Let’s begin with the obvious: Good talent is in high demand and short supply. Candidates have become discerning shoppers, more aware of their worth than in recent market cycles.
As a result, the onus is on us to change the way we source candidates and generate demand for our company. It’s no longer a case of boosting job ads across a few different channels; to court the best people, we need to focus on strategies that build meaningful and beneficial connections over the longer term. Today, branding, Employee Value Proposition (EVP), messaging, positioning, and creative differentiation are more important than ever.
Here are some questions you and your team may be asking:
- How do we best promote our business as the one to work for?
- How strong is our brand?
- What is our content strategy (Or: What do we say, when/how/why and to whom do we say it)?
- How do we revitalize our Employee Value Proposition (EVP)?
- How do we reach the best candidates in new and memorable ways?
The essence of new-school recruitment marketing
Summarized in a single phrase, your best recruitment marketing strategy is this: Add value. Sounds simple, but it does need some unpacking.
Take this recent episode of Sapia.ai’s Pink Squirrels! podcast, in which we spoke with Jennifer Paxton, VP of People at Smile.io. Jen has taken an always-on approach to talent acquisition by being active as a content creator on LinkedIn. Jen regularly posts helpful tidbits and articles about people leadership, employee engagement, career development, and plenty of other topics. In doing so, she is also able to organically (and indirectly) promote the virtues of Smile.io.
Here’s what’s neat about this: Jen is promoting Smile whether she references Smile or not. If you’ve built a dedicated audience, and that audience sees your other associations, they are much more likely to look favourably on those associations than if you mentioned them overtly or if they came upon the association in a different context (e.g., a display ad on LinkedIn). That’s good marketing.
According to Jen, this has been a big success for Smile, because she is constantly engaged in the process of creating and fostering good relationships with potential employees. Today, they may simply be followers and consumers of her content; tomorrow, they may be teammates. When a vacancy opens up, Jen has more tactics up her sleeve than merely boosting job ads. Her first (and best) option is to put a call out to her always-growing network of engaged professionals.
What Jen does is not necessarily easy – it requires dedication and consistency – but it is simple. It’s about adding value as a people leader, and creating a first-hand connection with the market. Everyday customer facetime is truly invaluable, and for Jen, it’s certainly working.
If you want to learn more about how you can lead recruitment marketing through an always-on content strategy, you can also check out this Pink Squirrels! episode with Russell Ayles, a veteran recruiter and LinkedIn Top Voice for 2022.
The challenge of recruitment marketing attribution
Here’s the rub: If you’re having to do all these new things over a long period of time to prime and court the talent you want, how do you know what’s working? For example, if it takes six months, at minimum, to build and execute a recruitment content strategy, how will you know in month two or month four how things are tracking?
Trickier still, when your CEO or CHRO asks to report on outcomes, what will you tell them? What level of analysis is suitable for stakeholders at that level? How do you reconcile the need for patience with the performance pressures of the executive?
This conundrum is the main reason most companies don’t bother with an add-value strategy, even when their talent pools have dried into a puddle. After all, the ‘boost-your-job-ad’ method still yields concrete and easily-understandable numbers, even if those numbers are bad.
Going new-school with recruitment marketing requires a bit of faith, supplemented by regular analysis of the signals of success. So let’s look at one of the biggest signals for success: Self-reported attribution.
Ask your candidates: How did you hear about us?
Seems far too simple to be useful, doesn’t it? In actuality, this one question can inform the success and evolution of your entire recruitment marketing strategy. It’s not a quantitative metric, of course, not as black-and-white as your abandonment rate or NPS metrics, but the insights can be truly transformative. Here’s how it works.
For the sake of simplicity, let’s say your team has decided on a three-pronged strategy:
- All talent acquisition leaders will start posting on LinkedIn, three times a week, according to a pre-set content plan.
- The Head of Talent Acquisition/CHRO, the CEO, and the company’s marketing leader will create a twice-monthly podcast on an area of interest related/adjacent to the business (for e.g., if you’re a retail fashion brand, you might consider a podcast on the principles of design, or merchandising, or upcycling).
- The regional Community Manager will take tidbits from the podcast and post them on Twitter daily, alongside a bunch of other fun and light content suited to the medium.
Three tactics, three different channels. Now, to track the ongoing health of these measures, you might look at the following metrics on a monthly basis:
- Trending traffic to website/careers page
- Increase in social media followers
- Increase in podcast shares
And plenty others besides. But, crucially, you should also add a field to the form you use as a first step in a job application: A free-text field with a simple, mandatory question: How did you hear about us?
(Ensure that, in form design, you don’t lead the candidate in any way. Don’t have any pre-text in the field (saying something like ‘e.g., Seek’). You want unbiased results.)
You’ll be amazed at what you can learn. Some candidates will offer you vague and unhelpful responses (like ‘Internet’), but over the medium term, you should start to see trends emerge. For example, if a great many of your good candidates are hearing about you through the podcast, they will tell you, and you will come away with hard numbers showing which of your long-term brand-building strategies is working best.
After six months, you’ll start to see more candidates. And you’ll see the following (for e.g.):
- 30% of candidates say they heard about you through LinkedIn
- 40% will say they heard about you through the podcast
- 10% will say Twitter
- 20% might cite some other channel, like referrals
This kind of recruitment marketing attribution is helpful because it is simple, it is highly indicative (both of past performance and future improvements), and it is compatible with the reality of the market we’re in. Right now, the majority of candidates aren’t looking for work with you – but they are looking for useful, valuable, enjoyable content. It may be a six-month journey from awareness to application readiness, and you should be with them along that journey, helping, educating, informing.
If, instead, you get stuck looking at the ROI of job ad boostings, or even the success of individual pieces of content, you’ll be led astray by the data. In isolation, individual customer touchpoints do not help you iterate. In fact, they will have you doing something different every week. You’ll confuse your audience, see limited success, get frustrated, and quit.
Channels, conversely, paint a picture of customer consumption behaviors and traffic patterns. They show, over time, that your presence is of net benefit.
The best part about self-reported attribution? You can start doing it now, without making any changes, and start to capture data about your activity and brand strength to date.
Give it a try.
Related
Blog
Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: