For years, the biggest pain point in talent acquisition has been the same: too many unsuitable candidates. It’s a volume problem compounded by a low-quality problem. Recruiters are forced to sift through mountains of applications, using outdated systems that rely on opaque matching to determine who gets an interview.
The result?
Frustrated recruiters, ghosted jobseekers, and an enormous waste of time and resources. We believe the solution lies not just in better technology, but in radical transparency and comprehensive data.
Here is the framework for a new, hybrid scoring system.
Transparency and treating the jobseeker like an educated consumer. We share candidate scores with them.
1. Suitability: fit based on multiple parameters including skills, education, and experience.
2. Correct Work Domain: Specific validation that the candidate has worked within the required industry segment.
By giving the jobseeker their score, we achieve a powerful alignment of incentives: they are encouraged to be more selective and only apply where they score well. This shifts their focus from mass-applying to:
• Deep Research: Understanding the company’s needs.
• Customization: Tailoring their résumé specifically to the role.
• Follow-Up: Engaging professionally post-application.
The immediate benefit for the hiring company is a cleaner, higher-intent applicant pool, allowing high-scoring individuals to be actively engaged immediately.
The Hybrid Scoring Model:
Objective vs. Subjective
Traditional scoring often falls into one of two traps:
• Subjective Scoring: Based on opinion (e.g., “how many points for living 5km away?”). This is easy to implement but impossible to properly validate.
• Pure Objective Scoring: Analytical and defensible, but incredibly hard to achieve and scale.
Our solution is a hybrid model. It combines analytically derived parameters (things like specific tenure, technical system history, and domain experience) with AI data science, operating within a validated “black box” system. This comprehensive approach looks at every variable a skilled recruiter would investigate: specific work domain experience, management history, proximity, and educational alignment. It moves beyond matching.
An Ecosystem of Modifiers and Defensive Measures
To ensure the score isn’t the sole basis for a hire, we have built an ecosystem of modifiers that provide essential context and vetting:
• AI Fraud Detection: tools now allow jobseekers to hyper-optimize résumés to match job descriptions perfectly—often exaggerating or faking qualifications. Our AI fraud modifier acts as a crucial defensive measure against these attempts to “game the system.”
• Custom Questions generated by AI: Six simple, yes/no questions provide quick, high-value data points that help the hiring professional identify better candidates.
• Vetting Services: We integrate high-value services like reference interviews and background checks, recognizing that a conversation with a former supervisor is the most valuable data point in recruiting.
Conclusion
The exponential growth of AI means the volume of applications will only increase. Advanced, validated, and comprehensive scoring systems are no longer a luxury—they are a necessity for productivity and efficiency.
By adopting a system like the Geescore—one that is transparent to the jobseeker, comprehensive in its data inputs, and supported by a suite of high-value modifiers—companies can finally transition from drowning in applications to engaging with the highest-quality candidates first.
Ready to clean up your talent acquisition funnel?
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