In the competitive market of candidate metrics, it’s absolutely critical to enhance the candidate experience throughout the entire application process. With the importance of candidate experience metrics and recruitment metrics rising, you simply cannot afford to lose the talent that you’ve spent time and money attracting.
This might sound straightforward, but abandonment rate, especially the application drop-off rate and candidate drop-off rate, is a significant concern. Many companies remain oblivious to where, when, and why candidates abandon their application processes.
Let’s initiate the discussion with recruitment metrics, followed by a dive into how we can apply them to enhance your broader talent acquisition journey.
Overall candidate abandonment rate = number of candidates still in the process at shortlist stage, minus the total number of candidates who landed on your careers page, divided by that total number again. Or:
At its core, monitoring abandonment rate or application drop-off rate is pivotal. It acts as a generalized diagnostic tool that indicates the efficiency of your recruitment process.
Imagine you had 100 visitors on your careers (or job ad) page, but only 10 make it to your shortlist. That’s a staggering 90% loss of your potential talent pool at various stages.
This leads to a crucial inquiry: How can one gauge the candidate drop-off rate at each phase of the application process?
Assuming you had 100 visitors to your careers page, and only 20 completed the primary application form, it implies an 80% candidate drop-off right at the outset.
This isn’t ideal, but now you have a clearer picture. You can now scrutinize the page to identify potential barriers that deter candidates from applying. Questions to consider might include:
- 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?
Without assessing each stage independently, it’s challenging to ascertain why candidates aren’t persisting.
To emphasize, alongside the overall abandonment rate, it’s imperative to measure the candidate drop-off rate at each step of your talent acquisition process. The subsequent section can guide your focus.
Where, when, and why do candidates abandon the application process?
It’s a general notion that an elongated application and interview process results in a higher candidate drop-off rate. However, this doesn’t provide actionable insights. To rectify issues, you need precise, localized data to pinpoint and address the bottlenecks.
Recent data from a 2022 Aptitude Research report on pivotal interviewing trends disclosed that candidates tend to abandon the process at these stages:
- 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
These statistics are invaluable. By auditing your recruitment process against these benchmarks, weak areas become evident.
For instance, a lengthy, multi-step interview might seem ideal for assessing cultural fit, but if it’s cumbersome for candidates and requires coordination among several stakeholders, it might be counterproductive.
The interview stage witnesses the highest application drop-off rate, with 25% of candidates opting out. This doesn’t come as a surprise, given the lengthy and often ineffective nature of many interview processes. Aptitude Research indicates that 33% of companies lack confidence in their interviewing capabilities, and 50% believe they’ve lost potential hires due to subpar interviews.
When surveying HR and TA leaders about their primary interviewing challenges, the responses included:
- Our interview process is too long (52%)
- We make candidates go through too many interviews (39%)
- Out interview process is inconsistent (38%)
- We don’t use objective data to drive decisions (32%)
- We have bias in the interview process (21%)
The lack of objective data is a glaring issue. Nearly a third of companies base their interview and application procedures on assumptions rather than concrete data, leading to inefficiencies.
Yet, 68% of companies admitted to not making any enhancements to the candidate experience this year. So, how many are genuinely evaluating their talent acquisition journey to identify shortcomings?
That’s why this post emphasizes the candidate abandonment rate. It’s a straightforward metric that reflects the vitality of your application process. It’s arguably simpler to measure compared to other intricate recruitment metrics, like quality-of-hire. As the adage goes, “What gets measured, gets managed.”
Start here today, and see what you learn.
(P.S. Sapia’s Ai Smart Chat Interviewer combines the first four stages of your process – application, screening, interviewing, and assessment – together, resulting in an application process that can secure top talent in as little as 24 hours.
Because it’s a chat-based interview with a smart little AI, your team doesn’t need to do anything – everyone who applies gets an interview, immediately. That maximizes your talent pool right from the get-go.
What’s more, our candidate dropout rate is just 15%, on average. That means that 85% of your starting talent pool will stick around.
