can help make every decision a better decision.

Types of talent assessment tools

The wide range of available talent assessment tools can be generally grouped into three areas of assessment: Work behaviours; Knowledge, skills and experience; Innate abilities and attributes. 

Some tools may focus on a single attribute such as coding abilities or English competency while others can combine a range of tests and interview capabilities within one platform.

Once the requirements of a role are understood, the right tools can be chosen to assess those competencies.

1. Learnt knowledge, skills and experience assessments look at candidates’ specific job knowledge, qualifications and work experience. Assessed against the agreed capabilities required for the role, these assessments can be an extremely accurate and effective predictor of a candidate’s performance in the role. Some tools may focus on specific sectors and roles  – eg sales, HR, health, hospitality, programming, engineering – while other platforms will cover a range of these with tests that can be customised to specific requirements

Some examples:

  • Job knowledge assessments: This type of test measures specific areas of knowledge or skills – often technical – that are considered minimum requirements for a role. 
  • Skills assessments: Through mobile-driven text conversations, video interviews, multiple-choice quizzes or even online ‘games’, job-specific and general work skills or soft skills can be assessed.
  • Coding assessments: There are many tools designed specifically to test and assess candidates’ coding abilities and technical skills. These assessments can be used at the screening stage to filter candidates or during later interview stages where full-scale coding challenges could reflect actual work or challenges the candidate would encounter as an employee. Tools can be a platform and industry-specific.
  • General skills: Tools can also address general work skills such as literacy and numeracy, basic typing and data entry, ability to follow instructions, and more.
  • Work behaviour assessments:  observe actual behaviours and simulations that match and help predict real on-the-job requirements. Job simulation exercises and work sample tests give candidates an opportunity to demonstrate their abilities and skills. They allow employers to assess job-specific skills and analyse candidates’ capabilities in decision-making and prioritising, multi-tasking or their ability to work under pressure. Tasks can be highly customised to specific responsibilities of the role and of the organisation.

2. Innate abilities and attributes assessments focus on traits that are not job-specific such as personality, interests and cognitive abilities including problem-solving, logic skills, reading comprehension and learning ability. These universal human traits have proven to be effective indicators of job performance and cultural fit. Softskill testing: Tools can be used for talent evaluation across a range of qualities and personality traits such as teamwork, sales ability,  good judgement, integrity, curiosity, impact, ownership and independence.

Some examples: 

  • Automated Interviews: AI-driven platforms can automate interview processes and provide a better experience for recruiters, hirers and candidates alike. Platforms like Sapia’s automated text interview can provide a true advantage, especially at the screening stage for large volume recruitment briefs such as customer-facing retail or service teams. 
  • Candidate ranking:  Powered by artificial intelligence and machine learning, many assessment tools will analyse results to grade and rank candidates.  Rankings around different criteria can save time and provide the confidence that you are focused on the right candidates.
  • Cognitive screening: These tools provide insight into how candidates think, solve problems and learn. Insights can help hirers understand future management needs to prepare and support new employees to be successful in their role.
  • Integrity assessments: Assessing attitudes and experiences relating to honesty, reliability and trust.
  • Psychographic screening: Insights into a candidate’s personality, values and interests can help assess their fit within a team and within an organisation’s culture and values.
  • Bias-free screening: Unconscious bias removed from the process so candidates are assessed on their skills and decisions are not influenced by a candidate’s gender, age, ethnicity and other personal credentials that do not affect their ability to do the job. Sapia’s mobile-first, text interview platform is an industry leader in blind interviewing.

10 questions to help you choose the best talent assessment tools 

Saving time and money, filling roles with better quality candidates. That’s the key reason talent assessment tools are indispensable across the recruitment industry and in every employment sector. But with the plethora of tools available, how do you decide which ones are right for your organisation? Which talent assessment tools will best contribute to your success?

Before you invest, Sapia’s talent assessment tool checklist can help:

1)  What do you need to know?

