> ## 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.

# Structuring Audiences for Activation

> Strategies for organizing your datasets to deliver audiences to advertising platforms

Before delivering audience data to a connector, you need to structure your data in datasets. This guide covers three common strategies for organizing your data, with recommendations for when to use each approach.

## Prerequisites

Before you begin:

* You have data with deliverable identifiers (hashed email, hashed phone, or mobile ad IDs)
* Your identifiers are properly formatted and hashed (see [Hashing PII for Upload](/guides/ingestion/hashing-pii))
* You understand which identifiers your destination requires (see [Data Activation Overview](/concepts/data-activation/overview))

***

## Strategy 1: One dataset per audience

Create a separate dataset for each audience you want to deliver. The dataset name and description correspond directly to the audience.

### Dataset structure

| Field                 | Description                                    |
| --------------------- | ---------------------------------------------- |
| `sha256_hashed_email` | The hashed identifier for each audience member |

Example data:

```csv theme={null}
sha256_hashed_email
06a240d11cc201676da976f7b49341181fd180da37cbe40a77432c0a366c80c3
a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd
b2c3d4e5f6789012345678901234567890123456789012345678901234bcde
```

### When to use this strategy

<AccordionGroup>
  <Accordion title="You build audiences outside Narrative">
    If your audience creation happens in external systems (CRM, CDP, marketing automation), then export each audience as a separate file for upload.
  </Accordion>

  <Accordion title="You use API automation for dataset creation">
    If you have automated pipelines that create datasets via the [SDK](/guides/sdk/managing-datasets), creating a new dataset per audience is straightforward.
  </Accordion>

  <Accordion title="Audiences are static or infrequently updated">
    If audiences don't change often, managing them as separate datasets keeps things simple.
  </Accordion>
</AccordionGroup>

### Advantages

* **Simplicity**: Each dataset maps directly to one audience
* **Clear ownership**: Easy to track which dataset powers which audience
* **Selective updates**: Update only the audiences that have changed

### Considerations

* Requires creating a new dataset for each new audience
* Can become difficult to manage with hundreds of audiences
* Duplicated records across datasets if users belong to multiple audiences

***

## Strategy 2: Master dataset with audience identifiers

Maintain a single dataset containing all audience members, with columns that identify which audience each record belongs to.

### Dataset structure

| Field                 | Description                        |
| --------------------- | ---------------------------------- |
| `sha256_hashed_email` | The hashed identifier              |
| `audience_id`         | Unique identifier for the audience |
| `audience_name`       | Human-readable audience name       |

Example data:

```csv theme={null}
sha256_hashed_email,audience_id,audience_name
06a240d11cc201676da976f7b49341181fd180da37cbe40a77432c0a366c80c3,1001,high_value_customers
a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd,1001,high_value_customers
b2c3d4e5f6789012345678901234567890123456789012345678901234bcde,1002,recent_purchasers
c3d4e5f6789012345678901234567890123456789012345678901234cdef,1002,recent_purchasers
```

### Delivering specific audiences

Use NQL to filter for a specific audience when delivering:

```sql theme={null}
SELECT
    sha256_hashed_email
FROM
    company_123.master_audiences
WHERE
    audience_id = '1001'
```

### When to use this strategy

<AccordionGroup>
  <Accordion title="You manage many related audiences">
    If you have dozens or hundreds of audiences that share similar structures, a single dataset is easier to maintain.
  </Accordion>

  <Accordion title="You want simplified data updates">
    Append new rows to a single dataset instead of managing uploads across many datasets.
  </Accordion>

  <Accordion title="You need to add new audiences without creating new datasets">
    Adding a new audience is as simple as appending rows with a new `audience_id`.
  </Accordion>
</AccordionGroup>

### Advantages

* **Centralized management**: One dataset to maintain
* **Easy audience additions**: New audiences are just new rows
* **Efficient updates**: Append new data without managing multiple datasets

### Considerations

* Requires NQL filtering to extract specific audiences
* Larger dataset size may affect query performance
* Need to maintain unique audience IDs

***

## Strategy 3: Raw data with dynamic audience creation

Upload a master dataset with all available customer attributes, then use NQL to define audiences dynamically based on any attribute combination.

