Support Resolution Time Histogram
A histogram groups continuous numeric observations into adjacent ranges and counts how many observations fall in each range. This example uses customer-support ticket resolution time, with two-hour bins from zero to twelve hours. The bar heights show where resolution times cluster and whether a smaller group of tickets takes much longer. This is different from a bar chart of issue categories: the order of histogram bins comes from the numeric scale, and the bars touch because the ranges are continuous. Start with raw values or a clear frequency table, choose sensible equal-width bins, and label both the measure and the count. Adjust the bin width if the chart hides useful structure or becomes too noisy.
Open it in the AI editor with a prompt pre-filled — keep what works, change what doesn't.
Scenario
Understanding ticket-resolution times
Key decisions
- Metric: Measure from ticket creation to resolution.
- Bins: Group continuous time values into equal ranges.
- Service level: Compare the distribution with the stated response target.
When to reuse this
Use this histogram to explain a numeric distribution before choosing a service-level improvement focus.
Frequently asked questions
What data do I need for a histogram?
What is a bin?
Can I choose the bin width?
Tweak it with chat, export PNG/SVG, or fork it for your own use case.