As an experienced recruiter, you can probably already recognise where your talent assessments sometimes fall short or you think they could be better. The data insight that can support your recruitment and hiring processes will be different for everyone and will vary according to:

  • industry or sector specialisation
  • experience level of candidates – entry-level to management and C-suite roles
  • qualifications, skills or personality traits required for roles
  • nature of brief–  specialist technical roles or large volume team roles

When you know what you need to measure, you can start narrowing your search to identify the tools that can give you what you’re looking for.

2) How will the findings be presented?

Consider the format and depth of the feedback that different tools can provide. Is a numerical ranking of candidates sufficient or will in-depth analysis, comparisons and recommendations better serve your needs?

3) Do assessments support the hiring organisation’s brand values and strategy?

Consider whether the tools positively support an organisation’s employment policies and practices such as workplace diversity and inclusion, language or numeric competencies and minimum skills requirements.

4) Do tools remove bias from talent assessment?

Removing unconscious bias from the talent assessment process is a priority for organisations looking to improve workplace diversity and inclusion. While a text-based chat platform (such as Sapia) can effectively take bias out of the equation, video submissions bring the opportunity for bias front and centre of the process.

5) Do the tools support the interview process?

Few, if any, hiring decisions should ever be made solely on the basis of talent assessment tools rankings or findings. Make sure tools can provide meaningful data that will enhance the interview process. Many tools will help identify areas that should be explored further in the interview process and even suggest questions to help shape the interview.

6) How will the tool integrate with existing systems?

The best tools will integrate with your existing systems and processes and with other tools. You want to be sure that you can combine data from different tools to create meaningful reports and records. Tools that integrate with your existing ATS (Applicant Tracking System) are likely to deliver the best savings in time and effort.

7) What will candidates think?

Every candidate deserves a fair and positive experience, whether they are successful or not. Choose tools that are easy and engaging to use, appropriate for the role and tools that will enhance, not undermine, your employer brand.

The best tools also deliver value by allowing candidates to provide feedback on their engagement with tools after the assessment process.

8) How do I find out what tools are best?

Ask your industry colleagues for recommendations and search the web for reviews and guides like this one that can help you navigate a very crowded market. When you think you’ve found the tools that will work best for you, your clients and your candidates, ask vendors to show you how their assessment tools can deliver with a personal demonstration or even a free trial.

9) Have you analysed the costs?

You want to be sure that your investment will pay its way. Take the time to consider the value of the candidate feedback or assessment of different tools will provide. Many vendors provide online calculators to help you estimate the return on your investment.

10) Do the tools support best practice?

Talent assessment tools can provide objective, measurable insights that other more traditional recruitment methods can’t provide. But technology has its limits too. Make sure that a positive candidate experience remains a priority – nobody wants to feel discriminated against or feel embarrassed or violated by intrusive personality testing. 

Make sure also that in focusing on one key skill or trait, you’re not missing a candidate’s true strengths. In short, don’t use your talent assessment tools as the recruitment tool, use them in conjunction with all the other methods, tools and skills in your recruitment toolbox.

The Buyers Guide to Navigating Ai Hiring Solutions

Leveraging objective data to augment decisions like who to hire and who to promote is critical if you are looking to minimise unconscious preferences and biases, which can surface even when those responsible have the best of intentions.

The greatest algorithm on earth is the one inside of our skull, but it is heavily biased. Human decision making is the ultimate black box.

Only with data, the right data alongside human judgment can we get any change happening. And clearly, what your employees and candidates are now looking for, is change. We hope that the debate over the value of diverse teams is now over. There is plenty of evidence that diverse teams lead to better decisions and therefore, business outcomes for any organisation.

