Ticket Distribution by Type
Ticket Distribution by Type: Breakdown of tickets by category.
Formula
Type share = Tickets of the type created in period / Ticket VolumeComputable definition
The canonical formula in the GASP formula grammar. The MCP server compiles it to SQL over the datasets listed below.
count_distinct(field.ticket_id) / total(metric.ticket_volume)Unit: percent (the formula yields an unscaled ratio)
Breakdowns
- Reported with one row per
ticket_typevalue, each read against the whole population (share of total)
Notes: Reported per ticket type: tickets of the type created in the period over Ticket Volume (all tickets created in the period). The numerator counts tickets as Ticket Volume does.
Data requirements
Grain: Per event, aggregate monthly · Frequency: Daily · Aggregation: aggregate (month) · Staged: no · Main source: Help Desk.
| Field | Dataset | Type |
|---|---|---|
created_at | ticket | timestamp |
ticket_id | ticket | string |
ticket_type | ticket | string |
Example SQL (Postgres)
SELECT _d.ticket_type AS ticket_type, (CAST((SELECT COUNT(DISTINCT ticket_id) FROM ticket WHERE created_at >= :period_start AND created_at < (:period_end + INTERVAL '1 day') AND ticket_type = _d.ticket_type) AS DOUBLE PRECISION) / NULLIF(((SELECT COUNT(DISTINCT ticket_id) FROM ticket WHERE created_at >= :period_start AND created_at < (:period_end + INTERVAL '1 day'))), 0)) AS ticket_distribution_by_type
FROM (SELECT 'how_to' AS ticket_type UNION ALL SELECT 'bug' AS ticket_type UNION ALL SELECT 'billing' AS ticket_type UNION ALL SELECT 'account' AS ticket_type UNION ALL SELECT 'feature_request' AS ticket_type UNION ALL SELECT 'other' AS ticket_type) _d From an agent: with the GASP MCP server, call
lookup_metric({ "name": "Ticket Distribution by Type" }) or generate_query({ "metric": "Ticket Distribution by Type", "adapter": "<your adapter JSON>" }).
What It Tells You
What's driving support volume. High "how-to" suggests onboarding or documentation gaps.