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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.”
HubSpot’snative duplicate detection only handles exact or very close matches and can’t compare against external Excel lead data. You need sophisticated similarity algorithms to identify companies like “ABC Corporation” vs “ABC Corp” or “Smith & Associates” vs “Smith and Associates LLC”.
Here’s how to build advanced similarity scoring that catches company name variations that exact matching misses.
Build sophisticated company name similarity detection using Coefficient
Coefficientenables advanced fuzzy company name matching by providing comprehensive HubSpot company data that you can analyze with Excel similarity algorithms. You’ll work with complete datasets that include related fields for multi-factor validation.
How to make it work
Step 1. Import comprehensive HubSpot company data.
Pull HubSpot company names along with related fields like domain, industry, and employee count using Coefficient’s custom field selection. This supports similarity matching beyond just name comparison and helps validate potential matches.
Step 2. Create Levenshtein distance approximation formulas.
Build character-level similarity scoring using Excel functions: `=1-((LEN(A2)+LEN(B2)-2*LEN(SUBSTITUTE(SUBSTITUTE(UPPER(A2),” “,””),UPPER(B2),””)))/(MAX(LEN(A2),LEN(B2))))`. This creates similarity scores between 0-1 where higher scores indicate better matches. Scores above 0.8 typically indicate strong similarity.
Step 3. Implement word-based matching algorithms.
Use SEARCH and FIND functions to identify common words between company names, accounting for word order variations: `=IF(AND(ISNUMBER(SEARCH(“ABC”,UPPER(B2))), ISNUMBER(SEARCH(“CORP”,UPPER(B2)))), “Word Match”, “”)`. This catches matches where key words appear in different positions.
Step 4. Handle business abbreviation standardization.
Create SUBSTITUTE functions that standardize common business abbreviations before comparison: `=SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(UPPER(A2),”CORPORATION”,”CORP”),”INCORPORATED”,”INC”),”LIMITED LIABILITY COMPANY”,”LLC”)`. This ensures “ABC Corporation” matches “ABC Corp” reliably.
Step 5. Set up dynamic similarity thresholds.
Use Coefficient’s dynamic filtering to point similarity threshold values to spreadsheet cells. This allows real-time adjustment of matching sensitivity without rebuilding formulas. Set different thresholds for different matching scenarios (0.9 for high confidence, 0.7 for review required).
Step 6. Add multi-field similarity validation.
Combine company name similarity with domain matching using Coefficient’s association handling. Import company domains alongside names for additional validation: `=IF(AND(name_similarity>0.7, domain_match=TRUE), “High Confidence”, “Review Required”)`. This reduces false positives significantly.
Step 7. Create color-coded similarity scoring.
Set up conditional formatting based on similarity score ranges: Green for high similarity (>0.8) indicating likely matches, Yellow for medium similarity (0.6-0.8) requiring manual review, Red for low similarity (<0.6) indicating probably different companies.
Catch company name variations that exact matching misses
Start buildingAdvanced similarity algorithms provide far more nuanced company name detection than basic duplicate management. You’ll identify potential duplicates with common business name variations and abbreviations.sophisticated similarity matching today.
