Iterable dbt Package

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

Resources

Number of materialized models¹: 45
Connector documentation
- Iterable connector documentation
- Iterable ERD
dbt package documentation

What does this dbt package do?

This package enables you to understand the efficacy of your growth marketing and customer engagement campaigns across email, SMS, push notification, and in-app platforms. It creates enriched models with metrics focused on event interactions, campaign performance, and user engagement.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_iterable

Final output tables

By default, this package materializes the following final tables:

Table	Description
iterable__events	Tracks all user events with campaign attribution, user details, and channel information to analyze user behavior, conversion paths, and campaign effectiveness at the event level. See tracked events details. Example Analytics Questions: What customer actions drive the most revenue and conversions? Are our marketing campaigns more effective than organic customer discovery? What customer journey paths lead to purchase or upgrade?
iterable__user_campaign	Aggregates user-level engagement with specific campaigns and experiment variations including event counts by type to measure individual user responses to campaign messaging. Example Analytics Questions: Which customers are most responsive to our campaigns, and what messages resonate with them? Which email variations are winning with our audience? How frequently do customers engage with each campaign?
iterable__campaigns	Tracks campaign performance with user interaction metrics, event counts, experiment variations, and template details to measure campaign effectiveness and optimize email strategy. Example Analytics Questions: Which campaigns are driving the most engagement and sales? How do different email designs and messaging strategies perform? What's the reach and impact of our marketing initiatives?
iterable__users	Provides a comprehensive view of each user with campaign engagement history, list memberships, unsubscription status, and interaction metrics to understand user preferences and lifetime engagement. Example Analytics Questions: Who are our most engaged customers and what do they have in common? Why are customers unsubscribing, and what did we lose when they left? Which acquisition channels bring us the most engaged customers?
iterable__list_user_history	Chronicles user-list membership history to track when users join or leave lists, manage audience segmentation, and analyze list growth without excessive Monthly Active Rows (MAR) usage. Example Analytics Questions: Are we growing or losing our subscriber base in key segments? Which customers are moving between audience segments, and why? How long do customers stay engaged before opting out?
iterable__user_unsubscriptions	Tracks all user unsubscriptions by message type and channel to manage communication preferences, protect sender reputation, and identify unsubscribe patterns. Example Analytics Questions: Which types of messages are causing customers to opt out? Are we losing customers completely, or just from certain communication types? What patterns indicate a customer is likely to unsubscribe?

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 Iterable connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Unsubscribe tables are no longer history tables

For connections created past August 2023, the user_unsubscribed_channel_history and user_unsubscribed_message_type_history Iterable objects will no longer be history tables as part of schema changes following Iterable's API updates. The fields have also changed. There is no lift required, since we have checks in place that will automatically persist the respective fields depending on what exists in your schema (they will still be history tables if you are using the old schema).

Please be sure you are syncing them as either both history or non-history.

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.