How do you analyze customer support data in Metabase?
Help desks like Zendesk and Intercom hold the record of every customer conversation your team handles. To analyze them in Metabase, you sync the tool into a database, map its objects to a shared support model, and build dashboards on top. Metabase has no native connector for these tools, so the sync comes first.
Which tools does this cover?
This pattern applies to help desks and shared inboxes, including:
- Zendesk — tickets, comments, SLA policies, satisfaction ratings
- Intercom — conversations, parts, Fin AI resolutions, contacts
- Front — shared-inbox conversations, comments, assignments
- Freshdesk — tickets, conversations, agents, groups, CSAT
- Gorgias — ecommerce tickets tied to orders and customers
- Kustomer — omnichannel conversations and timelines
- Pylon — B2B issues across Slack, Teams, and email
- Plain — threads, customers, and tenants via a GraphQL API
- LiveAgent — multi-channel tickets and call logs
- Jitbit — IT help desk tickets and categories
- Crisp — live-chat conversations and sessions
- Drag — Gmail shared-inbox cards and boards
What is the shared support data model?
Almost every tool maps onto these entities. Model them as clean tables, not raw connector JSON:
| Concept | Zendesk term | Intercom term | Used for |
|---|---|---|---|
| Ticket / conversation | Ticket | Conversation | The unit of customer work |
| Message | Comment | Conversation part | Response times |
| Agent | Agent | Admin/teammate | Workload |
| Customer | Requester | Contact | Volume by account |
| Tag | Tag | Tag | Drivers, topics |
| Status / SLA event | Audit / SLA policy | State change | Resolution time, breaches |
| Satisfaction | Satisfaction rating | Conversation rating | CSAT |
The single most important field is a reliable status/SLA history. With it you can compute true resolution time, time-in-status, reopen rate, and SLA attainment. Without it, those metrics must be caveated.
How do you connect a help desk to Metabase?
Metabase has no native connector for these tools, so the reliable path is a pipeline: sync the tool into a database with a managed connector, dlt, or the API on a schedule, then build durable dashboards on the modeled tables. See our guide to building a data pipeline for the full walkthrough — free or managed.
See the per-tool setup on the Zendesk and Intercom pages, or the support analytics overview for the full pattern.
What can you analyze across support tools?
- Responsiveness — first-response time, resolution time
- Volume & backlog — created vs. solved, open work aging
- SLA — attainment and breaches against your targets
- Quality — CSAT, reopen rate
- Drivers — load by channel, tag/topic, and account
Which dashboards should you build?
- Support overview — volume, FRT, resolution time, backlog, CSAT
- SLA & response time — attainment, breaches, aging
- CSAT & quality — satisfaction trend, reopen rate, drivers by tag
- Agent & team performance — workload balance and handle time