This means that CHROs today are being charged with interrupting the bias in their people decisions and expected to manage bias as closely as the CFO manages the financials. But the use of Ai tools in hiring and promotion requires careful consideration to ensure the technology does not inadvertently introduce bias or amplify any existing biases. To assist HR decision-makers to navigate these decisions confidently, we invite you to consider these 8 critical questions when selecting your Ai technology. You will find not only the key questions to ask when testing the tools but why these are critical questions to ask and how to differentiate between the answers you are given.

This guide is presented by Sapia whose AI-powered, text chat talent assessment tool has a user satisfaction rate of 99%.  


The Machine picked the wrong guy

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:

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To AI or not to AI

A recent CNN story quoted only 12% of companies used AI last year to deliver not just a faster status quo, but a complete reinvention of the way they work. The automated learning that comes from AI  solutions grounded in machine learning also delivers exponential returns to those who start early.

That same news story quantified those benefits as a 20% increase in cash flows over 10 years and the inverse is true as well – a 20% decline in cash flows for those that wait. These kinds of stats should trigger ‘FOMO’  for any enterprise business.

‘BC’ (before Covid-19), the motivation ‘to AI in HR’ might have been the automation of manual expensive HR processes, like recruitment, in a world of declining HR budgets and growing concerns about the bias we humans bring to those processes. 

‘To AI’ your HR processes can also go beyond your bottom line. It’s a way to humanise your candidate experience. A way to reduce the asymmetry of recruitment, to empower both sides to make the right decisions. It gives you this kind of candidate feedback from a solution that looks like this.

Right now,  curiosity about AI is being replaced by a burning platform for change. For those wearing the exhaustion of surge recruitment using old traditional processes (not to mention the increased chances of bias as a result), the case for change is obvious. For everyone else who does any volume of recruitment, 4 factors will accelerate the move to AI solutions.

1. The need for humanity in your people processes especially recruitment. 

Even though tragically it will soon be an employers market as unemployment rises, any organisations, including government, that can make that experience better for job seekers is onto a winner. Nothing sucks more than having to line up at Centrelink,  or fill out endless tedious application forms, and then hear nothing.

We ‘live’ on our smartphones, we expect convenience and immediate results, we want to be able to navigate a wide range of opportunities fast and make decisions fast.  This applies to services we consume regularly (think Uber Eats, Afterpay, even banking services such as our next home loan). That immediacy and convenience is now the new norm for consumers, and candidates as a consumer of their next job are looking for the same experience.

Imagine if your applicants only needed to answer 5 engaging questions over a text conversation. Every applicant also receives their own personalised feedback which helps them prepare for future interviews!

Compare recruitment to applying for a bank loan where AI has been in use for a decade or more. That’s now a reality with AI in recruitment.

Use Sapia’s FirstInterview to see how easy it is for you to give every job seeker a fast, simple and empowering experience.

And read what job seekers think about it here.

2. The accessibility and affordability of AI solutions

We specialise in volume recruitment for those roles where it is even more critical to hire the right people now. Frontline roles like your customer service teams,  carers and health care workers, sales consultants, and blue-collar workers. Our ready-made predictive models are instantly deployable enabling you to go live in under an hour.  When using our AI saves you at least $20 on every applicant, (i.e. if you receive 1000 applications, that is a saving of $20,000), and deployment is as easy a sending a link to your applicants, AI offers value to any sized organisation.

3. The right AI tool can remove bias from your recruitment and deliver a more diverse workforce

No amount of bias training will make us less biased.

The ability to measure bias is one reason to use AI-based screening tools over traditional processes. The growing awareness that AI can be fairer for people prompted the California State Assembly to pass a resolution to use unbiased technology to promote diversity in hiring.

Avoiding bias is why we use text data to assess applicants. With 25 million words to draw upon in our data bank, across 10 critical volume hiring roles, our approach is both bias-free in its design and its execution. Our technology is built on the advances in ML and NLP that allow computers to gain valuable insights from large volumes of textual data. Our AI is entirely ignorant of race, age, gender or any of those irrelevant markets of job fit.

