[BETA] Using Matik Analytics as a Data Source

Beta Feature: Matik Analytics is currently in beta. Contact your Technical Account Manager if you are interested in access.

Matik Analytics lets you query the usage and engagement data Matik already collects about your enterprise, the same way you query a data warehouse or a CRM. You write SQL against a small set of curated tables, and the results become Dynamic Content or Input options like any other data source.

There is nothing to connect. Matik Analytics is not in the list of data sources you add under Connect Data Source, and it needs no credentials, no host, and no permissions setup. Once it is enabled for your enterprise, it appears in the data source picker on its own.

Every query is automatically scoped to your own enterprise. You never write a filter to limit results to your company's data, and there is no way to see another company's.

Example Use Cases

Report on Matik usage in the format your team already uses

Instead of exporting a fixed CSV and reshaping it, query the data directly and get exactly the columns and grouping you need.

For example, to build a monthly adoption slide for standalone, non-email presentations, aggregate presentation activity by month and user group. A user can belong to more than one group, so presentation_counts stores group membership as a comma-joined list. Split that list into one row per group before grouping:

WITH group_rows AS (
    SELECT
        DATE_TRUNC('month', p.created_on) AS month,
        ids.id AS user_group_id,
        names.name AS user_group,
        p.total_presentations
    FROM presentation_counts p
    CROSS JOIN unnest(string_to_array(p.user_group_ids, ',')) WITH ORDINALITY AS ids(id, pos)
    JOIN unnest(string_to_array(p.user_groups, ',')) WITH ORDINALITY AS names(name, pos)
        ON names.pos = ids.pos
    WHERE p.created_on >= CURRENT_DATE - INTERVAL '6 months'
        AND p.is_attachment = FALSE
        AND p.template_source_type <> 'email'
)
SELECT
    month,
    user_group_id,
    MAX(user_group) AS user_group,
    SUM(total_presentations) AS presentations
FROM group_rows
GROUP BY month, user_group_id
ORDER BY month, presentations DESC

As with any other piece of Dynamic Content, the result can drive a table, a chart, or an Insights summary.

Follow up with the recipients who engaged

Matik Analytics allows you to leverage your own Matik Mail performance analytics to drive personalization. For example, after a campaign, you may want to send a targeted follow-up only to people who showed interest. A Matik Analytics query returns the recipients who clicked in the last 30 days:

SELECT DISTINCT recipient_email
FROM email_events
WHERE event_type = 'click'
    AND occurred_at >= CURRENT_DATE - INTERVAL '30 days'
    AND flow_name = &:flow_name

Reference that Dynamic Content inside a query on your own contacts data to build the follow-up list:

SELECT email
FROM contacts
WHERE email IN (
    SELECT recipient_email
    FROM {{recently_engaged_recipients}}
)

This works in both directions. A Matik Analytics query can be nested inside another data source's query, and another data source's Dynamic Content can be nested inside a Matik Analytics query.

Selecting Matik Analytics as a Data Source

Matik Analytics can be used to power Dynamic Content or Inputs, just like any other data source. Once the beta is enabled for your enterprise, select Matik Analytics as the data source.

Querying Matik Analytics

Matik Analytics is organized into Analytics Categories. Each category contains related tables that you can query.

Category	Included Data	Tables	Requires
Email analytics	Sends and engagement for emails your enterprise has sent through Matik.	`email_sends`, `email_events`	Matik Mail
Generation performance	Timing for each step of the presentations your enterprise has generated.	`generation_times`, `generation_times_daily`	None
Presentation activity	Who creates presentations, from which templates, and how that changes over time.	`presentation_counts`	None

The dropdown only lists categories your enterprise can use.

A single query reads from one category, so you cannot join a table in Email analytics to a table in Presentation activity. To combine them, build separate Dynamic Content for each and reference one inside the other.

