Tables provide a compact view of Fullstory analytics data, letting you quickly scan and compare multiple values without needing to create individual metrics for each dimension.

Tables are complimentary to dashboards in that they both combine multiple segments and metrics into a single view. While dashboards organize information into cards, tables take a straightforward approach of presenting rows and columns in a compact format. Table links can be added to dashboards to keep things organized.

In this article:

• Quick start
• Overview of a table
• Creating a table
• Grouping by segments
• Grouping by custom properties
• Exporting a table
• Adding a table link to a dashboard
• Frequently Asked Questions

Explore essential use cases of tables:

• Feature engagement
• Site monitoring
• A/B testing
• Campaign performance

Quick start

Want to get started without reading all of the documentation?

1. Click the Create button on the left side of the Fullstory menu.
2. Click Table from the menu that opens.
3. Name your table "Demo table".
4. Under Compare Users, confirm that "Everyone" is selected.
5. Change Date to be Past 7 Days.
6. Click Group by and choose Day from under Built-in Properties.
7. Open the Group by menu again and choose URL Host from under Built-in Properties.
8. Expand the first row of days to see the URL hosts for that row.
9. Click Save to save the table.

Overview of a table

1. Compare Users: Choose up to five user segments to compare. Segments will be displayed as a top level grouping.
2. Group by dimension: Select which properties (up to 3) by which to group data.
3. Date: Select the date range for the table.
4. Property > more actions: Click the 3 dots (⋮) next to a property to filter, reorder, or remove the grouping.
5. Rows: Data will be displayed in individual rows, grouped by property. Tables support up to 1000 rows per dimension.
6. Sessions: The first column will always be number of unique sessions.

7. Columns: Each subsequent column is a metric that has been added to the table. A table can have up to 12 columns.

   Column metrics use the segments chosen for the table, overriding the segment that may have been defined as part of the metric.
8. Add to table: Click the plus button to add a new column to the table. Metrics and funnel metrics can be added as columns.
9. Export as CSV: Export the visible table data to CSV. See below for more details.

Creating a table

Create a new table anywhere you would create a new product analytics object in Fullstory. You'll find Table below Dashboard by clicking the Create button in the following locations:

• The left Fullstory menu
• The library
• Spaces, both public and private

You can also add table links directly from within dashboards. See below.

Grouping by segments

You can compare up to five segments via the Compare Users menu, which will be displayed as the top-level grouping within the table. This can be useful for comparing A/B test, live site monitoring, and measuring feature adoption across segments.

In the example above, we see multiple segments for user cohorts representing conversion after  the 1st, 2nd, and 3rd+ touches. Added to the table are column metrics for conversion rate, revenue per visit, and total revenue.

Grouping by custom properties

In addition to built-in properties, tables can also be grouped by any custom properties that you've set up in your account, including event properties, page properties, user properties, and extracted element properties.

Exporting a table

Click the down arrow icon (⤓) in the top right of the table area to export table data to a CSV. All top-dimension rows and any grouped rows that have been expanded will be included.

Interested in using behavioral data to power workflows, send data to third-party systems, or batch sync with your data warehouse? Check out Fullstory Anywhere for an always-on alternative.

Adding a table link to a dashboard

Add a table link to a dashboard to quickly jump to tables that may be related.

Click the Add a card link from within a dashboard and choose Add from Library to add an existing table or choose Table to create a new table to add to the dashboard.

From within a table, click the Add to Dashboard link at the top of the table to add the table to a dashboard.

FAQ

Can I compare multiple segments within the same table?

Yes. Use the Compare Users dropdown. See Grouping by segments above to learn more.

Can I create metrics based on different segments and add them to the table to compare segments?

Fullstory lets you associate a segment when defining a metric. However, this segment will get overridden by the table's segment when the metric gets added as a column to the table. 

As a workaround, you can often approximate user segments by creating metrics that are filtered on events—which can contain user properties—rather than users. See this article for more information.

Do you have any examples of powerful ways to use tables?

Yes, check out some of these essential use cases:

• Feature engagement
• Site monitoring
• A/B testing
• Campaign performance

What are the dimension limits for table groupings?

When you group table data using the Group by menu, dimension limits vary depending on the grouping level and whether you're using metrics or funnels:

• First-level grouping: The first property you group by (the top-level rows in your table):
  • Metrics: More than 500 dimensions (no hard limit).
  • Funnels: Maximum of 10 dimensions.
• Second-level and third-level groupings: Additional properties you group by (the nested rows that appear when you expand a top-level row):
  • Metrics: Maximum of 20 dimensions per parent row.
  • Funnels: Maximum of 10 dimensions per parent row.

Example: If you create a table grouped by Browser, then by Email, you'll see many browsers in the first-level grouping. However, when you expand any individual browser row, you'll see a maximum of 20 email addresses for that browser.

How do table columns interact with grouping dimensions?

All column metrics in a table (the columns you add using the + button) display data based on the dimensions from your first-level grouping. This means:

• Each column shows values that correspond to the rows defined by your first-level grouping.
• If a dimension exists in a column metric but doesn't appear in your first-level grouping, that dimension's data won't be shown in the table.
• To see data for dimensions that aren't currently visible, adjust your groupings so those dimensions appear in your first-level grouping.

See something we could be doing better?

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