Glossary

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Glossary / Evaluation and implementation guide

Record Matching

Record matching is the concrete mechanics of deciding whether two records represent the same entity, and it is the operational core of deduplication.

Matching rules can be exact (identical email), fuzzy (similar company names, transposed phone digits) or rule-based (same domain plus same country).

Unlike identity resolution, which links behavioral signals to people, record matching operates on structured records in systems like CRMs and marketing databases.

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A practical example

Example: an import adds 'Globex SA' with domain globex.es while your CRM already holds 'Globex, S.A.' with the same domain.

A fuzzy matching rule keyed on email domain flags the pair for merge review instead of creating a duplicate account.

What to evaluate before investing

  • Ask which fields can serve as match keys and whether fuzzy thresholds are configurable per field.
  • Test behavior on edge cases you actually have: accents, legal suffixes, free email domains, merged subsidiaries.
  • Confirm whether matches are auto-merged, queued for review or only flagged, and whether bulk unmerge is supported.

Limitations and tradeoffs

Aggressive matching reduces duplicates but increases false merges; conservative settings leave duplicates in place, so thresholds are a genuine tradeoff, not a setting to maximize.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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