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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 dashboard merging fails with different Y-axis configurations because the platform can’t reconcile varying metric scales, different data types like percentages versus counts, or conflicting axis formatting from original dashboards. This typically results in visualization errors or generic axis settings.
The solution is preparing properly normalized data that can handle multiple Y-axis configurations before visualization.
Normalize metrics and prepare data for unified Y-axis visualization
CoefficientHubSpotHubSpot’sprovides complete control over metric presentation by importing raw data from alldashboard sources. You can create standardized metric calculations, normalize different data types, and build separate data ranges for different Y-axis requirements, unlikeautomatic merging of incompatible configurations.
How to make it work
Step 1. Import and standardize all metric data.
Use Coefficient’s HubSpot integration to import raw data from all dashboard sources. Create standardized metric calculations that convert percentages to decimal format, ensure count metrics use consistent units, and build separate columns for different metric types that require different Y-axis scaling.
Step 2. Create normalized metric versions.
Build calculated fields that create normalized versions of metrics for unified Y-axis scaling. For example, convert conversion rates to indexed values, create percentage-of-target columns for different metrics, or build metric hierarchies with primary and secondary indicators.
Step 3. Prepare multiple data ranges for different axis configurations.
Create separate data ranges in your spreadsheet for each Y-axis configuration needed, while maintaining consistent date and dimension columns across all ranges. This enables synchronized filtering while accommodating different metric scales and data types.
Step 4. Set up automated data preparation.
Use Coefficient’s scheduled refreshes to maintain proper metric scaling as underlying data values change over time. Apply conditional formatting and data validation to ensure metric consistency before visualization, and create documentation columns explaining transformation logic.
Build unified dashboards that handle multiple metric types properly
Start creatingPreparing properly formatted, compatible data eliminates Y-axis configuration conflicts that prevent effective dashboard merging.unified dashboard views that accommodate different metric scales and data types seamlessly.
