# What Is Candidate Matching?

Canonical URL: https://skillsociety.com.au/blog/glossary/what-is-candidate-matching
Markdown URL: https://skillsociety.com.au/blog/glossary/what-is-candidate-matching/markdown
Published: 2026-07-07
Updated: 2026-09-16
Author: Alberto Cubeddu
Excerpt: Candidate matching helps recruiters compare candidate evidence with job requirements, often using AI to understand skills and context beyond exact keywords.

Candidate matching is the process of comparing a candidate's skills, experience, availability, location, and preferences against a role's requirements to identify likely fit.

Keyword search can miss candidates who use different words for the same capability. Matching becomes more useful when it understands adjacent skills, role context, and whether the evidence actually matters for the job.

## What to Distinguish

Matching identifies potential fit against a role. Screening checks the relevant evidence and uncertainties; hiring decisions consider the wider assessment. A match score needs its criteria, source material and limitations to be useful.

## A Practical Example

**Illustrative example:** A candidate who wrote 'customer success onboarding' may be a strong match for an implementation specialist role even if the job ad says 'client enablement'. A semantic matching workflow should surface the candidate but still let a person inspect the evidence.

## What to Measure

Use explicit definitions for your reporting. The following are practical measurement choices, not universal benchmarks or claimed product results.

| Measure | Definition | Interpretation |
| --- | --- | --- |
| Shortlist acceptance | Candidates accepted for the next stage by the manager ÷ candidates submitted for review × 100. | Use an agreed brief; a changing manager preference can change the rate without changing matching quality. |
| Reviewed false positives | Matched candidates found not to meet the criteria ÷ matched candidates sampled × 100. | Document which requirement failed and how the sample was chosen. |

## How to Apply It

Review both obvious matches and a sample of low-ranked candidates. Distinguish an absent skill from missing evidence, and verify availability or essential credentials through the appropriate human process.

## Common Mistakes

- Confusing a high match score with a hiring decision.
- Overweighting job titles instead of skills evidence.
- Hiding match logic from recruiters.
- Not measuring whether matched candidates progress after interview.

## Where Skill Society Fits

SkillSociety treats matching as evidence alignment, not an automatic decision. Recruiters can review why a person appears relevant and use structured follow-up to confirm fit.

[Book a demo](https://skillsociety.com.au/booking?utm_source=blog&utm_medium=cta&utm_campaign=what-is-candidate-matching) to discuss your screening workflow, evidence requirements and human review points.

## Related Reading

Continue with [semantic search in recruitment](/blog/glossary/what-is-semantic-search-in-recruitment).

## Further Reading

- [Phenom HR AI Glossary](https://www.phenom.com/blog/hr-ai-glossary-terms) - AI-powered HR terms, including candidate matching and talent intelligence.
- [Talroo AI in Hiring Glossary](https://www.talroo.com/blog/decoding-ai-the-recruiters-glossary-to-artificial-intelligence-in-hiring) - AI recruiting terms such as NLP, conversational AI, semantic search, AI agents, explainable AI, and algorithmic bias.
- [OECD Skills-First Report](https://www.oecd.org/en/publications/empowering-the-workforce-in-the-context-of-a-skills-first-approach_345b6528-en/full-report/skills-first-in-oecd-countries-concepts-trends-and-implications-for-the-labour-market_0d6ba66f.html) - skills-first labour market context and skill-based matching trends.
- [LinkedIn Skills-First Report](https://economicgraph.linkedin.com/research/skills-first-report) - skills-first hiring and labour market matching context.