Agents

Compatibility Match

Score and evaluate fit based on Candidate responses and configured criteria.

Overview #

Compatibility Match evaluates how well a Candidate fits your requirements. Instead of reviewing every Candidate manually, you define criteria and the system scores Candidates automatically based on their responses.

This helps you:

  • Quickly identify strong Candidates
  • Make consistent, repeatable decisions
  • Save time on manual screening
  • Keep a clear record of why a Candidate scored the way they did

How It Works #

Compatibility Match works in three stages:

1. Collect Information #

As the Candidate moves through the Journey, they complete Steps that collect data — such as forms, options, and responses. This data becomes the input for scoring.

2. Apply Your Criteria #

You define what makes a good fit and what raises concerns:

  • Positive signals — Increase the score (e.g., "Has 5+ years experience")
  • Negative signals — Decrease the score (e.g., "Not available for 6 months")
  • Must-have requirements — Hard requirements that must be met (e.g., "Must have valid certification")

3. Generate a Score #

The system evaluates the Candidate's responses against your criteria and produces:

  • An overall score
  • A label (e.g., Strong match, Moderate match, Needs review)
  • A breakdown showing which Steps contributed to the score

How to Set Up Good Criteria #

Be Specific #

Write clear, measurable criteria. Avoid vague terms.

  • ✅ "Has managed a team of 5+ people"
  • ❌ "Seems like a good leader"

Prioritise What Matters Most #

Give higher importance (weight) to criteria that have the biggest impact on your decision. Lower-priority criteria should have less weight.

Separate Must-Haves from Nice-to-Haves #

  • Must-haves — Certification, eligibility, compliance requirements
  • Nice-to-haves — Experience level, availability, preferences

Before You Go Live — Check This #

  • Your criteria reflect your current requirements (not outdated policies)
  • You are not using conflicting criteria
  • The data needed for scoring is actually collected in earlier Steps
  • You have tested with sample Candidates to verify the scoring makes sense

Common Mistakes #

  • Defining too many low-importance criteria (dilutes the score)
  • Using criteria that Candidates cannot realistically answer
  • Forgetting to update criteria when your requirements change
  • Not testing the scoring with realistic data before going live

Last updated May 4, 2026