• Produces modeled tables that leverage Intercom data from Fivetran's connector in the format described by this ERD.

• Enables you to better understand the performance, responsiveness, and effectiveness of your team's conversations with customers via Intercom. It achieves this by:

  • Creating an enhanced conversations table to enable large-scale reporting on all current and closed conversations
  • Enriching conversation data with relevant contacts data
  • Aggregating your team's performance data across all conversations
  • Providing aggregate rating and timeliness metrics for customer conversations to enable company-level conversation performance reporting

The following table provides a detailed list of all tables materialized within this package by default.

Each Quickstart transformation job run materializes 42 models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

To use this dbt package, you must have the following:

• At least one Fivetran Intercom connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift or PostgreSQL destination.

Include the following intercom package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/intercom
    version: [">=1.4.0", "<1.5.0"]

By default, this package runs using your destination and the intercom schema. If this is not where your Intercom data is (for example, if your Intercom schema is named intercom_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
  intercom:
    intercom_database: your_database_name
    intercom_schema: your_schema_name

If you have multiple Intercom connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the intercom_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  intercom:
    intercom_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your Intercom connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each Intercom source rather than one set of unioned models.

By default, this package defines one single-connection source, called intercom, which will be disabled if you are unioning multiple connections. This means that your DAG will not include your Intercom sources, though the package will run successfully.

To properly incorporate all of your Intercom connections into your project's DAG:

1. Define each of your sources in a .yml file in your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_intercom.yml file.

# a .yml file in your root project

version: 2

sources:
  - name: <name> # ex: Should match name in intercom_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    config:
      loaded_at_field: _fivetran_synced
      freshness: # feel free to adjust to your liking
        warn_after: {count: 72, period: hour}
        error_after: {count: 168, period: hour}

    tables: # copy and paste from intercom/models/staging/src_intercom.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use anchors to only do so once

Note: If there are source tables you do not have (see Step 4), you may still include them, as long as you have set the right variables to False.

2. Set the has_defined_sources variable (scoped to the intercom package) to True, like such:

# dbt_project.yml
vars:
  intercom:
    has_defined_sources: true

vars:
  intercom__company_history_pass_through_columns:
    - name: company_history_custom_field
      alias: new_name_for_this_field
      transform_sql:  "cast(new_name_for_this_field as int64)"
    - name:           "this_other_field"
      transform_sql:  "cast(this_other_field as string)"
    - name: custom_monthly_spend
    - name: custom_paid_subscriber
  # a similar pattern can be applied to the rest of the following variables.
  intercom__contact_history_pass_through_columns:
  intercom__conversation_history_pass_through_columns:
  intercom__article_history_pass_through_columns:

# dbt_project.yml

...
vars:
  intercom__using_articles: False # This disables all help center functionality
  intercom__using_collection_history: False # Also requires articles to be enabled
  intercom__using_help_center_history: False # Also requires articles and collection_history to be enabled
  intercom__using_contact_company: False
  intercom__using_company_tags: False
  intercom__using_contact_tags: False
  intercom__using_conversation_tags: False
  intercom__using_team: False

models:
    intercom:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.

vars:
    intercom_<default_source_table_name>_identifier: your_table_name 

Intercom V2.0 does not support API exposure to company-defined business hours. We therefore calculate all time_to metrics in their entirety without subtracting business hours.

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package.

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.