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How to reduce candidate dropout by measuring and boosting candidate experience

How to measure candidate experience | Sapia Ai candidate experience software

Candidate experience: Everybody’s talking about it, few companies are actively investing in it.

According to a Sapia-sponsored Aptitude Research report from earlier this year, 68% of companies admit they have no plans to address the interview portion of their candidate experience throughout 2022 and 2023. Despite this, 50% of these companies know they’re losing talent due to their application and interview processes. What’s more, according to Forbes, companies that prioritize candidate experience can see their average quality-of-hire improve by 70%.

Why the unwillingness to address such an important facet of recruitment? In most cases, the teams responsible for enacting change to candidate experience are steeped in the everyday throes of talent acquisition, and don’t have time right now to examine their processes. Statistically speaking, this is probably where you’re at. Totally understandable; the 2023 labor market is tough. If your house is on fire, you’re probably not focussed on how well you treat the visitors at your doorstep.

Recently, on our Pink Squirrels! podcast, we sat down with Lars van Wieren, CEO at Starred, a candidate experience measurement tool. Lars offered some practical tips on getting started with candidate experience: Benchmarking it, measuring it at different stages of the process, and setting your business up to review and act on the findings.

Step 1: Benchmark your candidate experience

As the saying goes, what gets measured, gets managed. Lars recommends starting with a basic benchmark for your candidate experience. This need not be difficult, and you don’t necessarily need a fancy candidate experience tool to start gathering these data.

Simply ask your candidates: How likely are you to recommend our company to a friend or colleague? This is, in essence, a Net Performer Score (NPS) question, and the scale (1 to 10) should reflect that.

The impact of useful candidate experience | Sapia.ai candidate experience software

Ideally, you should be gathering feedback on your candidate experience at each stage of the application process, but to begin with, ask the question at the very end. And to get the best, least-biased data, you need to ask all applicants whether or not they’ve been shortlisted or hired – if you only ask those who have been shortlisted, or the few people who have been successful, you’re likely to get magnanimous results that don’t reflect your true candidate experience.

The NPS tracking question is easily configurable and embeddable into automated emails, meaning it can be set up through your ATS with little additional work.

When you begin to analyze the data, keep things simple: Dump the data into a spreadsheet, and look at your average numbers. If your score is below 0, you’ve got work to do – if it’s 0 to +30, you’re doing well. 30+ and over, well done!

(If you’re reading this, it’s probably not likely that you’ll get a 30+ score on the first go-round. That’s okay – the goal is to find out how much work you’ve got to do.)

The benefit of benchmarking NPS is that it gives your business a single, easy-to-understand proxy for the health of your candidate experience. Once you’ve got the number, you can start to make small changes to your application experience and see how that affects the overall number.

For example, you might consider making the following changes to improve your candidate experience:

  • Maybe we don’t need to ask for cover letters. Most candidates don’t like writing them (statistically speaking), and they’re a time-suck for our team.
  • Maybe we can shorten the content on our careers page. It’s long, and difficult to navigate.
  • Maybe we should consider using Easy Apply functionality.
  • Maybe we are running too many interviews in our process. Let’s do fewer, and see how that works.

At the same time, you might consider looking at your candidate abandonment rate – we’ve got a post on measuring and improving it here. Candidate experience scores and abandonment rates are almost always linked. Improve one, you improve the other.

Step 2: Gather more data on each step of your job application process

Our joint report with Aptitude Research uncovered some interesting data on the importance of two-way feedback between candidates and employers.

Gathering and acting on mutual feedback:

  • Boosts quality of hire from 36 to 58%, on average
  • Boosts candidate experience from 34 to 44%, on average
  • Improves first-year retention from 35 to 50%, on average

Feedback is critical. And, to make it as accurate and indicative as possible, your feedback should ideally be gathered at each stage of the application process: Application, screening, interviewing, assessment, offer, and rejection.

By doing this, you’ll know exactly where your candidate experience is lacking – and you can make fast, effective changes.

Multi-step candidate experience feedback may not be easy to do with your current setup, but it is relatively simple to configure if your ATS/chosen software solution has the capability.

Step 3: Get your team around the candidate experience data

Generally speaking, the task of improving candidate experience is that of your entire talent acquisition or recruitment team. But it’s a good idea to appoint an internal candidate experience champion – someone who is responsible for collating the benchmark data and regularly reporting on it.

What’s the reporting cadence? Depends on the amount of applications you have, and the length of your application process. A monthly score update check-in works best for most. Monthly measurement will likely give you an insightful trendline.

While the task of improving candidate experience is never done, it needn’t require an overhaul to your entire recruitment business. Start small, make iterative improvements over time, and focus on making at least one more candidate smile.

(P.S. Candidate experience is a natural benefit of using our Ai Smart Interviewer – in fact, more than 90% of the 2 million candidates we’ve interviewed have loved our process, and 84% of them are highly likely to recommend you to a friend or family member. Reach out to us here to find out more.)


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Sapia.ai Wrapped 2024

It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.

And we’re just getting started 🚀

Take a look at the highlights of 2024 

All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.

Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks. 

See why our users love us 

Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.

Share the candidate love

Leading the way in AI for hiring 

We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders. 

Join us in celebrating an incredible 2024

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Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment

Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.

AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection. 

This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?

1. The Static Nature of SJTs

Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements. 

Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.

AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.

2. Richer Data Through Open-Ended Responses

One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.

AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.

3. The Candidate Experience: Stressful or Supportive?

SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.

In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.

4. Addressing Bias and Fairness

Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups. 

AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.

5. An Assessment That Improves Over Time

Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.

AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.

Rethinking Candidate Assessment

As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.

These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.

For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you  choose tools that help you make decisions that are both informed and equitable.

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Keeping Interviews Real with Next-Gen AI Detection

It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.  

As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.

Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.

What’s New?

Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate. 

This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.

The Challenge of AI in Chat-based Interviews

Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role. 

For Candidates: Enabling Authenticity

When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences. 

For Hiring Teams: Actionable Insights

Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines. 

Built on Unmatched AI Interview Expertise

“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.

Why This Matters

The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:

“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”

Testing and Validation of the AGC Detector 2.0 

The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.

Fairness & Transparency in AI-Enabled Hiring

By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.

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