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Overview

The Slicer tab in Identity Insights displays identity overlap in a pivot table format, showing how different identifiers relate to each other. Understanding how to read this table helps you assess identifier relationships, identify gaps in your identity graph, and evaluate vendor performance.
Prerequisites:
  • Access to Identity Insights dashboard
  • At least two identifiers configured and collecting data
  • Identity overlap data is updated daily

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Steps

1

Open the Slicer tab

Navigate to Identity > Identity Insights and ensure you’re on the Slicer tab (this is the default view).
2

Understand table orientation

The pivot table reads top-to-bottom rather than left-to-right. Start with the identifier in the top row, then read down the column to see overlap percentages.
3

Read overlap percentages

For each identifier in the top row, read down its column to see what percentage of users with each identifier also have the top-row identifier. For example, if AppNexus is in the top row, reading down shows what percentage of users with each identifier also have AppNexus.
4

Switch to absolute values

Use the view toggle to switch between percentage overlap and absolute values (sum view). Absolute values show the actual number of users who have both identifiers.
5

Identify relationships

High overlap percentages indicate strong relationships between identifiers - these identifiers are commonly found together. Low overlap may indicate gaps in identity collection or resolution.
6

Assess identifier scale

Use the table to understand the relative scale of each identifier and how they compare to each other. This helps identify which identifiers provide the most coverage in your identity graph.

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Reading the Pivot Table

Example interpretation:
  • If email_sha256 is in the top row and appnexus shows 45% in its column, this means 45% of users with AppNexus also have a hashed email
  • If appnexus is in the top row and email_sha256 shows 30% in its column, this means 30% of users with hashed emails also have AppNexus
  • The percentages are not symmetric - they represent different perspectives of the same relationship
Tips for interpreting overlap:
  • High overlap (70%+) between two identifiers indicates they’re commonly found together and may have similar user coverage
  • Low overlap (under 20%) may indicate the identifiers target different user segments or there are gaps in collection
  • Use absolute values to understand the actual scale of overlapping users
  • Compare overlap percentages across different identifier pairs to identify the strongest relationships
  • Overlap data helps assess which identity vendors are performing well relative to each other
Important:
  • The pivot table orientation (top-to-bottom) can be counter-intuitive - always start with the top-row identifier
  • Overlap percentages are directional - the percentage depends on which identifier is in the top row
  • Identity overlap data reflects the Past 7 Days and Past 30 Days - recent changes may take time to appear
  • Identifiers that have been removed from your allow-list may still appear if data was collected in the reporting time range

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Next Steps

Accessing Identity Insights

Learn how to navigate the Identity Insights dashboard

Monitoring Data Ingestion

Track daily data collection patterns

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