Multi-Select Average Count

Multi-Select Average Count analyzes how many options are typically chosen per record in a multi-select picklist field, and reports the average, minimum, and maximum selected values along with a distribution across 0, 1, 2, and 3+ selections.

Use this analysis to check whether a multi-select field is being used as intended, or whether it's consistently left empty, treated as a single-select, or used as a catch-all with too many options chosen.

Detailed Job Results

The Avg Selections per Field bar graph shows the average number of options selected per multi-select picklist. Hover over a field bar to view its details.
Below, the Field Details table displays per field the number of records with a value in that field ( # Record with value), the average number of selected options (Average # Selection), the number of records where only one option was selected ( # Records 1 Selected), and the percentage of records where only one option was selected ( % Records 1 Selected).

Key Insights

• Average selected values per field: find out whether users typically pick none, one, or several options, giving a quick read on how each field is actually being used.
• Single-select behavior: if many records have exactly 1 value selected, the field may perhaps be better modeled as a regular single-select picklist.
• Overused fields: a high average of selected options, or many records with three or more selections, can indicate a "select everything" pattern, often caused by overlapping or unclear option values.

Recommended Actions

Scenario	Actions
Most records have only one selection for a field	- Consider converting the field to a single-select picklist to simplify reporting and user experience.
Average number of selected options is high, or many records have three or more selections	- Review the options list for overlapping or overly generic values. - Add guidance so users select only what truly applies. - Consider splitting the field into multiple fields.
Field is used in dashboards, segments, or scoring models	- Confirm the field has meaningful variation before relying on it. - Redesign the field first if usage is skewed toward empty or "select all".