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.
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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.
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.
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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.