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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.”
Publishing partners provide contact data in wildly different formats: some use full state names, others use mixed abbreviations, and many have inconsistent capitalization. HubSpot’s import process can’t handle these formatting variations, leading to validation errors and manual correction requirements.
Here’s how to standardize contact data from multiple publishing sources before import.
Standardize multi-source contact data using Coefficient
CoefficientHubSpotHubSpotprovides comprehensive tools for cleaning contact data from multiple publishing sources. Create standardized cleaning templates for each partner’s formatting patterns, then apply automated validation before uploading toor.
How to make it work
Step 1. Connect multiple partner data sources.
Use Coefficient’s integration capabilities to pull contact data from all your publishing partners into a single spreadsheet environment. This centralizes data cleaning instead of handling each source separately.
Step 2. Create comprehensive state lookup tables.
Build lookup tables that handle all variations you encounter: “Calif.” → “CA”, “California” → “CA”, “ca” → “CA”, and “CALIFORNIA” → “CA”. Include common misspellings and abbreviations specific to each partner’s data patterns.
Step 3. Apply partner-specific cleaning rules.
Use conditional logic to apply different cleaning approaches based on data source. Partner A might consistently use full state names, while Partner B uses mixed formats. Tailor your VLOOKUP formulas and validation rules accordingly.
Step 4. Set up automated exception handling.
Configure conditional formatting to flag unusual state entries that don’t match your lookup tables. This lets you process standard cases automatically while highlighting exceptions for manual review.
Step 5. Implement quality assurance validation.
Use data validation rules to verify all state formats meet HubSpot requirements before upload. Generate detailed reports showing what was cleaned and corrected, so you can track which partners consistently provide problematic data.
Transform weekly data challenges
Start cleaningThis approach converts the recurring challenge of cleaning inconsistent publishing partner data into a streamlined, automated process. You’ll maintain consistent contact list quality across all sources while reducing manual cleanup time.your partner data efficiently with Coefficient.
