An average time-to-hire of 40 days. Hiring costs in excess of $2,000 per candidate. An average turnover rate of 60-70%.
The challenges of hourly recruitment in the retail industry have been well-documented.
Despite this, many of the largest companies persist with old-school recruitment processes.
Given the break-neck pace and scale of the industry, it’s hard to diagnose and fix the problem.
Understandably, many HR leaders have been quick to layer on technology solutions that seem to make things easier; in actuality, these tech solutions have added complexity, making efficiency gains difficult and actionable insights hard to find.
The big and unmanageable HR tech stack
Where recruitment is concerned, a HR tech stack tends to look like this: an unwieldy ATS, often coupled with a conversational AI or scheduling tool.
This stack is implemented across a decentralized system – hundreds of stores across the country – resulting in a situation where hiring managers are forced to use systems they don’t understand and don’t like.
The bottom line is this: Retail companies are overstacked, overworked, and need to adopt different solutions to old problems.
1. Solve high turnover through soft skill and personality trait matching
One of the biggest challenges with recruitment at major retail companies is high turnover rates. Retail staff members move fast and often, and have a high likelihood of migrating to competing businesses.
This is partially a nature-of-the-beast problem, but if we better understand what makes people tick, we can better match them to the roles at which they’re likely to succeed, and therefore keep them longer.
For example, we know that the best retail cashiers are high in extraversion. They’re energized by being around people, have good interpersonal skills, and have a lower likelihood of experiencing negative emotion while on the job.
It makes sense, then, to prioritize extraversion when matching candidates to the role of cashier. That’s a personality trait – with attendant soft skills – that will predict success for that role.
When people are matched to the job for which they are best suited, they’ll experience higher levels of purpose and satisfaction. It’s obvious why – the daily activities will invigorate rather than drain them. People who have purpose stay longer.
Therefore, if you accurately match soft skills to roles, you’ll reduce churn. Our AI Smart Chat Interviewer is really good at this: Across the board, our skill-matching power reduces non-regrettable churn by a minimum of 25%.
Side note: HEXACO is your secret weapon
If you’re keen to get started measuring soft skills, download our HEXACO job interview rubric. It features more than 20 interview questions designed by our personality psychologists to assess the skills of candidates that come your way. It will even help you figure out what soft skills are best for you based on the needs and values of your organization.
2. Reduce competition from other employees through smart employer branding
Chances are, when your employees or candidates leave, they’re probably staying within the industry – and that means they’re likely going to your competitors. It’s 2023, and the stock-standard advice would be to offer higher wages and perks.
That’s not always feasible, and besides, there’s no guarantee that doing so will markedly reduce the threat of poaching and abandonment. Money is important, but it doesn’t trump purpose and belonging.
The key to better employer branding is a system for active listening. Find out what your people, be they employees or candidates, think. Ask them often. It’s important to do this at the onboarding stage, but it should continue through to the point of highest churn – the six-month mark.
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
An NPS (Net Performer Score) framework is a good place to start. How likely are you to recommend our company to a friend or colleague?
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 employee engagement. Once you’ve got the number, you can start to make small changes and see how that affects the overall number.
3. Increase your talent pool by limiting candidate abandonment
We hear it all the time: Sourcing is a big problem. When we ask customers about their current processes, however, a common problem emerges: We don’t really know how many people we’re losing from our recruitment funnel, and why.
This presents a great opportunity: Often, improving an application process means removing things, rather than adding them.
Conventional wisdom tells us that the longer your application and interview process goes on, the higher your dropout rate will be. But that’s a generalized issue – it tells you nothing about how to fix the problem, beyond simply making it shorter. You need specific, localized data to diagnose and fix your leakage spots.
Data from a 2022 Aptitude Research report on key interviewing trends found that candidates tend to drop out at the following stages, in the following proportions:
- 22% of candidates drop out at the application stage
- 24% at the screening stage
- 25% at the interview stage
- 18% at the assessment stage
- 9% at the offer stage
Let’s say that you had 100 visitors to your careers (or job ad) page, and 20 of them completed the first-step application form on that page. You’ve lost 80% of your possible pool right there. Not great, but at least you know – now you can examine that page to uncover possible issues preventing conversion.
Is the page too long? Does it have too much text? Is the ‘apply’ button clearly shown? Is the form too long, requiring too much information to fill out? Are your perks/EVP attributes clearly displayed?
We’ve got an in-depth guide for measuring and improving your abandonment rate here.
Related
Blog
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.
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.
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.
Blog
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.
Blog
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.