Support · Source connector

Intercom ELT Connector

Extract brings Intercom support data into data warehouses and databases for analysis with the rest of your business data. Teams use it to measure support workload, service quality, and customer experience. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.

View setup guide

Use cases

What teams use this connector for

Support performance reporting

Use Intercom data to measure workload, response patterns, resolution trends, and service quality.

Customer health analysis

Combine Intercom support activity with customer and product data to identify accounts needing attention.

Workload planning

Analyze Intercom volume and issue trends to plan staffing, routing, and operational follow-up.

Flexible reporting cadence

Refresh Intercom reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.

Warehouse and database analysis

Use Extract to make Intercom support data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.

Connector data

Available streams

Stream	Description	Sync mode
`company_segments`	Fields include company_id, id, _singular_extract_timestamp, created_at.	Incremental
`conversations`	Fields include id, _singular_extract_timestamp, admin_assignee_id, contacts.contacts.	Incremental
`segments`	Fields include id, _singular_extract_timestamp, created_at, name.	Incremental
`tags`	Fields include id, applied_by.id, _singular_extract_timestamp, applied_at.	Full refresh
`contact_tags`	Fields include contact_id, id, applied_by.id, _singular_extract_timestamp.	Full refresh
`data_attributes`	Fields include full_name, model, name, _singular_extract_timestamp.	Full refresh
`contacts`	Fields include id, _singular_extract_timestamp, android_app_name, android_app_version.	Incremental
`contact_notes`	Fields include contact_id, id, _singular_extract_timestamp, author.avatar.	Full refresh
`companies`	Fields include id, _singular_extract_timestamp, app_id, company_id.	Incremental
`contact_companies`	Fields include contact_id, id, _singular_extract_timestamp, app_id.	Incremental
`contact_segments`	Fields include contact_id, id, _singular_extract_timestamp, created_at.	Incremental

Schema

Data model at a glance

company_segments

company_idstring
idstring
_singular_extract_timestampstring
created_atstring
namestring
person_typestring
typestring
updated_atstring

conversations

idstring
_singular_extract_timestampstring
admin_assignee_idstring
contacts.contactsjson
contacts.typestring
conversation_parts.conversation_partsjson
conversation_parts.typestring
conversation_rating.created_atstring

segments

idstring
_singular_extract_timestampstring
created_atstring
namestring
person_typestring
typestring
updated_atstring

contact_tags

contact_idstring
idstring
applied_by.idstring
_singular_extract_timestampstring
applied_atstring
applied_by.typestring
namestring
typestring

data_attributes

full_namestring
modelstring
namestring
_singular_extract_timestampstring
admin_idstring
api_writableboolean
archivedboolean
created_atstring

+2 more

No verified relationships are available in this schema preview.

Connector pairings

Popular destinations

Commonly paired with Intercom in Extract workflows.

Why Extract

Why Extract for Intercom

Rust-native performanceUse Rust-native data movement to refresh Intercom reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
Full run transparencyInspect Intercom API activity, execution steps, and timing in Extract's run-level logs when data needs review.
ELT + Reverse ETL in oneCombine Intercom data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.

Setup

Auth method	OAuth sign-in
Required permissions	Read access to companies, contacts and selected workspace resources
Estimated setup time	Est. 5–10 min with access ready; excludes initial sync and approvals
Incremental sync	Full refresh or incremental changes, depending on stream and configuration
Historical backfill	No backfill setting; historical coverage depends on the stream and provider
Sync frequency	15 min, hourly, daily, or custom

View the full setup guide

FAQ

Frequently asked questions

How does Extract handle incremental and full Intercom syncs?

An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Stream schemas define full refresh, incremental changes. Behavior varies by stream and configuration. Extract records run details so teams can verify each sync.

Can I backfill historical Intercom data with Extract?

Historical coverage for Intercom: No connector-specific backfill parameter is exposed. Initial historical coverage depends on the stream and provider. Use the setup guide and run details to confirm the available source records and selected settings.

How can teams monitor Intercom data quality and freshness?

Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Intercom API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.

How often can Extract sync data from Intercom?

Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.

What does the Intercom data model look like in the destination?

The Available streams table describes the datasets Extract can read from Intercom. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.