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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 duplicate detection problems that HubSpot can’t solve natively. “ABC Corp” and “ABC Corporation” are clearly the same company, but exact-match systems miss these duplicates completely.
Here’s how to set up sophisticated similarity algorithms that identify near-duplicates and provide confidence scores for manual review.
Set up intelligent similarity detection using Coefficient
Coefficient enables sophisticated similarity algorithms and pattern matching in a spreadsheet environment, catching near-duplicates that exact-match systems miss entirely.
How to make it work
Step 1. Prepare and standardize your data for 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 inconsistent spacing and capitalization. Generate comparison datasets for systematic analysis across your records.
Step 2. Create similarity detection formulas.
Use SOUNDEX functions for phonetic matching of similar-sounding names or company identifiers. Create partial matching with =IF(SEARCH(LEFT(B2,5),C2)>0,”SIMILAR”,”DIFFERENT”) for prefix similarity detection. Add pattern recognition using text functions to identify structured data variations like phone numbers or IDs.
Step 3. Implement advanced similarity algorithms.
Calculate percentage matching to determine similarity scores (e.g., 85% similar for “ABC Corp” vs “ABC Corporation”). Set up token-based analysis to compare individual words within compound field values. Create weighted scoring that assigns different importance to various parts of custom fields.
Step 4. Configure similarity thresholds and rules.
Set conservative thresholds at 95%+ similarity for high-confidence matches. Use aggressive detection at 70%+ similarity for broader duplicate identification. Apply context-specific rules with different thresholds for names versus addresses versus product codes.
Step 5. Set up multi-field similarity analysis.
Create composite scoring that combines similarity scores across multiple custom fields. Add cross-validation that requires similarity in 2+ fields for duplicate classification. Include exclusion logic that skips comparison when critical fields are empty.
Step 6. Implement automated monitoring and review workflows.
Schedule similarity analysis during off-peak hours for performance optimization. Configure alerts when high-probability similar duplicates are detected with confidence scores in notifications. Create human verification queues that present similar matches for manual review with batch processing capabilities.
Step 7. Add industry-specific similarity detection.
Detect customer name variations like “John Smith” vs “J. Smith” or “Jonathan Smith”. Identify company name similarities like “ABC Corporation” vs “ABC Corp” vs “ABC Inc”. Match address variations with different formatting or abbreviations. Find product description similarities with minor variations.
Catch duplicates that exact matching misses
This sophisticated similarity detection transforms basic duplicate identification into intelligent pattern recognition while maintaining accuracy through configurable confidence thresholds. Start with HubSpot and Coefficient to catch the duplicates hiding in your data.