Why do our candidates stick around? More than 90% of them love the experience. See how we can help you here, today.)
Related
Blog
How leading retailers are using AI-Native Hiring
Retail leaders have embraced AI to improve supply chains, automate checkout, and enhance customer experience. But what about finding the people who deliver that customer experience?
AI brings incredible possibilities to supercharge how retailers hire, develop, and retain talent.
At Sapia.ai, we helped iconic retailers like Woolworths, Starbucks, Holland & Barrett, and David Jones reimagine hiring from the ground up – replacing resumes, ghosting, and gut feel with structured, ethical AI that delivers performance and fairness at scale.
The Retail Problem: Volume, Turnover, and Ghosting
Retail is high volume. It’s high churn. And it’s high stakes for candidate experience:
- Candidates ghosted during slow hiring cycles
- Store managers are overloaded with admin
- Recruiters are overwhelmed with 100,000+ seasonal applicants
- Talent is overlooked due to bias or unfair screening processes, not a lack of potential
And yet, most hiring still relies on broken tools: resumes, forms, manual processes, and outdated systems.
Sapia.ai: The AI-Native Hiring Engine Built for Retail
Our platform automates the entire “apply to decide” journey, leveraging AI & automation to streamline the hiring process & bring intelligence into retail hiring.
Smart Interviewer™: Mobile-first, chat-based, structured interviews for a holistic candidate assessment.
Live Interview™: AI-driven bulk interview scheduling without calendar chaos.
InterviewAssist™: Instant interview guide generation.
Discover Insights: Embedded analytics to track hiring health in real-time.
Phai: GenAI coach for career and leadership potential.
Unlike resume parsing or generic chatbots, Sapia.ai assesses soft skills, communication, and culture fit using natural language processing and validated psychometrics. It’s ethical AI built in, not bolted on.
From Application to Interview in Under 24 Hours
Candidates don’t want to wait. They don’t want to be ghosted. And they don’t want resumes to define them.
> 80% of Sapia.ai chat interviews are completed in under 24 hours.
We see consistently high completion across categories: grocery, merchandising, home improvement, and luxury retail.
“It was fast, fair, and I actually got feedback. That never happens.” – Retail Candidate Feedback
Real Impact, Across Every Retail Category
Sapia.ai powers hiring for millions of candidates across diverse retail environments:
| Impact of Sapia.ai on Retail Hiring in 2024 | |||
| Category | Hours Saved | FTEs Saved | Cost Saved |
| Grocery | 272k | 131 | $6.5m |
| General Merchandise | 193k | 93 | $4.6m |
| Specialty Retail | 133k | 64 | $3.2m |
| Home Improvements | 103k | 50 | $2.5m |
| Merchandising | 22k | 11 | $0.5m |
| Luxury | 9k | 4 | $0.2m |
The savings created by intelligent, AI-native automation have unlocked team capacity, impacted retailers’ P&L, and improved store readiness.
Speed That Delivers Real ROI
Every candidate gets interviewed instantly. No waiting. No bias. Just fast, fair, data-backed decisions. This generates real impact for retailers who previously relied on slow, outdated processes to handle thousands of applicants.
- Woolworths: 5,000 hours saved in a single week
- Starbucks: Doubled hiring capacity, 91.8% completion
- Holland & Barrett: Time to hire cut from 20 to 7 days
- Woodie’s: 3x more ethnic minorities hired in 3 months
DEI by Design, Not by Mandate
With Sapia.ai:
- 98% of candidates opt in to demographic questions
- Zero adverse impact detected across gender, ethnicity, and disability
- 1.5–3x improvements in diverse hiring rates
DEI Fairness Scores (based on actual hiring data):
Gender: 1.03 (vs customer baseline of 1.01)
Ethnicity: 1.15 (vs customer baseline of 0.74)
Why? Because ethical AI removes what humans can’t unlearn: bias. With a candidate experience that is inclusive by design, retailers can ensure fairness in screening, and measure it in hiring.
