mmmaia / houseware_dbt_intercom_metrics

About Pre-built metrics for Intercom data. Check out metrics for other SaaS tools at https://hub.houseware.io/

Intercom 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 Intercom 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 Intercom Support Tickets.

Metrics

This package contains transformed models built on top of Fivetran Intercom & Intercom_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

metric	description
intercom_monthly_ticket_volume	Number of intercom tickets generated monthly.
intercom_monthly_closed_tickets	Number of tickets closed monthly.
intercom_monthly_open_tickets	Number of monthly open tickets.
intercom_monthly_resolution_rate	Percentage of tickets closed monthly.
intercom_monthly_csat_score	Percentage of positive ratings defining customer satisfaction score.
intercom_monthly_customer_initiated_conversations	Number of conversations customer have initiated monthly.
intercom_monthly_average_ticket_volume	Average number of tickets recieved every month.
intercom_average_resolution_time_in_hours	Average time taken to resolve ticket monthly.
intercom_average_response_time_in_hours	Average time taken to respond to a ticket every month.

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran intercom 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_intercom_metrics.git"
    revision: v0.1.1

Step 3: Define database and schema variables

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

# dbt_project.yml

...
config-version: 2

vars:
  intercom_source:
    intercom_database: your_database_name
    intercom_schema: your_schema_name

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

(Optional) Step 4: Change build schema

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

# dbt_project.yml

...
models:
  intercom_metrics:
    +schema: my_new_schema_name # leave blank for just the target_schema
  intercom_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Step 4: Separate metrics into different schema

By default, this package will compute all the metrics in your target schema inside target database. It's a good practice to add a suffix to your schema defining what source the metrics are coming from Go to your dbt_project.yml file

# dbt_project.yml
...
config-version: 2
models:
  intercom_metrics:
    +schema: intercom_metrics

🗄 Which warehouses are supported?

This package has been tested on BigQuery, 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