Structuring Audiences for Activation - Narrative I/O Knowledge Base

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)
You understand which identifiers your destination requires (see 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:

sha256_hashed_email
06a240d11cc201676da976f7b49341181fd180da37cbe40a77432c0a366c80c3
a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd
b2c3d4e5f6789012345678901234567890123456789012345678901234bcde

When to use this strategy

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.

You use API automation for dataset creation

If you have automated pipelines that create datasets via the SDK, creating a new dataset per audience is straightforward.

Audiences are static or infrequently updated

If audiences don’t change often, managing them as separate datasets keeps things simple.

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:

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:

SELECT
    sha256_hashed_email
FROM
    company_123.master_audiences
WHERE
    audience_id = '1001'

When to use this strategy

You want simplified data updates

Append new rows to a single dataset instead of managing uploads across many datasets.

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.

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:

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:

SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    gender = 'female'
    AND estimated_household_income >= 75000

Recent high-value customers:

SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    last_purchase_date >= '2024-01-01'
    AND lifetime_value >= 1000

Age-based segments:

SELECT
    sha256_hashed_email
FROM
    company_123.customer_data
WHERE
    age BETWEEN 25 AND 44

When to use this strategy

You want maximum flexibility

Create any audience segment without re-uploading data or pre-defining audiences.

You prefer minimal data preprocessing

Send data to Narrative with minimal transformation and let the platform handle segmentation.

Your audience definitions change frequently

Quickly iterate on audience criteria without modifying source data.

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

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.

Next steps

After structuring your data:

Normalize your data: Map your fields to Rosetta Stone attributes so connectors recognize your identifiers
Configure your connector: Install and configure the connector for your destination platform
Deliver your audiences: Use the connector to push your data to the destination

Data Activation Overview

Understand how connectors deliver data to destinations

Datasets

Learn about dataset structure and management

Filtering & Transforming

NQL basics for filtering audience data

Managing Datasets

Create and manage datasets with the SDK

​Prerequisites

​Strategy 1: One dataset per audience

​Dataset structure

​When to use this strategy

​Advantages

​Considerations

​Strategy 2: Master dataset with audience identifiers

​Dataset structure

​Delivering specific audiences

​When to use this strategy

​Advantages

​Considerations

​Strategy 3: Raw data with dynamic audience creation

​Dataset structure

​Creating audiences with NQL

​When to use this strategy

​Advantages

​Considerations

​Choosing a strategy

​Next steps

​Related content

Data Activation Overview

Datasets

Filtering & Transforming

Managing Datasets

Prerequisites

Strategy 1: One dataset per audience

Dataset structure

When to use this strategy

Advantages

Considerations

Strategy 2: Master dataset with audience identifiers

Dataset structure

Delivering specific audiences

When to use this strategy

Advantages

Considerations

Strategy 3: Raw data with dynamic audience creation

Dataset structure

Creating audiences with NQL

When to use this strategy

Advantages

Considerations

Choosing a strategy

Next steps

Related content