This package builds off of the Shopify dbt package to weave together your Shopify e-commerce data with insights from marketing connectors. Currently, this package supports combining Shopify with email and SMS marketing data from Fivetran's Klaviyo dbt package.
This dbt package enables you to:
- Tie e-commerce revenue to your email and SMS marketing via last-touch attribution.
- Consolidate customers, their information, and activity across platforms.
- Create a rich portrait of customer personas based on how customers are engaging with and responding to specific marketing efforts.
Check out our blog post for further discussion on how the package can accelerate your business analysis.
This package produces three final output models, and is currently designed to work simultaneously with our Shopify and Klaviyo dbt packages. Dependencies on these packages are declared in this package's packages.yml file, so they will automatically download when you run dbt deps. The primary outputs of this package are described below. Intermediate models are used to create these output models, and are not documented here.
| model | description |
|---|---|
| shopify_holistic_reporting__orders_attribution | Each record represents a unique Shopify order, enhanced with a customizable last-touch attribution model associating orders with Klaviyo flows and campaigns that customers interacted with. Includes dimensions like whether it is a new or repeat purchase in Shopify. See available customizations here. |
| shopify_holistic_reporting__daily_customer_metrics | Each record represent a unique customer's daily activity attributed to a campaign or flow in Klaviyo. The grain is set at the customer-day-flow/campaign level. This model is enriched with both Shopify and Klaviyo metrics, such as the net revenue, taxes paid, discounts applied, and the counts of each type of interaction between the user and the campaign/flow. |
| shopify_holistic_reporting__customer_enhanced | Each record represents a unique individual (based on email) that may exist in Shopify, Klaviyo, or both platforms. Enhanced with information coalesced across platforms, lifetime order metrics, and all-time interactions with email marketing campaigns and flows. |
If you would like a deeper explanation of the logic used by default in the dbt package you may reference the DECISIONLOG.
Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.
Include in your packages.yml
packages:
- package: fivetran/shopify_holistic_reporting
version: [">=0.1.0", "<0.2.0"]See connector-specific configurations in their individual dbt package READMEs:
If you have multiple Shopify and/or 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 union_schemas or union_databases variables for each type of connector. Note that Shopify and Klaviyo refer the same names for the variables, so you will need to properly namespace them within the klaviyo_source and shopify_source packages to use this functionality for both sources:
# dbt_project.yml
...
config-version: 2
vars:
klaviyo_source:
union_schemas: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different schemas/datasets of the same database/project
union_databases: ['klaviyo_usa','klaviyo_canada'] # use this if the data is in different databases/projects but uses the same schema name
shopify_source:
union_schemas: ['shopify_usa','shopify_canada'] # use this if the data is in different schemas/datasets of the same database/project
union_databases: ['shopify_usa','shopify_canada'] # use this if the data is in different databases/projects but uses the same schema nameBy default, this package will build the final models within a schema titled (<target_schema> + _shopify_holistic) and intermediate models in (<target_schema> + _int_shopify_holistic) in your target database. If this is not where you would like your modeled Shopify Holistic Reporting data to be written to, add the following configuration to your dbt_project.yml file:
# dbt_project.yml
...
models:
shopify_holistic_reporting:
+schema: my_new_schema_name # leave blank for just the target_schema
intermediate:
+schema: my_new_schema_name # leave blank for just the target_schemaNote that if your profile does not have permissions to create schemas in your warehouse, you can set each
+schemato blank. The package will then write all tables to your pre-existing target schema.
Models from the individual Shopify and Klaviyo packages will be written their respective schemas.
Don't see a model or specific metric you want to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package!
Please create issues or open PRs against main. Check out this post on the best workflow for contributing to a package.
This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
dbt v0.20.0 introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. 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.
# dbt_project.yml
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']- Provide feedback on our existing dbt packages or what you'd like to see next
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