Google Ads Metrics dbt Package (Docs)

🛑 Few things to keep in mind

These packages are under active development and are expected to change with dbt metrics as it evolves over time. As of now, dbt metrics requires users to define models to calculate metrics and these models are persisted on the warehouse. Keeping this in mind, we have currently modelled our packages such that metrics and the models calculating these metrics have a 1:1 mapping, which is why you will see multiple metrics for the same conceptual metric entity accounting for different time grains and dimensions. In future, with the roll out of dbt Server and evolution of dbt metrics, we expect to streamline our packages to remove these redundancies.

The metrics in these packages are transformed on top of source data ETL'd via Fivetran to your warehouse. Make sure you have connected your SaaS source with Fivetran for the packages to work properly.

📣 What does this dbt package do?

This package provides pre-built metrics for Google ads data from Fivetran's connector. It uses data in the format described by this ERD.

This package enables you to access commonly used metrics on top of Google Ads Data

Metrics

This package contains transformed models built on top of google_ads_source package. A dependency on the source packages is declared in this package's packages.yml file, so it will automatically download when you run dbt deps.

The metrics offered by this package are described below. Note that some metrics contain extended metrics for segmentation based on adgroups, campaigns, and accounts.

metric	description
google_ads__monthly_ads	Number of ads running monthly
google_ads__monthly_adgroups	Number of ad groups running monthly
google_ads__monthly_campaigns	Number of campaigns running monthly
google_ads__monthly_accounts	Number of accounts running monthly
google_ads__monthly_impressions	Total monthly impressions received across all ads
google_ads__monthly_ad_clicks	Total monthly clicks received across all ads
google_ads__monthly_ad_conversions	Total monthly conversions received across all ads as measured by Google Ads
google_ads__monthly_ad_conversion_value	Total monthly conversion value received across all ads as measured by Google Ads
google_ads__monthly_ad_spend	Total monthly spend across all ads
google_ads__monthly_cost_per_click	Monthly cost per ad click
google_ads__monthly_cost_per_impression	Monthly cost per ad impression.
google_ads__monthly_click_through_rate	Monthly rate of users clicking on ad after viewing.
google_ads__monthly_cost_per_conversion	Monthly cost per ad conversion.
google_ads__monthly_roas	Monthly return on ad spend.
google_ads__monthly_avg_cost_per_click	Monthly average cost per ad click across all ads reported at a campaign level
google_ads__monthly_avg_cost_per_impression	Monthly average cost per ad impression across all ads reported at a campaign level
google_ads__monthly_avg_click_through_rate	Monthly average click-through rate across all ads reported at a campaign level
google_ads__monthly_avg_cost_per_conversion	Monthly average cost per conversion across all ads reported at a campaign level
google_ads__monthly_avg_roas	Monthly average return on ad spend across all ads reported at a campaign level

Notes

Please refer to the following notes regarding nuances related to the data available from the package:

• The latest attributes for account, campaign, adgroup and ad (like name, status) are being fetched from the Fivetran source tables. To change this, you can edit the dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  is_most_recent_record: 'FALSE'

• The calculation of monthly ratio metrics is done at an aggregate level. For example, the Monthly Cost per Click metric for campaigns is calculated as Total Spend (across all campaigns) / Total Clicks (across all campaigns)
• The calculation for average ratio metrics is done by taking the average of the ad-level ratio metrics at the campaign level. For example, the Monthly Average Cost per Click metric is calculated as Avg (Total Spend per Ad) / Avg (Total clicks per Ad) ; this number is reported per campaign

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

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

Step 2: Install the package

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:
  - git: "https://github.com/HousewareHQ/dbt_google_ads_metrics.git"
    revision: v0.1.0

Step 3: Define database and schema variables

By default, this package will look for your Google Ads data in the fivetran_google_ads schema of your target database. If this is not where your Google Ads data is, please add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  google_ads_database: your_database_name
  google_ads_schema: your_schema_name

For additional configurations for the source models, please visit the Google Ads source package.

(Optional) Step 4: Change build schema

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

# dbt_project.yml

...
models:
  google_ads_metrics:
    +schema: my_new_schema_name # leave blank for just the target_schema
  google_ads_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

🗄 Which warehouses are supported?

This package has been tested on Snowflake.

🙌 Can I contribute?

Additional contributions to this package are very welcome! Please create issues or open PRs against main. Check out this post on the best workflow for contributing to a package.

🏪 Are there any resources available?

• Provide feedback on what you'd like to see next
• Have questions, feedback, or need help? Email us at [email protected]
• Check out Houseware's blog
• Learn more about dbt in the dbt docs
• Check out Discourse for commonly asked questions and answers
• Join the chat on Slack for live discussions and support
• Find dbt events near you
• Check out the dbt blog for the latest news on dbt's development and best practices