The Customer Segmentation table lists which customers belong to each segment at any point in time. One row per customer per segment instance. Learn more about customer segmentation.

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Table Guide

What You Can Answer With This Table

• Which customers are in a specific segment? — filter by segment_id
• How has segment membership changed over time? — compare customer counts across instance_event_date, optionally group by segment_by

Before You Query

• A segment is a group of customers matching defined rules. A segment instance is a snapshot of that segment at a point in time (generated daily or every other day).
• To match results with the Customer Segments UI, filter for the most recent segment instance per segment. Without this, queries include all historical instances and totals will appear inflated.
• The join key is customer_identity. Depending on segment_by, this contains either an email address or a customer ID. When joining to other tables, match against the corresponding field (e.g., customer_id on the Orders table, or email_address on the Customers table).

Key Relationships

Table	Join Key	Use This Join To
Customers	customer_identity to customer_id or email_address	Understand who is in each segment using profile attributes
Orders	customer_identity to customer_id or customer_email	Compare what segment members bought and spent
Pixel Orders	customer_identity to customer_id or customer_email	Analyze which channels or campaigns drove segment members' orders
Customer Segmentation Analytics	segment_id + instance_event_date	Compare segment performance around a pinned_event_date using pre-aggregated metrics

When to Use a Different Table

• Use Customer Segmentation Analytics table when you need segment performance metrics such as revenue, AOV, or purchase frequency. The Customer Segmentation table is for segment membership.
• Use Customers table when you need customer profiles without segment context. The Customer Segmentation table is for segment membership.

View the full Triple Whale Data Ontology →

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Dimensions

Dimensions are immutable properties that can be used for grouping data.

Measures

Measures are numeric fields that can be aggregated and/or combined to calculate new metrics.