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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 visualization capabilities can’t create heat maps or multi-dimensional time charts that show both hourly and daily patterns simultaneously, limiting your ability to identify specific hour-of-day patterns across different weekdays.
Here’s how to transform HubSpot timestamp data into sophisticated visual patterns that reveal exactly when to schedule staff for optimal coverage.
Build advanced time pattern visualizations with Coefficient
HubSpot’s native dashboard widgets only support basic charts with daily minimum granularity. By importing complete timestamp data, you can create sophisticated visualizations that combine HubSpot data with spreadsheet charting capabilities for multi-dimensional time analysis.
How to make it work
Step 1. Import complete ticket dataset with timestamps.
Pull all HubSpot tickets with full timestamp data, bypassing HubSpot’s daily reporting limitations. Make sure to include the complete “Create Date” field which contains hour, minute, and second details.
Step 2. Create time dimension columns.
Extract both hour components using =HOUR(timestamp) and weekday components using =WEEKDAY(timestamp). This builds a two-dimensional time analysis foundation with separate columns for hours (0-23) and days of the week.
Step 3. Build a pivot table matrix.
Create a pivot table with hours as rows and weekdays as columns, showing ticket counts in the intersecting cells. This reveals weekly patterns and identifies which hours are busiest on specific days.
Step 4. Generate heat map visualizations.
Use conditional formatting to create visual heat maps showing peak and low-volume periods across the entire week. Apply color scales where darker colors represent higher ticket volumes for easy pattern recognition.
Step 5. Set up automated pattern updates.
Schedule daily refreshes so your weekly pattern analysis continuously incorporates new ticket data. Your heat maps and charts will automatically update to reflect current trends without manual intervention.
Step 6. Create complementary chart types.
Build stacked bar charts showing daily hour-by-hour breakdowns and line charts comparing weekday versus weekend patterns. Multiple visualization types help identify different aspects of your ticket volume patterns.
Transform raw data into workforce planning insights
This approach transforms raw HubSpot timestamp data into actionable workforce planning insights that clearly identify when to schedule staff for optimal coverage. Start visualizing your ticket patterns today.
