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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.”
Missing revenue data in Looker Studio campaign reports often stems from broken associations, incomplete UTM parameters, or overly restrictive filters, but identifying the root cause can be challenging without visibility into the data pipeline.
You’ll learn how to create an auditable troubleshooting process that transforms data quality issues from black box problems into transparent, manageable fixes.
Create transparent data validation with an auditable middle layer using Coefficient
CoefficientHubSpotprovides exceptional visibility for troubleshooting missingrevenue data by creating an auditable middle layer between HubSpot and Looker Studio. You can verify source data completeness, identify null values in critical fields, and validate associations before data reaches your dashboards.
This approach transforms troubleshooting from guesswork into a systematic process with data preview, formula testing, and historical comparison capabilities.
How to make it work
Step 1. Verify source data completeness in Google Sheets.
Import your HubSpot revenue data and immediately check row counts against HubSpot reports using =COUNTA(A:A) to verify all records imported. Use Coefficient’s filtering to isolate problem records and identify null values in critical revenue fields with formulas like =COUNTBLANK(amount_column). Validate date ranges to ensure no missing periods in your campaign data.
Step 2. Identify common data quality issues.
Check for missing associations between contacts and deals using =IF(ISBLANK(contact_id),”Missing Association”,”OK”) in a helper column. Validate UTM parameters with =IF(OR(ISBLANK(utm_campaign),ISBLANK(utm_source)),”Incomplete UTM”,”Complete”) to identify attribution gaps. Review filter criteria to ensure they’re not overly restrictive and excluding valid revenue data.
Step 3. Debug using spreadsheet validation formulas.
Create validation checks like =IF(AND(stage=”Closed Won”,amount=0),”Revenue Error”,”OK”) to identify deals that should have revenue but don’t. Use =COUNTIFS(campaign_column,A2,amount_column,”>0″) to verify campaign attribution is working correctly. Build data quality scorecards that highlight problematic records before they reach Looker Studio.
Step 4. Set up ongoing data quality monitoring.
Create alerts that trigger when data quality issues occur, such as when revenue totals drop unexpectedly or when key campaign data is missing. Use Coefficient’s snapshot feature to compare current data with historical baselines and identify when problems began. Set up automated notifications via Slack or email when data anomalies are detected.
Turn data troubleshooting into a systematic process
Start buildingTransparent data validation eliminates guesswork from troubleshooting missing revenue data. With visible data processing, formula testing, and automated quality checks, you can identify and fix issues before they impact business decisions.better data quality processes with Coefficient today.
