fivetran / dbt_intercom

Data models for Fivetran's Intercom connector built using dbt.

Intercom Transformation dbt Package (Docs)

📣 What does this dbt package do?

• Produces modeled tables that leverage Intercom data from Fivetran's connector in the format described by this ERD and builds off the output of our Intercom source package.

• 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 models materialized within this package by default.

Model	Description
intercom__admin_metrics	Each record represents an individual admin (employee) and a unique team they are assigned on, enriched with admin-specific conversation data like total conversations, average rating, and median response times by specific team.
intercom__company_enhanced	Each record represents a single company, enriched with data related to the company industry, monthly spend, and user count.
intercom__company_metrics	Each record represents a single row from intercom__company_enhanced, enriched with data like total conversation count, average satisfaction rating, median time to first response, and median time to last close with contacts associated to a single company.
intercom__contact_enhanced	Each record represents a single contact, enriched with data like the contact's role, company, last contacted information, and email list subscription status.
intercom__conversation_enhanced	Each record represents a single conversation, enriched with conversation part data like who was assigned to the conversation, which contact the conversation was with, the current conversation state, who closed the conversation, and the final conversation ratings from the contact.
intercom__conversation_metrics	Each record represents a single row from intercom__conversation_enhanced, enriched with data like time to first response, time to first close, and time to last close.

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

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

Step 2: Install the package

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: [">=0.9.0", "<0.10.0"]

Step 3: Define database and schema variables

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_database: your_database_name
    intercom_schema: your_schema_name

(Optional) Step 4: Additional configurations

Expand for configurations

Adding passthrough metrics

You can add additional columns to the intercom__company_enhanced, intercom__contact_enhanced, and intercom__conversation_enhanced tables using our pass-through column variables. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables in your root dbt_project.yml.

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:

Disabling Models

This package assumes that you use Intercom's company tag, contact tag, contact company, and conversation tag, team and team admin mapping tables. If you do not use these tables, add the configuration below to your dbt_project.yml. By default, these variables are set to True:

# dbt_project.yml

...
vars:
  intercom__using_contact_company: False
  intercom__using_company_tags: False
  intercom__using_contact_tags: False
  intercom__using_conversation_tags: False
  intercom__using_team: False

Changing the build schema

By default this package will build the Intercom staging models within a schema titled (<target_schema> + _stg_intercom) and the Intercom final models with a schema titled (<target_schema> + _intercom) in your target database. If this is not where you would like your modeled Intercom data to be written to, add the following configuration to your dbt_project.yml file:

models:
  intercom:
    +schema: my_new_schema_name # leave blank for just the target_schema
  intercom_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    intercom_<default_source_table_name>_identifier: your_table_name 

Limitations

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.

🔍 Does this package have dependencies?

This dbt package is dependent on the following dbt packages. Please be aware that 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/intercom_source
      version: [">=0.8.0", "<0.9.0"]

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

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

🙌 How is this package maintained and can I contribute?

Package Maintenance

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.

Contributions

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!

🏪 Are there any resources available?

• If you have questions or want to reach out for help, please refer to 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.
• Have questions or want to be part of the community discourse? Create a post in the Fivetran community and our team along with the community can join in on the discussion!