Klaviyo Transformation dbt Package (Docs)

What does this dbt package do?

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

• Enables you to better understand the efficacy of your email and SMS marketing efforts. It achieves this by:

  • Performing last-touch attribution on events in order to properly credit campaigns and flows with conversions
  • Enriching the core event table with data regarding associated users, flows, and campaigns
  • Aggregating key metrics, such as associated revenue, related to each user's interactions with individual campaigns and flows (and organic actions)
  • Aggregating these metrics further, to the grain of campaigns, flows, and individual users

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

Table	Description
klaviyo__events	Each record represents a unique event in Klaviyo, enhanced with a customizable last-touch attribution model associating events with flows and campaigns. Also includes information about the user who triggered the event. Materialized incrementally by default.
klaviyo__person_campaign_flow	Each record represents a unique person-campaign or person-flow combination, enriched with sums of the numeric values (i.e. revenue) associated with each kind of conversion, and counts of the number of triggered conversion events.
klaviyo__campaigns	Each record represents a unique campaign, enriched with user interaction metrics, any revenue attributed to the campaign, and other conversions.
klaviyo__flows	Each record represents a unique flow, enriched with user interaction metrics, any revenue attributed to the flow, and other conversions.
klaviyo__persons	Each record represents a unique user, enriched with metrics around the campaigns and flows they have interacted with, any associated revenue (organic as well as attributed to flows/campaigns), and their recent activity.

How do I use the dbt package?

Step 1: Prerequisites

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

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

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) 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']

Step 2: Install the package

Include the following klaviyo 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/klaviyo
    version: [">=0.8.0", "<0.9.0"]

Step 3: Define database and schema variables

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

vars:
  klaviyo_database: your_database_name
  klaviyo_schema: your_schema_name

(Optional) Step 4: Additional configurations

Expand for configurations

Unioning Multiple Klaviyo Connectors

If you have multiple Klaviyo connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either (note that you cannot use both) the klaviyo_union_schemas or klaviyo_union_databases variables:

# dbt_project.yml
...
config-version: 2
vars:
  klaviyo_source:
    klaviyo_union_schemas: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different schemas/datasets of the same database/project
    klaviyo_union_databases: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different databases/projects but uses the same schema name

Attribution Lookback Window

This package attributes events to campaigns and flows via a last-touch attribution model in line with Klaviyo's internal attribution. This is necessary to perform, as Klaviyo does not automatically send attribution data for certain metrics. Read more about how the package's attribution works here and see the source code here.

By default, the package will use a lookback window of 120 hours (5 days) for email-events and a window of 24 hours for SMS-events. For example, if an 'Ordered Product' conversion is tracked on April 27th, and the customer clicked a campaign email on April 24th, their purchase order event will be attributed with the email they interacted with. If the campaign was sent and opened via SMS instead of email, the 'Ordered Product' conversion would not be attributed to any campaign.

To change either of these lookback windows, add the following configuration to your dbt_project.yml file:

If you would like to disable the package's attribution process completely, set these variables to 0.

# dbt_project.yml

...
config-version: 2

vars:
  klaviyo:
    klaviyo__email_attribution_lookback: x_number_of_hours # default = 120 hours = 5 days. MUST BE INTEGER.
    klaviyo__sms_attribution_lookback: y_number_of_hours # default = 24 hours. MUST BE INTEGER.

Note that events already associated with campaigns or flows in Klaviyo will never have their source attribution data overwritten by the package modeling.

Attribution-Eligible Event Types

By default, this package will only credit email opens, email clicks, and SMS opens with conversions. That is, only flows and campaigns attached to these kinds of events will qualify for attribution in our package. This is aligned with Klaviyo's internal attribution model.

However, this package allows for the customization of which events can qualify for attribution. To expand or otherwise change this filter on attribution, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  klaviyo:
    klaviyo__eligible_attribution_events: ['types', 'of', 'events', 'to', 'attribute', 'conversions', 'to'] # this is case-SENSITIVE and should be in all lower-case!!

Filtering Conversion Metrics to Pivot Out

The Klaviyo dbt package pivots relevant conversion events out into metric columns in the klaviyo__person_campaign_flow, klaviyo__campaigns, klaviyo__flows, and klaviyo__persons models. The package will sum up revenue attributed to each person's interactions with flows and campaigns (plus organic actions), count the instances of each kind of triggered conversion, and, at the flow and campaign grain, count the number of unique people who converted. The package splits up events to pivot out into two variables, klaviyo__count_metrics and klaviyo__sum_revenue_metrics, which will record the count of events/users and their associated revenue values, respectively.

By default, the package is configured to pivot out the below metrics. To change the conversion events that are pivoted out, tailor the following configuration to your desired metrics in your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  klaviyo: # case insensitive 
    klaviyo__count_metrics: 
      - 'Active on Site'
      - 'Viewed Product'
      - 'Ordered Product'
      - 'Placed Order'
      - 'Refunded Order'
      - 'Received Email'
      - 'Clicked Email'
      - 'Opened Email'
      - 'Marked Email as Spam'
      - 'Unsubscribed'
      - 'Received SMS'
      - 'Clicked SMS'
      - 'Sent SMS'
      - 'Unsubscribed from SMS'

    klaviyo__sum_revenue_metrics:
      - 'Refunded Order'
      - 'Placed Order'
      - 'Ordered Product'
      - 'checkout started'
      - 'cancelled order'

Passthrough Columns

Additionally, the Klaviyo package includes all source columns defined in the macros folder of the source package. We highly recommend including custom fields in this package as models now only bring in the standard fields for the EVENT and PERSON tables.

You can add more columns using our passthrough column variables. These variables allow for the passthrough fields to be aliased (alias) and casted (transform_sql) if desired, although it is not required. Datatype casting is configured via a SQL snippet within the transform_sql key. You may add the desired SQL snippet while omitting the as field_name part of the casting statement - this will be dealt with by the alias attribute - and your custom passthrough fields will be casted accordingly.

Use the following format for declaring the respective passthrough variables:

# dbt_project.yml

...
vars:
  klaviyo__event_pass_through_columns: 
    - name:           "property_field_id"
      alias:          "new_name_for_this_field_id"
      transform_sql:  "cast(new_name_for_this_field as int64)"
    - name:           "this_other_field"
      transform_sql:  "cast(this_other_field as string)"
  klaviyo__person_pass_through_columns:
    - name:           "custom_crazy_field_name"
      alias:          "normal_field_name"

Changing the Build Schema

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

# dbt_project.yml

...
models:
  klaviyo:
    +schema: my_new_schema_name # leave blank for just the target_schema
    intermediate:
      +schema: my_new_schema_name # leave blank for just the target_schema
  klaviyo_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Note that if your profile does not have permissions to create schemas in your warehouse, you can set each +schema to blank. The package will then write all tables to your pre-existing 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:
    klaviyo_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for more 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/klaviyo_source
      version: [">=0.7.0", "<0.8.0"]

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

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

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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, 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.