### Dataset structure

| Field                        | Description                  |
| ---------------------------- | ---------------------------- |
| `sha256_hashed_email`        | The hashed identifier        |
| `age`                        | Customer age                 |
| `gender`                     | Customer gender              |
| `estimated_household_income` | Income range or value        |
| `last_purchase_date`         | Date of most recent purchase |
| `lifetime_value`             | Total customer value         |

Example data:

```csv theme={null}
sha256_hashed_email,age,gender,estimated_household_income,last_purchase_date,lifetime_value
06a240d11cc201676da976f7b49341181fd180da37cbe40a77432c0a366c80c3,34,male,85000,2024-01-15,1250.00
a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd,28,female,52000,2024-02-20,890.00
b2c3d4e5f6789012345678901234567890123456789012345678901234bcde,45,female,120000,2024-01-05,3200.00
```

### Creating audiences with NQL

Define audiences on-the-fly using any attribute combination:

**High-income females:**

```sql theme={null}
SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    gender = 'female'
    AND estimated_household_income >= 75000
```

**Recent high-value customers:**

```sql theme={null}
SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    last_purchase_date >= '2024-01-01'
    AND lifetime_value >= 1000
```

**Age-based segments:**

```sql theme={null}
SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    age BETWEEN 25 AND 44
```

### When to use this strategy

<AccordionGroup>
  <Accordion title="You want maximum flexibility">
    Create any audience segment without re-uploading data or pre-defining audiences.
  </Accordion>

  <Accordion title="You prefer minimal data preprocessing">
    Send data to Narrative with minimal transformation and let the platform handle segmentation.
  </Accordion>

  <Accordion title="Your audience definitions change frequently">
    Quickly iterate on audience criteria without modifying source data.
  </Accordion>
</AccordionGroup>

### Advantages

* **Maximum flexibility**: Define any audience criteria without changing data
* **Minimal preprocessing**: Upload raw data, segment later
* **Rapid iteration**: Test new audience definitions instantly

### Considerations

* Requires familiarity with NQL for audience creation
* Complex queries may take longer to execute on large datasets
* Attribute data must be normalized consistently

***

## Choosing a strategy

| Factor                   | Strategy 1 (Per Audience) | Strategy 2 (Master + IDs) | Strategy 3 (Raw Data) |
| ------------------------ | ------------------------- | ------------------------- | --------------------- |
| **Number of audiences**  | Few (1-20)                | Many (20-100+)            | Variable/unknown      |
| **Audience definitions** | Fixed                     | Fixed                     | Dynamic               |
| **Data preprocessing**   | Done externally           | Partial                   | Minimal               |
| **NQL required**         | No                        | Basic filtering           | Advanced queries      |
| **Flexibility**          | Low                       | Medium                    | High                  |

<Info>
  You can combine strategies. For example, use Strategy 3 for your core customer data while maintaining Strategy 1 datasets for specialized audiences from external sources.
</Info>

***

## Next steps

After structuring your data:

1. **[Normalize your data](/guides/rosetta-stone/mapping-schemas)**: Map your fields to Rosetta Stone attributes so connectors recognize your identifiers

2. **Configure your connector**: Install and configure the connector for your destination platform

3. **Deliver your audiences**: Use the connector to push your data to the destination

***

## Related content

<CardGroup cols={2}>
  <Card title="Data Activation Overview" icon="bullseye-arrow" href="/concepts/data-activation/overview">
    Understand how connectors deliver data to destinations
  </Card>

  <Card title="Datasets" icon="table" href="/concepts/primitives/datasets">
    Learn about dataset structure and management
  </Card>

  <Card title="Filtering & Transforming" icon="filter" href="/guides/nql/filtering-transforming">
    NQL basics for filtering audience data
  </Card>

  <Card title="Managing Datasets" icon="gear" href="/guides/sdk/managing-datasets">
    Create and manage datasets with the SDK
  </Card>
</CardGroup>