Email Analytics

Available to enterprises with Matik Mail enabled.

email_sends - one row per recipient of a sent email, with delivery state and engagement totals.

Column	Type	Description
`sent_at`	timestamp	When the send record was created.
`recipient_email`	text	Email address the message was sent to.
`recipient_type`	text	Whether the recipient was on the to, cc, or bcc line.
`template_name`	text	Name of the email template used.
`flow_name`	text	Name of the scheduled flow that sent it, if any.
`delivery_status`	text	Delivery outcome for the message.
`open_count`	integer	Number of times this recipient opened the message.
`click_count`	integer	Number of link clicks by this recipient.
`unsubscribed`	boolean	Whether the recipient unsubscribed from this send.
`spam_report`	boolean	Whether the recipient reported the message as spam.
`last_open_at`	timestamptz	Most recent open, or null if never opened.
`last_click_at`	timestamptz	Most recent click, or null if never clicked.

email_events - one row per email engagement event, either an open or a click.

Column	Type	Description
`event_type`	text	The kind of engagement, such as open or click.
`occurred_at`	timestamptz	When the event happened.
`recipient_email`	text	Email address that generated the event.
`recipient_type`	text	Whether the recipient was on the to, cc, or bcc line.
`template_name`	text	Name of the email template used.
`flow_name`	text	Name of the scheduled flow that sent it, if any.
`url`	text	Clicked URL. Null for opens.
`attachment_id`	integer	Id of the clicked attachment. Null for opens and non-attachment clicks.
`attachment_name`	text	Name of the clicked attachment. Null for opens and non-attachment clicks.
`attachment_template_id`	integer	Template id of the clicked attachment. Null when there is none.
`attachment_template_name`	text	Template of the clicked attachment. Null when there is none.

Generation Performance

How long each step of a presentation generation takes. Use generation_times_daily for reporting and generation_times when you need individual runs.

generation_times - one row per pipeline step of a presentation generation, with its elapsed time.

Column	Type	Description
`occurred_on`	date	Day the generation ran.
`event_name`	text	Pipeline step that reported this timing.
`elapsed_ms`	bigint	How long the step took, in milliseconds.

generation_times_daily - daily rollup of generation timings per pipeline step: run count, median, and 95th percentile.

Column	Type	Description
`occurred_on`	date	Day the runs happened.
`event_name`	text	Pipeline step that reported these timings.
`runs`	integer	Number of times the step ran that day.
`p50_ms`	numeric	Median elapsed milliseconds across the day's runs.
`p95_ms`	numeric	95th percentile elapsed milliseconds across the day's runs.

Presentation Activity

presentation_counts - daily presentation generation counts. One row per UTC day, user, template, and whether the row counts rendered email attachments.

Column	Type	Description
`created_on`	date	UTC day the presentations were created. The current partial day is dropped, so days are only ever complete.
`user_id`	integer	Id of the user who created the presentations.
`user_name`	text	Name of the creating user, as of the row's last sync.
`user_email`	text	Email of the creating user.
`user_group_ids`	text	Comma-joined ids of the groups the user belongs to. The stable key for group filters.
`user_groups`	text	Comma-joined names of the groups the user belongs to, in the same order as `user_group_ids`. Display text, as of the row's last sync.
`is_producer`	boolean	Whether the user holds a non-consumer role. Matched by role name, so custom roles count.
`template_id`	integer	Id of the template the presentations were generated from.
`template_name`	text	Name of the template, as of the row's last sync.
`template_source_type`	text	Kind of template: `powerpoint`, `google_slides`, `email`, and so on. `email` marks email shell rows.
`generation_source`	text	Entry point that initiated the generation: `web_app`, `sfdc_app`, `mcp`, `external_api`, `assistant`, `scheduled_flow`, `triggered_flow`. `NULL` means the source was not captured.
`is_attachment`	boolean	Whether the row counts presentations rendered as email attachments.
`total_presentations`	bigint	Completed presentations created that day. Test runs and Mail Outbox previews are excluded, and deleted presentations still count.
`total_failed`	bigint	Presentations that failed that day through an error, a failed condition, or a timeout.

