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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.”
Multi-criteria matching provides robust company deduplication, but HubSpot’s import tool only supports single-field matching, missing duplicates when companies have slight variations across multiple data points.
You’ll learn how to build weighted scoring algorithms that combine domain, phone, address, and name matching to catch duplicates that single-field matching would miss.
Build weighted matching systems using Coefficient
Coefficient enables sophisticated multi-criteria matching through weighted scoring formulas in spreadsheets. This catches duplicates that HubSpot’s single-field matching in HubSpot would miss, like “ABC Corp” with domain “abc-corporation.com” matching “ABC Corporation” with the same domain.
How to make it work
Step 1. Import comprehensive HubSpot data for matching.
Use Coefficient to pull company data including domains, phone numbers, addresses, and names. This creates your complete reference dataset for multi-criteria comparison.
Step 2. Create weighted scoring formulas.
Build composite matching logic: =IF(domain_match=TRUE,40,0) + IF(phone_match=TRUE,30,0) + IF(address_match=TRUE,20,0) + name_similarity_score. Domain gets highest weight (40 points) as the most reliable identifier.
Step 3. Set confidence thresholds for automated processing.
Define score ranges: 70+ points = high confidence match (auto-update), 50-69 points = medium confidence (manual review), below 50 = new record. Point threshold cells to spreadsheet values for easy adjustment.
Step 4. Use conditional exports based on matching scores.
Set up Coefficient’s export actions to handle different confidence levels automatically. High-confidence matches get UPDATE operations, while low scores get INSERT operations for new companies.
Catch duplicates that single-field matching misses
Multi-criteria matching with weighted scoring provides more accurate deduplication than HubSpot’s basic name matching, keeping your company database clean and reliable. Build sophisticated matching rules that work better than single-field approaches.
