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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Fuzzy matching for similar custom field values represents one of the most challenging aspects of duplicate detection that HubSpot simply cannot address natively.
Here’s how to set up sophisticated similarity algorithms and pattern matching that identify near-duplicates missed by exact-match systems.
Implement fuzzy matching for similar duplicates using Coefficient
Coefficient’s spreadsheet environment enables sophisticated similarity algorithms and pattern matching for HubSpot custom fields. You can calculate character-level differences, implement phonetic matching, and set configurable similarity thresholds that catch duplicates human reviewers might miss in HubSpot .
How to make it work
Step 1. Prepare data for similarity analysis.
Import HubSpot records with target custom fields for similarity analysis. Create standardized versions using text cleaning formulas like TRIM, UPPER, and SUBSTITUTE to remove inconsistencies. Generate comparison datasets for systematic analysis across all records.
Step 2. Create similarity detection formulas.
Implement partial matching with: =IF(SEARCH(LEFT(B2,5),C2)>0,”SIMILAR”,”DIFFERENT”) for prefix similarity. Use SOUNDEX functions for phonetic matching of similar-sounding names or company identifiers. Calculate percentage similarity scores using character comparison formulas.
Step 3. Set up configurable similarity thresholds.
Configure conservative approach with 95%+ similarity for high-confidence matches. Set aggressive detection at 70%+ similarity for broader duplicate identification. Apply context-specific rules with different thresholds for names vs. addresses vs. product codes.
Step 4. Implement automated similarity monitoring and review workflow.
Schedule similarity analysis during off-peak hours for performance optimization. Configure alerts when high-probability similar duplicates are detected with confidence scores included. Create human verification queues for manual review of similarity matches before final action.
Catch duplicates that exact matching misses
This sophisticated similarity detection transforms basic duplicate identification into intelligent pattern recognition with configurable confidence levels. Start detecting similar duplicates that traditional exact-match systems completely miss.