Candidate Experience = Brand Experience
Retail candidates are your customers. And the experience you give them matters. We have built a brand advocacy engine that delights candidates and gives you the data to prove it.
- 9.2/10 CSAT across 2.6 M+ retail candidates
- NPS: 78 (30+ points above industry benchmark)
- 87% more likely to recommend the company’s products post-interview
Responsible, Explainable AI Built for Retail
Not all AI is created equally. Since 2018, Sapia.ai has been built on a foundation of responsible AI:
- No use of resumes or scraped data
- Hosted securely via AWS Bedrock
- Claude-powered LLM scoring with model cards and explainability
- Independent audits on bias, privacy, and methodology
“We can’t go back to life before Sapia.ai. We used to spend half the day reading resumes.”
— Talent Lead, Starbucks AU
What’s at Stake: Time, Brand, and Revenue
Every day spent using outdated hiring methods costs retailers:
- Wasted recruiter hours
- Lost revenue from unfilled roles
- Bad churn that drains training budgets
- Lower customer satisfaction from poor-fit hires.
With Sapia.ai, you get the productivity unlock retail hiring demands, and the intelligence your talent deserves.
Want to see how fast, fair, and human retail hiring can be?
Book a demo
Blog
Reinventing the Competency Framework: A Data-Driven Approach for the AI Era
We can’t hide from reality anymore. Talent needs are shifting overnight, and AI is redefining what it means to work. Traditional talent frameworks are no longer fit for purpose. At Sapia.ai, we believe the future of talent strategy lies in a smarter, fairer, and more adaptive way of defining what great looks like.
Our AI hiring platform is built on the largest proprietary dataset of interview answers globally – we’re a data company at heart, and we’ve seen the power of data-driven people methodology in transforming how organisations hire and retain good talent.
So, when it came to building a new Competency Framework that could be leveraged globally for hiring for any role at any scale, of course, we used a ground-up, data-led methodology that bridges the gap between organisational psychology and AI.
Why Rethink Competency Frameworks?
Conventional frameworks are typically crafted through expert interviews and focus groups. While valuable, they tend to be subjective, static, and too slow to keep pace with evolving job demands. As roles become more fluid and technology augments or replaces task-based skills, organisations need a new way to understand the human capabilities that genuinely matter for performance.
We wanted to identify enduring, job-agnostic competencies that reflect what drives success in a modern workplace – capabilities like adaptability, resilience, learning agility, and customer orientation.
(Why competencies and not just skills? Read why here.)
Our Approach: Where AI Meets I/O Psychology
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
- Can we make competency discovery agile, scalable, and evidence-based?
- Can we use AI to automate the process without losing the rigour of traditional psychology?
The answer to both: yes.
We began with a rich dataset of over 37,000 job descriptions across industries and role types. Using large language models (LLMs) and advanced NLP techniques, we extracted over 200,000 behavioural descriptors. These were distilled down through a four-step process:
- Behavioural Descriptor Extraction
- Clustering and Labeling
- Cluster Analysis by I/O Psychologists
- Thematic Categorisation and Definition of Competencies
This resulted in a refined list of 25 human-centric competencies, each with clear behavioural indicators and practical relevance across a wide range of roles.
Built to Scale. Built to Adapt.
Our framework is intelligent, but importantly, it’s adaptive. Organisations can apply this methodology to their own job descriptions to discover custom competencies. This bottom-up, role-data-led approach ensures alignment to real work, not just theoretical models.
And because the framework integrates directly with our AI-powered hiring tools, you get a connected system that brings your talent strategy to life.
Our framework comes to life in the following tools:
- Job Analyser – Starting with a job description, it creates a unique competency profile for each role to build tailored structured interviews in seconds.
- Structured Chat-based Interviews that assess candidates’ responses according to the competency profile for consistent candidate assessment.
- Talent Insights Reports from every interview with deep reasoning and explainability for fair and objective hiring decisions.
