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

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.

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.

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.