The CS Tickets table tracks support ticket lifecycle events with agent, channel, tag, and satisfaction survey data. One row per ticket event (created, closed, reopened).

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Table Guide

What You Can Answer With This Table

• How many tickets are being created vs. closed over time? — filter by event_type, group by event_date
• How do satisfaction scores vary by agent or channel? — use survey_score, group by assignee_user_name or channel
• Which products or orders are associated with support tickets? — use products_names, total_order_value, has_refund

Before You Query

• Required field: event_date
• Event-driven grain: Each ticket lifecycle event (created, closed, reopened) generates a separate row. A single ticket may appear multiple times. Filter by event_type to isolate specific events (e.g., ticket-created for new ticket volume).
• Order attribution window: The orders, products_names, skus, and related columns include orders placed up to 30 days before ticket creation. Tickets without a linked customer have empty order attribution.

Key Relationships

Table	Join Key	What the Join Enables
Customers	customer_id	Customer profile data (lifetime value, total orders, location, marketing consent)

When to Use a Different Table

• Need individual message content → CS Messages table. CS Messages stores each message in a conversation; CS Tickets stores ticket-level metadata and lifecycle events.
• Need a full event-level audit log → CS Events table. CS Events captures every event type (assigned, unassigned, updated, message-created); CS Tickets only surfaces created, closed, and reopened events.
• Need aggregated daily support KPIs → Customer Support Agg Metrics table (BETA). Agg Metrics provides pre-calculated daily response and resolution times without ticket-level detail.

View the full Triple Whale Data Ontology →

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Dimensions

Dimensions are immutable properties that can be used for grouping data.