Here at #ThriveMap, we can help you reduce new hire attrition for #VolumeHiring.
Our Realistic Job Assessments show candidates the role realities and allow them to get a taste of what it's like to work for your organisation.
Find out more:
bit.ly/30WEIfH
What if your hiring process was discriminating against candidates but your reporting showed everything was fine? If you're using AI screening tools for your recruitment process, you'll want to read Standford's new research:
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Less than 1 in 4 people think AI screening is fair.
The Global Recruiter picked up on our State of the Assessment Market 2026 data this week, and that stat alone tells you why it's getting attention.
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Signal by ThriveMap is a free job analysis tool that helps you understand exactly what a role involves, straight from the people doing it. In minutes, you get a clear, data-backed breakdown of tasks, required attributes, and realistic expectations.
👉 hubs.la/Q04jlMsQ0
Most pre hire assessments teach candidates how to “pass”, not whether they’ll actually enjoy or succeed in the role.
That’s why job realism matters.
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If 1 in 3 of your frontline hires leaves early, the problem isn't candidates. It's that they were never shown what the job is like.
Realistic Job Assessments flip the model: candidates experience the real role before they accept it. Wrong-fit ones opt out. Right-fit ones lean in.
Most hiring teams think automation is solving their volume problem.
In reality, it creates GDPR risk they don’t fully understand.
We’ve broken down what GDPR Article 22 actually means for AI hiring tools — and where teams are already getting it wrong.
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📣 New: ThriveMap now integrates with Cornerstone
Our new integration adds a predictive layer on top: insight into whether a candidate will actually succeed and stay in the role once hired.
Read more 👉 hubs.la/Q04hh-DM0#HRTech#TalentAcquisition#Hiring#Cornerstone
ICO Roundtable This Morning:
Not all AI in hiring carries the same level of risk.
ThriveMap is joining ICO discussions on ADM, Article 22 and fair recruitment technology.
#Hiring#GDPR
Most high volume hiring looks efficient… until you measure retention.
Safelite transformed their approach using job realistic assessments across 300,000 candidates. The results:
👉30,870 fewer days of unfilled vacancies
👉 $1.1M saved in cost to hire
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Hiring processes are still trying to predict candidate fit from limited signals. But candidates don’t know what the job is like when they accept. Companies reducing attrition aren’t better at prediction.
They’re better at showing candidates the reality. hubs.la/Q04fMxXh0
Most apprenticeship hiring looks fine…
Until you look at retention.
In this case with Berkeley Group:
👉 6-month retention: 87% → 98%
👉 12-month retention: 62% → 85%
Here's how they did it:
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Keen to understand how candidates are actually using AI in assessments?
It's probably not how you think.
Here's 50 stats on what’s really happening 👇
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“Job catfishing” is starting to get real attention.
Nearly 3 in 4 people say they’ve accepted a job that wasn’t what they expected.
Good to see this being picked up more widely — this piece from our CEO Christopher Platts breaks it down for HR Director.
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Employers have got very good at selling roles.
They’ve got worse at telling the truth.
That’s the expectation gap.
Candidates aren’t failing hiring.
They’re accepting jobs that don’t exist.
And leaving when reality hits.
👉 hubs.la/Q04dl5sW0
Entry-level hiring loop:
Hire in bulk
Lose half
Put it down to “poor fit”
Repeat
Most hiring processes are still measuring the wrong things.
Turns out it's not a candidate problem.
It's not a tech problem.
It's a selection problem.
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Most hiring assessments aren’t broken — they’re just optimised for internal efficiency.
But there’s a gap between what candidates are shown and what the job is actually like:
72% felt misled
66% left due to unmet expectations
That’s a design problem.
👉 hubs.la/Q04bVpxJ0