4. Knowing someone’s traits and values is a shortcut to hiring for culture 

Marketing guru Seth Godin wrote a blog a few years ago on the ‘real skills’ that matter in hiring.

Whilst we all know what matters for our roles, our teams, our culture- real skills like resilience, curiosity,  humility, drive and so on, these attributes are invisible in a CV and very hard to assess fairly and scientifically in a phone call or f2f interview.

Using text data, we can not only uncover standard personality traits such as extraversion, openness, humility but also real skills that matter such a drive, critical thinking, team player and accountability. Our data science team has recently uncovered that the language one uses in answering standard interview questions show a correlation to how likely they are to hop jobs. New hires that leave early cost significant time and money for organisations. Identifying such candidates early on can help companies make better hiring decisions.

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How our Sapia Labs team adapted a Google invention to lift the bar on Ai transparency in recruitment

Artificial Intelligence (mostly Machine Learning) is being used more and more for high-impact decision making, so it is important to ensure these models are used in a fair manner. 

At Sapia, we recognise the impact these technologies have on candidates when used in screening. We are committed to ensuring fairness by making the evaluations more inclusive, valid, unbiased and explainable – this is the essence of our FAIR™ framework.  

The Fair Ai for Recruitment (FAIR™) framework presents a set of measures and guidelines to implement and maintain fairness in Ai-based candidate selection tools. It does not dictate how Ai algorithms must be built, as these are constantly evolving. Instead, it seeks to provide a set of measures that both Ai developers and users can adopt to ensure the resulting system has factored in fairness.

The lack of transparency related to training data and behavioural characteristics of predictive models is a key concern raised when using machine learning based applications. For example, in most instances, there is no documentation around intended/unintended use cases, training data, performance, model behaviour, and bias-testing. 

Recognising this limitation, researchers from Google’s Ethical Artificial Intelligence team and the University of Toronto proposed Model Cards in this research paper. A Model Card is intended to be used as a standard template for reporting important information about a model, helping users make informed decisions around the suitability of the model. The paper outlines typical aspects that should be covered in a Model Card, such as  “how it was built, what assumptions were made during its development, what type of model behaviours could be experienced by different cultural, demographic, or phenotypic population groups, and an evaluation of how well the model performs with respect to those groups.”

Sapia Labs has adopted and customised the concept of a Model Card to communicate a broad range of important information about a model to relevant internal and external stakeholders. It acts as a model specification, and the single source for all model details.

Here are some of the topics covered in a Sapia Model Card:

  • Model Details: Provides high-level information about the model under the subsections overview, version, owners, licence, references, model architecture, feature versions, input format, output format. These details clearly set out the responsibility for the model and document all the relevant information.
  • Considerations: Important considerations in using this model, such as intended users and use cases, ensuring that the model is used only as originally intended. It also includes a colour-coded summary of adverse impact testing results (covered under quantitative analysis below).
  • Dataset: Sources and composition of the dataset and distribution charts of features used by the model.
  • Quantitative analysis:
    • Adverse impact testing: Statistics on sensitive attributes and groups, a visual overview of adverse impact testing results in terms of effect sizes and the ratio of recommendation rates (4/5th rule), followed by a very detailed report going into the adverse impact at the individual feature level.
    • Model dynamics: Distribution of the outcome score and the behaviour of the model, presented with partial dependency plots, which improve the explainability of the model.

The generation of the Model Card is automated, and is an integral part of the model build process, ensuring a Model Card is available with every model. 

Having a standardised document for communicating a model specification has enabled faster and more effective decision making around models, especially on whether to go live or not. Integrating Model Cards is part of the continuous improvement process at Sapia Labs on the ethical use of ML/AI. The contents continue to evolve based on the team’s ongoing research and requests made by other stakeholders. As far as we know, this effort is an industry first for the employment assessment industry, and we are proud to be leading in this space.

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