Things to note:

An email send creates a shell row (template_source_type = 'email') plus a row for each rendered attachment. Filter on is_attachment or template_source_type so sends are not counted twice.
generation_source data started getting tracked in September 2026, so generations from before then may show NULL.

Writing Queries

Matik Analytics accepts one read-only PostgreSQL query at a time. Start your query with SELECT or WITH ... SELECT, and use only the tables listed for the selected Analytics Category.

Matik Analytics supports referencing &:inputs and {{nested_dc}}, just like any other SQL query in Matik. These references can be used anywhere their resolved values are valid, including using nested table Dynamic Content in a FROM or JOIN clause. Matik Analytics Dynamic Content can also be nested in another SQL data source that supports table Dynamic Content.

Supported SQL

Joins between tables in the same Analytics Category
Subqueries and common table expressions (CTEs), including recursive CTEs
Aggregates and window functions
Set operations such as UNION, INTERSECT, and EXCEPT
Standard PostgreSQL expressions and functions, except for the restricted operations described below

SQL that is not supported

More than one SQL statement in a query. A single trailing semicolon is allowed.
Statements that write data or modify the database, including INSERT, UPDATE, DELETE, MERGE, CREATE, DROP, ALTER, and TRUNCATE
Database administration or session commands, including GRANT, REVOKE, COPY, SET, EXPLAIN, SHOW, and VACUUM
Locking or table-creation forms such as FOR UPDATE and SELECT INTO
Schema-qualified table names, such as public.email_sends
System catalogs, raw Matik application tables, or tables from another Analytics Category
A CTE that uses the same name as one of the category's built-in tables
Functions that inspect or modify the database server or session, access server files, open external database connections, or pause or control database processes

Matik validates each query before it runs and displays an error when the query uses unsupported SQL.

You can also ask the Matik AI Agent to write or update an analytics query for you, rather than authoring the SQL yourself.

Limitations

Data is not real time. Analytics data syncs throughout the day and is typically less than two hours behind the activity it describes. In presentation_counts, the current partial day is always excluded, so the most recent day you can query is the last complete UTC day.
One statement per query. A second statement after a semicolon is rejected.
One category per query. Tables from different categories cannot be joined in a single query.
Query text is limited to 1 MB.
Query timeout. Matik Analytics queries that run longer than 60 seconds are stopped.
Saved results. A saved query result holds up to 50,000 rows.

Best Practices

Filter out attachment rows when counting unique sends. In presentation_counts, an email send produces both an email row and attachment rows. Without a filter on is_attachment or template_source_type, the query will count both the email and any attachments.
Split group membership before you group by it. user_group_ids and user_groups are comma-joined lists, so grouping on either column groups by the whole combination of a user's groups rather than by one group at a time. Split them into one row per group first, as in the example above. Note that this drops users who belong to no group; switch the CROSS JOIN to a LEFT JOIN ... ON true if you need those rows.
Group by user_group_ids, display user_groups. Group names change and are stored as of the last sync. The ids are stable, so group on those and use the names for display.
Use generation_times_daily for trends. It is already rolled up per day and step, so it returns far fewer rows than generation_times and stays well clear of the saved-result cap.
Use nested Dynamic Content to query across Analytics Categories. Since a query reads from a single category, combining email and presentation data means referencing one Dynamic Content inside another.

[BETA] Using Matik Analytics as a Data Source

Example Use Cases

Report on Matik usage in the format your team already uses

Follow up with the recipients who engaged

Selecting Matik Analytics as a Data Source

Querying Matik Analytics

Email Analytics

Generation Performance

Presentation Activity

Writing Queries

Supported SQL

SQL that is not supported

Limitations

Best Practices

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