Mailchimp dbt Package

This dbt package transforms data from Fivetran's Mailchimp connector into analytics-ready tables.

Resources

• Number of materialized models¹: 33
• Connector documentation
  • Mailchimp connector documentation
  • Mailchimp ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog
• dbt Core™ supported versions
  • >=1.3.0, <3.0.0

What does this dbt package do?

This package enables you to transform recipient and activity tables into analytics-ready models and provide aggregate metrics about campaigns, automations, lists, members, and segments. It creates enriched models with metrics focused on email performance, member engagement, and campaign effectiveness.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_mailchimp

Final output tables

By default, this package materializes the following final tables:

Table	Description
mailchimp__automations_activities	Tracks individual user activities (opens, clicks, bounces) for automation emails with timestamp, IP, URL, and bounce type details to analyze automation engagement patterns and troubleshoot delivery issues. Example Analytics Questions: Which automated emails are driving the most subscriber engagement (opens and clicks)? Where are delivery issues occurring across our automation workflows? What content and links resonate most with different audience segments in our automated emails?
mailchimp__automation_emails	Provides detailed automation email profiles with timing (created, started, sent), delay settings, tracking configurations, subject lines, status, and engagement metrics (sends, opens, clicks, unsubscribes) to optimize automation workflows and email performance. Example Analytics Questions: Which emails in our automation sequences have the strongest performance? How does email timing impact subscriber engagement and retention? Are there specific workflow positions where subscribers tend to disengage?
mailchimp__automations	Summarizes automation workflows with timing (created, started), status, trigger settings, list and segment targeting, and aggregate engagement metrics (sends, opens, clicks, unsubscribes) to measure automation effectiveness and ROI. Example Analytics Questions: Which automation workflows generate the best ROI for our email marketing? How quickly do our automations convert after being activated? Which audience triggers and segments respond best to automated campaigns?
mailchimp__campaign_activities	Chronicles individual user activities (opens, clicks, bounces) for campaign emails with send timing, response lag metrics (minutes, hours, days), IP addresses, URLs, and bounce types to analyze campaign engagement timing and patterns. Example Analytics Questions: When are subscribers most likely to engage with our campaign emails? Which campaigns and content drive the fastest response from our audience? What delivery problems are affecting campaign performance?
mailchimp__campaign_recipients	Tracks campaign email sends at the recipient level with engagement metrics (opens, clicks), engagement flags (was_opened, was_clicked, was_unsubscribed), and time-to-open calculations to analyze individual recipient behavior and response timing. Example Analytics Questions: How engaged are recipients with our campaigns across different audience segments? How quickly do subscribers respond to our emails after receiving them? Which campaigns successfully convert passive readers into active clickers?
mailchimp__campaigns	Consolidates campaign profiles with timing, list/segment targeting, campaign type, content settings, A/B test configurations (test_size, wait_time, winner_criteria), and comprehensive engagement metrics (sends, opens, clicks, unsubscribes) to measure campaign performance and optimize future sends. Example Analytics Questions: Which campaign types and strategies deliver the strongest engagement? Are our A/B tests helping us improve campaign performance? How does campaign preparation time impact our send schedule and results?
mailchimp__lists	Provides comprehensive list profiles with contact details, subscription URLs, list rating, member counts, most recent signup timing, and aggregate campaign and automation metrics (sends, opens, clicks, unsubscribes) to evaluate list health and growth. Example Analytics Questions: Which email lists have the most engaged and growing audiences? How does list health and quality impact campaign and automation performance? Are our automated emails performing as well as one-time campaigns for each list?
mailchimp__members	Consolidates member profiles with email details, subscription status, signup and opt-in timing, location data (country, timezone, latitude/longitude), member rating, VIP status, and engagement metrics (campaign and automation) to segment audiences and personalize communications. Example Analytics Questions: Who are our most engaged subscribers and where are they located? How do VIP members and high-value subscribers interact with our emails differently? What's the subscriber journey from signup to becoming an engaged member?
mailchimp__segments	Tracks segment profiles with list associations, member counts, segment type, creation and update timing, and aggregate campaign and automation metrics (sends, opens, clicks, unsubscribes) to measure segment performance and refine targeting strategies. Example Analytics Questions: Which audience segments deliver the best campaign performance? How do our targeted segments compare in engagement between campaigns and automations? Which segments are growing or changing, and how does that affect their engagement?

¹ Each Quickstart transformation job run materializes these 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.

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Prerequisites

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

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

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following mailchimp 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/mailchimp
    version: [">=1.3.0", "<1.4.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/mailchimp_source in your packages.yml since this package has been deprecated.

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Define database and schema variables

Option A: Single connection

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

vars:
    mailchimp_database: your_destination_name
    mailchimp_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Mailchimp 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 mailchimp_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  mailchimp:
    mailchimp_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

Optional: Incorporate unioned sources into DAG

If you use Fivetran Transformations for dbt Core™ and are unioning multiple Mailchimp connections, you can define your sources in a property .yml file, using this as a template. Set the variable has_defined_sources: true under the Mailchimp namespace in your dbt_project.yml. Otherwise, your Mailchimp connections won't appear in your DAG. See the union_connections macro documentation for full configuration details.

Disable models for non-existent sources

Your Mailchimp connection might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality in Mailchimp or have actively excluded some tables from your syncs. To disable the corresponding functionality in the package, you must set the relevant config variables to false. By default, all variables are set to true. Alter variables for only the tables you want to disable:

vars:
  mailchimp_using_automations: false # disable if you do not have the automation_email, automation_email, or automation_recipient_activity tables
  mailchimp_using_segments: false # disable if you do not have the segment table
  mailchimp_using_unsubscribes: false #disable if you do not have the unsubscribe table

(Optional) Additional configurations

Expand/collapse configurations

Changing the Build Schema

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

models:
    mailchimp:
      +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.

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:
    mailchimp_<default_source_table_name>_identifier: your_table_name 

Source casing for case-sensitive destinations

By default, the package applies case-insensitive comparisons when resolving source_relation values. If your destination is case-sensitive and you want downstream transformations to respect the exact casing of your source database and schema names, set the following variable:

vars:
    fivetran_using_source_casing: true

(Optional) Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

🔍 Does this package have dependencies?

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"]

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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

Are there any resources available?

• 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.