- Phai Career Coach for internal mobility and employee growth that considers their competency strengths and career aspirations.
The Future of Talent Acquisition & Development is Competency-First
Skills alone cannot predict success. Competencies do. As AI continues transforming how we work, Sapia.ai’s Competency Framework offers a scalable, scientific, and fair foundation for hiring and developing the talent of tomorrow.
Want to see how it works? Download the full framework.
Blog
It’s Time to Stop Hiring for Skills, and Start Hiring for Competencies
If you’re a CHRO or Head of Recruitment at an enterprise today, chances are you’ve been inundated with messages about the importance of “skills-based hiring.” LinkedIn’s recent Work Change Report (2025) is full of compelling data: a 140% increase in the rate at which professionals are adding new skills to their profiles since 2022, and a projection that by 2030, 70% of the skills used in most jobs today will have changed.
This is essential reading. But there’s a missed opportunity: the singular focus on “skills” fails to acknowledge the real metric that talent leaders need to be using to future-proof their workforce — competencies.
Skills vs Competencies: The Crucial Distinction
- Skills are task-specific capabilities. Think Python programming, Excel, or even negotiation.
- Soft skills refer to interpersonal or behavioural qualities like adaptability, communication, and resilience.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
- Competencies are clusters of skills, knowledge, behaviours and abilities that are observable, measurable, and context-specific.
Put simply, competencies answer the all-important question: Can this person apply the right skills, in the right way, at the right time, to deliver results in our environment?
Why Competencies Matter More Than Ever
The Work Change Report outlines a future where job titles are fluid, roles evolve quickly, and AI is a constant disruptor. This creates three massive challenges for hiring at scale:
- Roles are changing faster than static skill frameworks can keep up
- Job candidates may have non-linear, cross-functional backgrounds
- The shelf-life of technical skills is shrinking rapidly
Skills alone don’t tell us whether someone can succeed in a role that will look different 12 months from now. But competencies can. Because they measure not just what a person knows, but how they apply it.
Adaptive Talent: The New Competitive Advantage
The LinkedIn report highlights a critical insight: organisations now prioritise agility in entry-level hiring. And there’s a good reason for that. With professionals expected to hold twice as many jobs over their careers compared to 15 years ago, adaptability is not just a nice-to-have. It’s core to success.
But you can’t measure agility with a keyword on a CV. You measure it by looking at competencies like:
- Learning agility
- Change resilience
- Cross-functional collaboration
- Problem-solving in ambiguous contexts
When you shift the focus away from skills to behavioural competencies that can be defined, observed, and assessed in structured ways, you open yourself up to a much more dynamic and more useful way of managing talent.
Building a Competency-Based Talent Framework
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
- Define Role-Specific Competencies: Move beyond job descriptions based on qualifications or vague skill sets. Break roles down into measurable competencies that reflect current and emerging performance expectations. This step is crucial for organisations to be able to accurately assess role-fit in the next stages. Sapia.ai does this automatically, taking job descriptions and building role-specific competency models in seconds.
- Assess Competencies Fairly and Objectively: Use structured behavioural interviews, ideally at scale. These provide a much more accurate picture of a candidate’s readiness than self-reported skills or credentials. Sapia.ai’s AI powered interviews enable competency assessment, at scale.
- Build Pathways for Development and Internal Mobility: A competency framework makes it easier to identify transferable strengths, development gaps, and future-fit potential. It gives employees clarity on how to grow within the business. Using an AI-powered coach can help ensure that talent is being continuously developed against the organisation’s competency framework.
The Future of Work Requires Depth, Not Just Breadth
LinkedIn’s data shows that people are learning more skills more quickly than ever. But the real question for talent leaders like you is: Are those skills being applied in ways that drive value? Are we hiring for task proficiency or performance?
The truth is that the organisations that will thrive in an AI-driven, skills-fluid economy aren’t the ones chasing the next hot skill. They’re the ones designing systems to identify, develop and scale competence.
Keen to Shift to Competencies, but Lacking a Framework?
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.