> ## Documentation Index
> Fetch the complete documentation index at: https://docs.narrative.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Building a look-alike audience

> Expand a seed audience by scoring a larger population for users that resemble it, using Look-alike Studio

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.

<Info>
  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.
</Info>

## Prerequisites

* An existing seed audience — typically a dataset created with [Audience Studio](/guides/audience-studio/building-an-audience) — with at least one identity attribute (such as `sha256_hashed_email` or `maid`) mapped via [Rosetta Stone](/guides/rosetta-stone/mapping-schemas)
* 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

<Note>
  The seed and population must be **different datasets**. Scoring an audience against itself would only return the seed users back.
</Note>

## Step 1: Pick a seed audience

<Steps>
  <Step title="Open Look-alike Studio">
    Navigate to **My Audiences** in the sidebar, then open **Look-alike Studio**.
  </Step>

  <Step title="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](/guides/audience-studio/building-an-audience) first.
  </Step>
</Steps>

## 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.

<Steps>
  <Step title="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.
  </Step>
</Steps>

## 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.

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>
</Steps>

<Tip>
  Choose features that distinguish your seed users from the general population. Universally common attributes add noise without improving similarity scoring.
</Tip>

## Step 4: Configure the output

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

<Steps>
  <Step title="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.
  </Step>

  <Step title="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.
  </Step>
</Steps>

## Step 5: Finalize the audience

<Steps>
  <Step title="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
  </Step>

  <Step title="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.
  </Step>

  <Step title="Save details">
    Click **Save details** to lock in the finalize step. The **Create** button becomes available once every step is complete.
  </Step>

  <Step title="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](/guides/audience-studio/building-an-audience).
  </Step>
</Steps>

## After creation

A look-alike audience is a regular dataset, so you can:

* **Activate it** — Open it in [Audience Studio](/guides/audience-studio/building-an-audience) 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

## Related content

<CardGroup cols={2}>
  <Card title="Audience Studio" icon="users" href="/concepts/data-activation/audience-studio">
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  <Card title="Building an audience" icon="user-plus" href="/guides/audience-studio/building-an-audience">
    Create the seed audiences look-alike models learn from
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  <Card title="Rosetta Stone mappings" icon="language" href="/guides/rosetta-stone/mapping-schemas">
    Map identity and feature attributes so datasets can be matched and scored
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