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Subgroup views

A subgroup view is a focused slice of your data: one audience, one segment, or one comparison you want clients to see clearly. In AddMaple you build subgroup views with filters, custom columns/variables, and pivots, then save the charts into dashboards or mini-dashboards.

All steps below come from existing help articles.

Quick filters while exploring

AddMaple supports instant filters across your dataset. Common ways to add a filter:

1. Filter button

From the summary, chart, table, or raw data views, click the Filter button at the top of the screen (you can also press / on your keyboard to open the filter menu).

Choose a column, then choose how to filter. Options depend on column type.

See Filter your data.

2. From a chart

When you explore a column, hover over a bar (or equivalent) and use Filter by this value.

See Filter your data.

3. From the table view

In table view, click an MC+ or MC value in a row to filter by it.

See Filter your data.

Saved segments (custom columns)

For reusable audience definitions (for example "Enterprise buyers" or "Detractors"), create a custom column/variable:

  1. Click the More menu and select Custom Column/Variable
  2. Give the new column a name
  3. Choose Exclusive or Overlapping categories
  4. Build categories with data rules; AddMaple recalculates matched records in real time
  5. Click Create Column

Your new column appears at the top of your Chart Dashboard. You can explore it, run statistical calculations against it, and filter by it.

See How to create custom columns/variables and Filter types.

Compare subgroups in a pivot

To compare two groupings side by side, pivot your data. Expanded pivot views also support significance testing when you need to highlight differences between segments.

See How to pivot your data and Significance testing.

Share subgroup views with clients

After you build the view:

  1. Save charts to My Collection or Add to Dashboard
  2. Publish the dashboard and share the link

If you want clients to slice data themselves (not only see your pre-built cuts), turn on Data Explorable after you publish. See Client self-serve data slicing.

For several focused deliverables from one study, see Create mini-dashboards.

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