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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 Excel import often provides limited visibility into contact matching failures until after import completion. When contacts don’t match existing records, you typically get cryptic error messages without clear guidance on which specific records failed or why.
Here’s how to proactively identify and resolve contact matching issues before any data touches HubSpot.
Prevent contact matching errors with proactive validation using Coefficient
CoefficientHubSpotprovides superior error handling by letting you identify and resolve contact matching issues in the spreadsheet environment before executing anyoperations. This prevents the reactive cleanup typically required after failed imports.
How to make it work
Step 1. Import existing HubSpot contacts for reference validation.
Pull all your HubSpot contacts with Contact IDs, email addresses, and any other identifiers you’ll use for matching. This creates a complete reference database for validation.
Step 2. Create match verification formulas in Google Sheets.
Use VLOOKUP or INDEX/MATCH to identify which contacts in your Excel data have existing HubSpot records: =IF(ISERROR(VLOOKUP(B2,HubSpot_Contacts!B:A,1,FALSE)),”NEW”,”EXISTING”). This flags each contact as new or existing before any import attempts.
Step 3. Build validation columns for common error sources.
Create columns that flag potential issues: invalid email formats with =IF(ISERROR(FIND(“@”,C2)),”Invalid Email”,”Valid”), missing required fields with =IF(D2=””,”Missing Data”,”Complete”), and duplicate entries within your import data.
Step 4. Separate matched and unmatched contacts into different operations.
Create separate datasets for contacts that matched existing HubSpot records (for UPDATE operations) and new contacts (for INSERT operations). This prevents the mixed-operation errors that cause many import failures.
Step 5. Execute staged processing with error tracking.
Process matched and unmatched contacts in separate Coefficient operations. This lets you resolve matching issues for one group without affecting successful updates for the other group.
Step 6. Create error resolution workflows.
For contacts that don’t match existing records, use spreadsheet formulas to suggest potential matches based on similar email domains or names, making manual review more efficient.
Stop playing import error cleanup
Start preventingProactive error prevention beats reactive cleanup every time. By identifying contact matching issues before import, you save hours of manual data cleanup and ensure higher success rates.import errors today.
