Looker/Data Studio
Paminga's Looker Studio connector lets you pull your email engagement data into your own Looker Studio dashboards, alongside whatever else you already report on there.
It is not a fixed set of charts. You choose the dimensions and metrics, and Paminga aggregates on its end to match. Report on sends, opens, and clicks by automation. Break one automation down by individual email. Chart a year by month, or a week by day. Combine it with your other data sources in a single report.
Looker Studio is free.
What You Can Report On
The connector currently exposes one dataset, Email Engagement, covering email sent from Classic Campaigns and Drip Series and other Workflows.
Dimensions — the ways you can slice:
| Dimension | What it gives you |
|---|---|
| Date, Year Month, Year | Time, at whichever granularity your chart needs |
| Automation Name / ID | The Classic Campaign or Workflow that sent the email |
| Automation Type / Type Label | Which kind of automation it was — useful for comparing Classic against next-gen |
| Email Name / ID | The individual email |
Metrics — what gets counted:
| Metric | |
|---|---|
| Sends, Opens, Clicks | The core three |
| Bounces, Unsubscribes, Spam Reports | Deliverability and list health |
| Open Rate, Click Rate, Click to Open Rate, Bounce Rate, Unsubscribe Rate | Calculated — see Rates recalculate as you group |
You only pay the cost of what you chart. Ask for sends by month and Paminga returns one row per month, not a year of daily rows it makes Looker Studio add up.
Before You Start
You need a Paminga Personal Access Token. Create one under Personal Access Tokens.
Create the token under a dedicated API user rather than your own login. Reports keep working when people change roles or leave, and you can see at a glance what the token is for.
The token carries the permissions of the user who owns it, and reports only ever return data from that user's Paminga instance.
Connect the Connector
- Open the connector using the button above.
- Authorize the connector when Google asks. It needs permission to make external requests — that is how it reaches Paminga.
- Paste your Personal Access Token when prompted.
- Choose your Paminga Region — United States or Europe. If you are not sure, it is United States.
- Choose the Dataset. Today that is Email Engagement.
- Click Connect.
If Looker Studio says the connector ID is invalid, you are almost certainly signed in as a different Google account than the one you meant to use. Switch accounts in Looker Studio and open the link again.
Looker Studio drops you into the field list. From there, build charts as you would with any other data source.
How Dates Work
This is the one thing worth reading twice.
Metrics are attributed to the day the event happened, not the day the email was sent. An open today can belong to an email you sent three weeks ago.
That means a single day can legitimately show opens and clicks against zero sends. It looks like a bug the first time you see it. It isn't — those opens belong to earlier sends, and counting them on the send date would misrepresent when your audience actually engaged.
If you want send-day attribution, chart at a coarser grain — Year Month rather than Date — so sends and their engagement usually land in the same bucket.
Rates Recalculate as You Group
Open Rate, Click Rate, and the other rates are calculated fields. Looker Studio computes them from the underlying counts at whatever level your chart is grouped by.
This matters. If rates were stored values, grouping a month of daily rows would sum thirty daily open rates and show you something like 1,680%. Because they are calculated, Looker Studio divides total opens by total sends for whatever range and breakdown your chart uses, and the number is right at every level.
The practical consequence: you can group, filter, and drill however you like, and the rates follow correctly.
What's Included
The Email Engagement dataset covers email sent from Classic Campaigns and from Workflows — including Drip Series.
It does not cover email sent from other sources, such as forms, Action Sets, or automated actions. Those totals will therefore not match every number you see elsewhere in Paminga.
If email from other sources matters for your reporting, tell your Customer Success Manager. The connector is built so that new data can be added without you having to rebuild your reports.
Data Freshness
Looker Studio caches connector data. By default it refreshes roughly every 12 hours.
If your numbers look stale, that is almost always why. You can change it: open the data source, click Data freshness, and pick a shorter interval. Very short intervals mean more frequent queries against Paminga, so pick something that matches how you actually use the report.
Report Performance
Paminga aggregates before returning data, so most reports are quick. Two things make a report slow:
- A wide date range at a fine grain. A full year broken down by Date, Automation, and Email is a lot of rows. Use Year Month instead, or narrow the range.
- Breaking down by Email Name across every automation at once. Filter to the automations you care about first.
If a report asks for more than can be loaded at once, the connector tells you so directly rather than returning partial data.
Other Ways to Get Your Data
The connector is built on Paminga's GraphQL API, so anything it can show you, you can also query directly. See APIs.
If your reporting lives in a data warehouse rather than a BI tool, Paminga can push data to Snowflake on a schedule instead.
