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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 deal names often contain contact information that can be extracted for retroactive associations. You can use advanced pattern recognition and text extraction formulas to recover emails, names, and phone numbers from deal names, then create proper contact associations that HubSpot’s native tools cannot achieve.
This approach transforms unstructured deal names into actionable contact data for comprehensive relationship building.
Build advanced pattern recognition for contact extraction using Coefficient
Coefficient’s spreadsheet environment excels at pattern recognition and text extraction from deal names. You can analyze naming patterns, build specialized extraction formulas, and create automated association workflows that recover contact data trapped in deal names.
How to make it work
Step 1. Import deals and analyze naming patterns.
Import all HubSpot deals with names and existing associations. Analyze naming patterns to identify extraction opportunities like “John Smith – ABC Company – Widget Deal”, “New Deal – jane@email.com – 2024”, or “ABC Corp (john.doe@abc.com) – Enterprise”. Build a pattern library for common formats.
Step 2. Create specialized extraction formula suite.
Build email extraction: `=REGEXEXTRACT(A2,”([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})”)`. Create name extraction: `=REGEXEXTRACT(A2,”^([A-Z][a-z]+ [A-Z][a-z]+)”)` for “First Last” format. Add company extraction: `=TRIM(REGEXEXTRACT(A2,”- ([^-]+) -“))` for dash-separated formats.
Step 3. Build validation and confidence scoring.
Cross-reference extracted emails with existing contacts using XLOOKUP. Validate email formats with REGEXMATCH. Create confidence scores: direct email found = 100%, full name found = 70%, partial match = 40%. Flag extractions below 70% confidence for manual review.
Step 4. Implement extraction fallback logic.
Create combined extraction with fallbacks: `=IFS(LEN(B2)>0,B2,LEN(C2)>0,CONCATENATE(LOWER(SUBSTITUTE(C2,” “,”.”)),”@company.com”),TRUE,”NO_CONTACT_INFO”)` where B2 is email extract and C2 is name extract. This maximizes extraction success rates.
Step 5. Execute automated association creation.
For high-confidence matches, auto-create contacts if needed and associate with existing contacts. Update deal names to remove redundant information. For low-confidence matches, export to review queue and send daily digest to sales ops team for manual resolution.
Recover contact data trapped in deal names
This systematic extraction approach transforms unstructured deal names into proper contact associations that would require extensive manual work through HubSpot’s interface. You get automated pattern recognition plus continuous improvement capabilities. Start extracting contact information from your deal names today.
