How do I analyse support ticket volume trends with iDBQuery?
Ask iDBQuery 'How has ticket volume trended over the last year, by category and channel?' It aggregates your support data over time, returns the trend and any spikes as a chart, and cites each period's count back to the tickets that make it up.
Ticket-volume trends show how demand on your support team changes over time and what drives spikes. Spotting them early lets you staff and fix root causes before customers feel the pain. iDBQuery surfaces them from your support data in seconds.
- You'd ask iDBQuery: 'Show weekly support ticket volume over the last year by category and channel, and flag the biggest spikes.'
- It aggregates tickets by period and dimension, returns a trend chart with spikes highlighted, and provides the counts in a table.
- Each count is cited to its source rows, so 'the March spike was 1,900 tickets' drills into the exact tickets.
The value is in explaining the spikes without a data-team ticket of your own. Ask 'What category drove the March jump?' and iDBQuery keeps the thread, decomposing the increase. Because ticket data, product releases, and outage logs may live in different systems, the one live model lets you correlate a volume spike with a release date in the same answer. Save a live volume report for the weekly support review, and let the Analyst agent investigate whether a rise is a genuine trend or a one-off, and which topic is behind it, with the rows to back it. This pairs with SLA and CSAT analysis for a complete picture of support health.
Updated 2026-06-22