Overview #
Compatibility Match evaluates candidate fit by applying configured logic to collected responses.
It helps teams move from subjective judgment to repeatable and explainable decisions.
Why it matters #
- Improves consistency across evaluators
- Reduces manual triage time
- Makes acceptance/rejection rationale traceable
- Enables fast filtering before deeper human review
How it works #
Input layer #
Compatibility consumes structured inputs from:
- Form responses
- Option selections
- Required confirmation steps
- Metadata captured during the journey
Rules layer #
Configured criteria define how fit is measured:
- Positive indicators (increase confidence)
- Negative indicators (decrease confidence)
- Mandatory conditions (hard requirements)
Output layer #
The result is used to:
- Influence next-step routing
- Trigger a Path Outcome
- Support internal decisioning and reporting
Designing effective criteria #
Use observable signals #
Write criteria using measurable response values, not vague impressions.
Weight by business impact #
High-risk disqualifiers should have stronger impact than low-value preferences.
Separate hard vs soft checks #
- Hard checks: mandatory compliance, legal, eligibility
- Soft checks: preference fit, readiness signals, quality indicators
Validation checklist #
- Criteria reflect current business policy
- No duplicate/contradictory rules
- Required fields are actually collected in earlier steps
- Edge cases tested (missing, partial, conflicting data)
Common pitfalls #
- Overfitting logic to one campaign or region
- Too many low-value criteria
- Using criteria that candidates cannot realistically answer correctly
- Forgetting to review match logic after process changes