> ## 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. # Look-alike Studio > Expand a seed audience by scoring a larger population against the seed using a guided builder Look-alike Studio is the platform's visual look-alike audience builder. It takes a small **seed** dataset of users you already value (converters, high-LTV customers, opted-in subscribers) and scores a larger **population** dataset to find users who resemble the seed — producing a new audience of likely-to-convert users without writing NQL by hand. ## How Look-alike Studio works A look-alike audience is built from three inputs: 1. **A seed dataset** — Users who exhibit the behavior you want to find more of. Typically small (thousands to low millions of records). 2. **A population dataset** — The larger universe to score against. Must share a join-key identifier with the seed (for example, both datasets map `sha256_hashed_email`). 3. **Shared attributes** — Categorical or continuous fields present on both datasets that describe each user. These are the features used to score similarity. The builder generates a multi-stage NQL workflow that computes per-attribute weights from the seed, scores every population user against the seed distribution, and materializes the highest-scoring users as a new audience dataset. ## Scoring model Look-alike Studio uses a Naive Bayes model with Gaussian terms for continuous attributes: * **Categorical attributes** (country, device type, product category) contribute log-likelihood weights based on how much more frequently each value appears in the seed versus the population. * **Continuous attributes** (age, lifetime value, days since last visit) contribute Gaussian density terms parameterized by the seed's mean and variance for each attribute. * **Attribute weights** down-weight low-information attributes — an attribute that has the same distribution in the seed and the population contributes little signal. Each population user receives a single composite score between 0 and 1 representing how seed-like they are. ## Builder steps ### 1. Seed selection Choose the dataset of users you want to find more of. The dataset must have [Rosetta Stone attribute mappings](/guides/rosetta-stone/mapping-schemas) so the builder can identify join keys and shared features. Only datasets on the currently selected [data plane](/concepts/primitives/data-planes) are shown. Pipeline intermediates and other audiences are hidden from the picker. ### 2. Population selection Choose the larger dataset to score against. The population must share at least one **join-key identifier attribute** with the seed (such as `sha256_hashed_email` or `narrative_id`) so the builder can identify which population users overlap with seed users and exclude or include them appropriately. If the seed and population do not share a join-key attribute, the dataset is shown as ineligible. ### 3. Attribute selection Look-alike Studio classifies every attribute that exists on both datasets into one of three roles: | Role | Description | Used for scoring? | | --------------- | ------------------------------------------------------------- | ------------------------------------------ | | **Identity** | Identifiers like hashed email, narrative\_id, MAID | No — used only to join seed and population | | **Categorical** | Low-to-medium cardinality fields like country, device, gender | Yes — Naive Bayes | | **Continuous** | Numeric fields like age, LTV, frequency | Yes — Gaussian density | You select which classified attributes to include as scoring features. More attributes is not always better — irrelevant or correlated attributes add noise. Start with 3–8 attributes that you believe describe what makes the seed distinctive. ### 4. Output configuration Choose how the look-alike audience is sized: * **Limit by size** — Return the top-N highest-scoring population users (for example, the top 100,000). * **Limit by score** — Return every population user whose score exceeds a threshold (for example, score ≥ 0.5). You also choose whether to include the original seed users in the output: * **New users only** — Only population users not already in the seed are returned. Use when you want net-new reach. * **New + original seed users** — Seed users are added to the output. Use when you want a single combined audience to activate. ### 5. Finalize Configure the output dataset metadata: * **Audience name** (required) — Identifies the audience in the platform and downstream systems. * **Description** — Optional context. * **Tags** — Optional labels. The system automatically applies the `_nio_audience` and `_nio_lookalike` tags so the audience appears under **My Audiences** and is recognizable as look-alike-derived. Name uniqueness is enforced against all existing datasets, including hidden interactive results and look-alike pipeline intermediates. ## What Look-alike Studio creates When you click **Create Look-alike Audience**, the builder: 1. Assembles the seed, population, attribute selections, and output config into a multi-stage workflow YAML. 2. Submits the workflow via the workflow engine with `trigger_immediately`. 3. Materializes around a dozen intermediate views (canonical feature combos, per-attribute distributions, attribute weights, scored candidates) tagged `_nio_lookalike_intermediate`. These are hidden from the standard dataset catalog so only the final audience appears under **My Audiences**. 4. Materializes a final audience dataset with the `_nio_audience` and `_nio_lookalike` tags, your display name, description, and any user-supplied tags applied as part of the `CREATE MATERIALIZED VIEW` so the audience is born fully tagged even if you leave mid-run. The run surfaces a dismissible progress dialog — closing it does not cancel the workflow, which continues server-side. When the run completes, the audience opens in the standard audience metadata drawer and is available for delivery through any compatible [connector](/reference/connectors/index). ## Resuming or restarting * **Dismiss progress, keep running** — Closing the progress dialog leaves the workflow running. The audience appears under **My Audiences** when it finishes. * **Retry on error** — A failed run shows an error dialog with a **Retry** action that resubmits the same workflow. * **Start Over** — Cancels polling and resets all builder state so you can build a different look-alike audience. ## Related content Step-by-step guide to creating your first look-alike audience Filter-based audience builder for direct segmentation Strategies for organizing data for activation Technical details for each destination connector