Looker/Data Studio

Paminga's Looker Studio connector lets you pull your Paminga 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. See which campaigns are actually producing form submissions, and which of your files people are downloading. 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 exposes three datasets. Each is a separate data source in Looker Studio, so add the connector once per dataset you want.

Email Engagement

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, counted the way The Pulse counts them — see What's Included
Machine Opens	Opens made by software rather than a person, such as a mail client prefetching images. Kept out of Opens, reported here
Unsubscribe Clicks	Clicks on the unsubscribe link. Kept out of Clicks, reported here. Recorded for Workflow email only
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

Form Submissions

Every submission to your Paminga forms, with the contact who submitted, the page they submitted from, and the campaign that brought them there.

Dimensions:

Dimension	What it gives you
Date, Year Month, Year	Time, at whichever granularity your chart needs
Submitted At	The exact timestamp. Adding it gives you one row per submission
Submission ID	The individual submission. Adding it also gives one row per submission
Form Name / ID	The form that was submitted
Form Generation	Whether it was a current Paminga form or a legacy Classic one
Page URL	The page the form was submitted from
Partial Submission	Whether the visitor was captured before finishing the form
Contact Email, First Name, Last Name, ID	Who submitted
Account Name / ID	The account that contact belongs to
UTM Campaign, Source, Medium, Term, Content	The tags on the visit that led to the submission
Marketing Channel, Marketing Source	How Paminga classified that visit — Organic Search, Paid Social, and so on

Metric:

Metric
Submissions	How many submissions. Partial submissions are included — split by Partial Submission to separate them

This one dataset does both jobs. Drop Form Name and Submissions on a chart and you get counts per form. Add Submission ID or Submitted At and the same dataset gives you a row per submission, which is the shape you want for a table you intend to read or export.

note

The answers people typed into your form fields are not part of this dataset. Every form has a different set of fields, and a Looker Studio data source needs a fixed set of columns. To read the answers, use Export Form Submissions from the Form Submissions page instead.

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.

File Downloads

Every file and asset downloaded from your tracked website — PDFs, guides, reports — with the contact who took it and the campaign that brought them.

Dimensions:

Dimension	What it gives you
Date, Year Month, Year	Time, at whichever granularity your chart needs
Downloaded At	The exact timestamp. Adding it gives you one row per download
Download ID	The individual download. Adding it also gives one row per download
File Name	Just the file name, readable — "Reach for the STARs.pdf", not the escaped path
File Type	The extension, lower case, e.g. pdf
File URL	The full URL the file was served from
Domain	The tracked domain it was served from
Contact Email, First Name, Last Name, ID	Who downloaded it
Account Name / ID	The account that contact belongs to
UTM Campaign, Source, Medium, Term, Content	The tags on the visit the download happened during
Marketing Channel, Marketing Source	How Paminga classified that visit

Metric:

Metric
Downloads	How many downloads. The same person fetching the same file twice counts twice

PNG, JPG, JPEG and GIF files are excluded, so your numbers reflect assets people actually asked for rather than every logo on the page. Other image formats such as SVG and WebP are not filtered out.

Attribution is much better here than on form submissions

A download is recorded against the page view that produced it, so Paminga already knows which visit it belongs to — nothing has to be matched up after the fact. In practice Marketing Channel is populated for the large majority of downloads, whereas on form submissions it is a minority. If you want one report where channel attribution is reliably present, build it on this dataset.

UTM tags are still sparser than channel, for the ordinary reason: most visits are not tagged. A direct or repeat visitor has no UTM to record.

Before You Start

You need a Paminga Personal Access Token. Create one under Personal Access Tokens.

tip

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

1. Open the connector using the button above.
2. Authorize the connector when Google asks. It needs permission to make external requests — that is how it reaches Paminga.
3. Paste your Personal Access Token when prompted.
4. Choose your Paminga Region — United States or Europe. If you are not sure, it is United States.
5. Choose the Dataset — Email Engagement, Form Submissions, or File Downloads.
6. Click Connect.

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 section worth reading twice.

Which Day an Event Belongs To

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.

Your Time Zone

Days are cut, and timestamps are shown, in the time zone of the Paminga user whose Personal Access Token the connector uses. That is the same zone Paminga's own dashboards group by, so a send at 9 PM lands on the same day in Looker Studio as it does in The Pulse.

The date range you pick in Looker Studio is read in that zone too. "August 5 through September 2" means those days as your team experiences them, not as UTC counts them.

If the token belongs to a dedicated API user, as we recommend, check that user's time zone in their personal settings. It is the one the connector will use.

The Default Date Range Is 28 Days

Looker Studio applies a default date range of the last 28 days to every new report. The connector itself has no limit — if your data seems to start four weeks ago, that is the report's date range, not the connector's.

To see more, add a Date range control to the report, or open the report settings and change the default date range to something like Year to date.

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.

Opens and Clicks are counted the way The Pulse counts them. A machine open — a mail client fetching images on the recipient's behalf, before any person has looked at the email — is not an open. A click on the unsubscribe link is not a click. Both are still reported, as Machine Opens and Unsubscribe Clicks, so nothing is hidden and you can add them back if you want the raw count. Repeat opens and clicks by the same contact are counted every time, on both the connector and The Pulse.

The Form Submissions dataset covers submissions to your Paminga forms, both current and Classic. Its Submissions total is built to match the Form Submissions page under Marketing Center for the same date range, so the two reconcile. Submissions to third-party forms are a separate thing and are not included.

Reconcile against the Form Submissions page, not the Form Submissions Dashboard

The Form Submissions Dashboard under Reporting shows Classic forms only. If you compare the connector against it, every submission to a current Paminga form will look like an overcount. The Form Submissions page under Marketing Center covers both kinds and is the number to check against. Neither one collapses two submissions from the same email address into one, and neither does the connector.

note

If data 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.

Where Marketing Channel and UTM Come From

Marketing Channel, Marketing Source, and the UTM columns describe the visit that led to a submission. Paminga can fill them in only when it can tie the submission back to that visit, which needs the Paminga tracking code on the page hosting the form.

So a form on a page carrying your tracking code gets attribution. The same form embedded on a site without it gets none, and those columns come back empty.

Expect these columns to be sparser than the rest

This is the honest version, because a half-populated column is easy to misread.

Across current Paminga instances, most form submissions cannot be tied back to a visit — so Marketing Channel is commonly available on a minority of submissions, and UTM tags on fewer still. That is not a gap in UTM capture: visits themselves are tagged nearly all the time. It is the link between the submission and the visit that is usually missing.

Before you build a report on these columns, chart Submissions by Marketing Channel for a recent month and see how much lands in the blank bucket. If most of it does, treat these columns as directional rather than as a measurement.

Empty is not the same as Direct Traffic. A blank Marketing Channel means Paminga could not tie the submission to a visit, not that the visitor arrived without a referrer. Reading blanks as direct traffic is the single easiest way to draw a wrong conclusion from this dataset.

The submission itself is always counted either way. Attribution only ever adds columns; it never removes rows, so your Submissions total is unaffected by any of this.

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.