Build segments

Building Segments

Quick Start

To build a segment:

1. Clarify what user group the user wants to isolate
2. Select events/metrics and aggregation to compare across segments
3. Identify breakdown dimensions and filters from get_catalog semantic details, list_events, and list_user_traits
4. Render the segmentation insight with render_insight to validate
5. Save with create_insight, or create a discovery when the finding should enter the review/notification workflow

When to Use This Skill

• User asks to define a cohort or audience
• Comparing user groups (e.g., free vs paid, active vs churned)
• Comparing event behavior across properties (e.g., feature usage by plan, region, device)
• Filtering a population for deeper analysis
• Building a segment as input for a funnel, retention, or other insight

Core Workflow

Step 1: Understand the Objective

Ask the user (or infer from context) what group they want to isolate:

• Who are my most valuable users?
• Which users are at risk of churning?
• Who should receive this campaign?

Step 2: Identify Available Dimensions

Use the Altertable MCP server to discover which dimensions and traits are available for filtering:

• get_catalog for semantic dimensions, measures, and table columns
• list_events for event names and event statistics
• list_user_traits for user attributes that can drive segmentation

Match the user's criteria to actual dimension or trait names.

Step 3: Build the Segment Definition

A segmentation setup typically includes:

segment:
  name: segment-name
  description: Human-readable description
  event_definitions:
    - event: "event_name"
  aggregation_mode: Count
  primary_dimension_ref:
    source: source-slug
    name: dimension-name
  breakdowns:
    - source: source-slug
      name: plan_type
  filters:
    - dimension: dimension-name
      operator: Eq
      value: "value"

All filters use AND logic -- every condition must be true.

Step 4: Preview and Validate

Render the segmentation insight via render_insight to check:

• Is the segment size reasonable? (not zero, not everyone)
• Do the results match the user's expectation?
• Are edge cases handled (NULLs, test accounts)?

If the preview looks wrong, adjust filters and preview again.

Step 5: Create the Insight

Once validated:

• Use create_insight with kind: segmentation to save the segment as a chart
• Use create_discovery when the validated finding should flow through the review and notification workflow

Filter Operators

Category	Operators	Use for
Equality	Eq, Ne	Exact match or exclusion
Comparison	Gt, Gte, Lt, Lte	Numeric ranges, date ranges
String	StartsWith, EndsWith, Contains (and Not variants)	Partial text matching
List	In, NotIn	Multiple discrete values
Null	IsNull, IsNotNull	Checking for missing data
IP	IpMatches, IpNotMatches	CIDR range filtering

See Filter operators reference for detailed behavior, type rules, and examples per operator.

Common Pitfalls

• Not previewing before creating -- always preview to catch filter mistakes before saving
• Using wrong operator for the type -- e.g., Contains on a numeric dimension, or Gt on a string
• Forgetting NULL handling -- equality operators don't match NULL; use IsNull/IsNotNull explicitly
• Overly broad segments -- if the segment includes most users, the filters are likely too loose
• Missing exclusion criteria -- always consider whether test accounts, internal users, or bots should be excluded
• Not checking dimension names -- inspect semantic model details and traits to confirm exact names before building filters

Reference Files

• Filter operators - Read for detailed operator behavior, type rules, NULL semantics, and combining patterns
• Dimension references - Read for dimension types, source-qualified references, JSON paths, and join behavior
• Cohort patterns - Read for ready-made segment definitions (lifecycle, value, subscription, behavioral, risk cohorts)