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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 daily-only reporting granularity completely obscures peak hour identification because it aggregates all 24 hours into single data points, making it impossible to identify when staffing should be concentrated.
Here’s how to bypass HubSpot’s daily reporting limitations by accessing raw timestamp data to identify statistically significant peak hours for optimal staffing decisions.
Identify statistical peaks with Coefficient
HubSpot can’t break down daily totals to show which specific hours drive high-volume days. By importing raw timestamp data, you can circumvent daily aggregation limitations and perform sophisticated peak analysis using HubSpot ticket data.
How to make it work
Step 1. Import raw timestamp data.
Import all HubSpot tickets with complete “Create Date” timestamps, circumventing the daily aggregation limitation entirely. This gives you access to the granular data that HubSpot’s reports hide.
Step 2. Create hourly frequency distributions.
Extract hours using =HOUR(timestamp) and create frequency distributions showing ticket counts for each hour (0-23). Use pivot tables or COUNTIFS formulas to count tickets by hour.
Step 3. Calculate statistical peak thresholds.
Calculate averages and standard deviations by hour to identify statistically significant peaks. Use formulas to identify hours with volumes >1.5 standard deviations above the mean as true peaks.
Step 4. Analyze day-specific peak patterns.
Use =WEEKDAY(timestamp) to analyze peaks separately for different days of the week. Monday peaks might occur at different hours than Friday peaks, requiring different staffing strategies.
Step 5. Create peak intensity scoring.
Build formulas ranking hours by intensity using =(hourly_volume – daily_average) / daily_average * 100 to quantify peak severity. This helps prioritize which peaks need the most attention.
Step 6. Set up automated peak detection alerts.
Configure alerts that trigger when current hour volume exceeds historical peak thresholds, enabling real-time staffing adjustments. This provides proactive notification of unusual volume spikes.
Step 7. Track peak trends over time.
Compare monthly peak hour patterns to identify seasonal or business-driven changes. Use conditional formatting to highlight how peak hours shift over time.
Transform daily data into peak hour intelligence
This transforms HubSpot’s limited daily data into actionable peak hour intelligence that directly supports optimal staffing decisions and resource allocation. Start identifying your peak hours today.
