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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 CS space provides limited visibility into individual components that drive customer health score changes, with native reporting unable to break down which specific metrics cause health score fluctuations.
Here’s how to build transparent component analysis that reveals the root causes of health score changes and enables targeted customer success interventions.
Analyze health score components through multi-source data correlation
Coefficient addresses this component analysis gap by importing health scores alongside the underlying metrics that typically influence them. It enables multi-source data integration from HubSpot to correlate health score changes with specific customer behaviors that HubSpot’s native reporting cannot provide.
How to make it work
Step 1. Import health scores with underlying component metrics.
Set up parallel imports for health scores and suspected component metrics including support ticket volume and resolution times, product engagement and usage data, communication frequency and response rates, and revenue and contract renewal information.
Step 2. Configure automated change detection analysis.
Use scheduled imports with Formula Auto Fill Down to automatically calculate period-over-period changes in each potential component metric. Create formulas like =(Current_Value-Previous_Value)/Previous_Value for percentage changes and correlation analysis between component changes and health score movements.
Step 3. Build component tracking with conditional formatting.
Create calculated columns showing percentage changes in each metric and use conditional formatting to highlight significant component changes. Build correlation matrices to identify which components most strongly predict health score changes using formulas like =CORREL(health_score_range, component_range).
Step 4. Enable advanced component analysis and alerts.
Set up time-lagged correlation analysis to identify leading indicators, component weight estimation through regression analysis, and threshold identification for each component that triggers health score changes. Create custom alert systems when critical components show negative trends.
Transform opaque scores into actionable insights
This approach transforms HubSpot’s opaque health score system into a transparent, actionable framework where customer success teams can understand and address the root causes of health score changes with targeted intervention strategies. Start building your component analysis system today.
