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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’s native import validation only catches basic format errors after upload, potentially corrupting your CRM with bad data that creates downstream reporting problems and sales team confusion.
Here’s how to implement comprehensive data validation that catches quality issues before any records reach your CRM, maintaining data integrity from the start.
Implement comprehensive pre-import validation using Coefficient
Coefficient enables comprehensive sales data validation through spreadsheet-based data cleaning that happens before any records reach HubSpot . This approach maintains CRM data integrity from the start, preventing the downstream reporting errors that plague HubSpot post-import cleanup workflows.
How to make it work
Step 1. Set up validation columns with formulas checking each data quality rule.
Create comprehensive validation formulas: `=IF(ISERROR(FIND(“@”,B2)),”INVALID_EMAIL”,”VALID”)` for email format validation, `=IF(LEN(C2)<10,"INVALID_PHONE","VALID")` for phone number checks, and `=IF(D2<0,"INVALID_AMOUNT","VALID")` for currency validation.
Step 2. Create conditional formatting to visually identify validation failures.
Use conditional formatting to highlight problematic records with red backgrounds for failed validations. Set up rules that automatically color-code records based on validation column results, making data quality issues immediately visible.
Step 3. Import existing HubSpot data for cross-validation against new records.
Pull current HubSpot records into reference sheets for duplicate detection: `=IF(ISERROR(VLOOKUP(A2,ExistingData!A:A,1,FALSE)),”NEW”,”DUPLICATE”)`. This prevents duplicate records from entering your CRM during bulk imports.
Step 4. Configure Coefficient exports to only include records passing all validations.
Set up conditional exports that only process records marked as “VALID” across all validation checks. Use formulas like `=IF(AND(E2=”VALID”,F2=”VALID”,G2=”VALID”),”EXPORT”,”SKIP”)` to control which records reach HubSpot.
Step 5. Set up alerts when validation failure rates exceed acceptable thresholds.
Use Slack and Email Alerts to notify your team when validation failure rates spike above normal levels. Configure alerts based on summary calculations that track validation statistics across your daily imports.
Maintain pristine CRM data quality
This pre-validation approach prevents bad data from entering your system, reducing downstream reporting errors and sales team confusion caused by inconsistent data. Start implementing comprehensive sales data validation today.
