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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 beta feature prevents timestamp data from being accessible in standard reporting, creating a significant gap for customer success teams tracking score changes over time.
Here’s a proven workaround that bypasses these timestamp restrictions and enables the time-series analysis HubSpot blocks.
Bypass timestamp restrictions with dynamic data extraction
Coefficient serves as an effective workaround by providing capabilities that HubSpot’s native reporting lacks. It extracts health score data directly from the API with timestamps intact, then enables advanced filtering and analysis in HubSpot spreadsheet environments.
How to make it work
Step 1. Set up dynamic filtering for customer segments.
Use Coefficient’s filtering capabilities with up to 25 filters and AND/OR logic to pull specific customer segments with their health score data and timestamps preserved. Point filter values to spreadsheet cells for flexible, changeable criteria.
Step 2. Configure automated trend calculations.
Set up Formula Auto Fill Down to automatically calculate health score changes, percentage movements, and trend indicators as new timestamped data imports. Create formulas for week-over-week changes like =(Current_Score-Previous_Score)/Previous_Score.
Step 3. Build conditional alert systems.
Configure automated Slack and email notifications triggered by cell value changes when health scores drop below thresholds. Set up alerts for significant score drops over specified time periods using Coefficient’s notification system.
Step 4. Create comprehensive data mapping.
Combine health score data with other HubSpot objects like deals, tickets, and activities using Coefficient’s association handling. This provides context behind score changes that’s impossible within CS space reporting limitations.
Transform static data into dynamic insights
This workaround transforms HubSpot’s timestamp-blocked health score data into a dynamic, time-aware dataset suitable for comprehensive customer success analysis and proactive intervention strategies. Start building your time-series health score tracking system today.
