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This guide walks through building a look-alike audience in Look-alike Studio — from picking a seed audience and population dataset to selecting similarity attributes, sizing the output, and finalizing the audience.
A look-alike audience is a new audience derived from an existing seed audience by scoring a larger population dataset and keeping the users that most resemble the seed. Use it to prospect beyond your known customers without leaving the platform.

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Prerequisites

  • An existing seed audience — typically a dataset created with Audience Studio — with at least one identity attribute (such as sha256_hashed_email or maid) mapped via Rosetta Stone
  • A separate population dataset on the same data plane, also with at least one identity attribute mapped via Rosetta Stone
  • Both datasets must share at least one identity attribute so seed users can be matched against the population
The seed and population must be different datasets. Scoring an audience against itself would only return the seed users back.

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Step 1: Pick a seed audience

1

Open Look-alike Studio

Navigate to My Audiences in the sidebar, then open Look-alike Studio.
2

Select a seed audience

Choose the existing audience the look-alike model will learn from. Only audiences on the currently selected data plane are shown.Audiences without identity attributes appear under Not available — they have no join keys, so their users can’t be matched against a population. Map an identity attribute via Rosetta Stone to use them as a seed.If you don’t have any audiences yet, create one in Audience Studio first.

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Step 2: Pick a population dataset

The population is the pool of candidate users the model scores against the seed. Users in the population who most resemble the seed become the look-alike audience.
1

Select a dataset

Choose a dataset of candidate users on the same data plane as the seed.The seed audience itself is disabled — it can’t double as the population. Datasets without identity attributes are also disabled, since seed users couldn’t be matched within them.

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Step 3: Choose similarity attributes

Look-alike Studio classifies the population dataset’s fields into:
  • Identity attributes — Used as join keys to match seed users within the population. Required on both sides.
  • Feature attributes — Used to measure similarity between users. You choose which features the model considers.
  • Metadata — Non-predictive fields excluded from scoring.
1

Confirm join keys

Look-alike Studio shows the identity attributes available on the seed and population. If either side has no identity attributes, scoring isn’t possible — return to Rosetta Stone and map at least one shared identifier on both datasets.
2

Select feature attributes

Pick the features the model should use to compare users — for example, age, interests, purchase categories, or geography. More features can improve quality but increase compute cost.Use the search field to filter the attribute list by name or path.
Choose features that distinguish your seed users from the general population. Universally common attributes add noise without improving similarity scoring.

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Step 4: Configure the output

Decide how large the look-alike audience should be and whether the original seed users are kept.
1

Pick a sizing mode

Choose one:
  • Limit by size — Keep the top N highest-scoring users from the population. Defaults to 10,000.
  • Limit by score — Keep every user whose similarity score meets or exceeds the threshold (0–100%). Defaults to 50%.
Sizing by score produces a variable-size audience that grows or shrinks with the population’s similarity distribution. Sizing by size produces a fixed-size audience regardless of distribution.
2

Choose whether to include seed users

Pick one:
  • New users only — The look-alike audience contains only matched population users. Use this for prospecting.
  • New + original seed users — Adds the seed users back into the output. Use this when you want a single audience that combines known customers with prospects.

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Step 5: Finalize the audience

1

Name the audience

Enter:
  • Display name — The human-readable name shown throughout the platform
  • Unique name — Auto-slugified from the display name and deduped against existing dataset names. Shown read-only.
  • Description — Optional context about the audience’s purpose
  • Tags — Optional labels for organization
2

Review the configuration summary

The summary card recaps every choice you’ve made:
  • Seed audience
  • Population dataset
  • Similarity attributes selected
  • Output limit (size or score threshold)
  • Whether seed users are included
If anything is wrong, return to the relevant step from the builder sidebar.
3

Save details

Click Save details to lock in the finalize step. The Create button becomes available once every step is complete.
4

Create the look-alike audience

Click Create to:
  1. Run the look-alike scoring query against the population
  2. Materialize the top-scoring users (and optionally the seed) as a new audience dataset
  3. Add the dataset to My Audiences
The new audience can then be delivered to connectors from Audience Studio.

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After creation

A look-alike audience is a regular dataset, so you can:
  • Activate it — Open it in Audience Studio to deliver it to connectors
  • Tag or describe it — Edit metadata from the dataset’s detail view
  • Use it as a seed for another look-alike — Chain models when you want to expand iteratively

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Related content

Audience Studio

Build and activate audiences from your data

Building an audience

Create the seed audiences look-alike models learn from

Rosetta Stone mappings

Map identity and feature attributes so datasets can be matched and scored

Audience strategies

Structure underlying data for effective activation