> For the complete documentation index, see [llms.txt](https://docs.neonblue.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.neonblue.ai/data/tags-personas-and-passback.md).

# Tags, Personas, and Passback

#### 1. Purpose of this page

This page explains:

* What “tags” mean in the context of Neon Blue
* How personas are inferred
* How assignment data is shared back for use in warehouse analytics and downstream tools

The term “tags” can refer to different types of labels; this page distinguishes them clearly.

***

#### 2. Types of tags in Neon Blue

In Neon Blue, tags fall into three broad categories:

1. **Experiment tags**
   * Identify which gate or variant a user was assigned to.
   * Backed by the `gate_assignments` and `experiment_assignments` tables in the outbound Snowflake share.
2. **Persona tags**
   * Describe inferred user preferences or segments (for example, preference for certain content styles). 
3. **Custom or business tags**
   * Any additional labels maintained in a customer’s own systems (for example, internal segments, lifecycle labels).
   * Often joined to Neon Blue assignments to run segment-level analyses.

This documentation focuses on experiment and persona tags generated by Neon Blue and shared back via Snowflake.

***

#### 3. Experiment tags and assignments

Experiment tags express “who saw what, when” in Neon Blue experiments.

They are represented in the outbound share as:

* **Gate assignments**
  * Indicate whether users are in global holdout, baseline, or Neon Blue for a given gate.
  * Used for program- and flow-level analysis.
* **Experiment assignments**
  * Indicate which variant each user was assigned at a specific touchpoint (email, banner, etc.).
  * Used for variant- and creative-level analysis.

These tags are:

* Sticky for analysis (assignments do not change retroactively).
* Sufficient to reconstruct exposure and measure outcomes in the customer’s own warehouse or experimentation platform.

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#### 4. Persona tags

Persona tags capture user-level preferences derived from experiment results and behavioral data.

Conceptually:

* Neon Blue generates variants that correspond to different content philosophies (for example, value-focused vs ingredient-focused).
* Over time, users are exposed to these variants across multiple touchpoints.
* The platform observes which types of variants each user responds to. Neonblue-Coterie-Tags-and-Conte…

From this, Neon Blue infers **personas**, such as:

* “Prefers ingredient-detail content”
* “Prefers short, direct messaging”
* “Prefers visually-led banners”

Persona assignment data is exposed via Snowflake as a table similar to:

`neon_blue_outbound.persona_assignments`

with columns such as:

* `user_id` – canonical user id
* `persona_id` – internal identifier
* `persona_name` – human-readable label
* `confidence_score` – numeric value (for example, between 0 and 1) indicating assignment confidence
* `updated_at` – last update timestamp 

Persona tags can then be:

* Joined to internal profile tables and used for segmentation in analytics
* Exported to messaging platforms via existing mapping tables for targeting

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#### 5. Tag passback via Snowflake

Neon Blue returns both experiment and persona tags through the outbound Snowflake share:

* Gate assignment table (with gate and variant keys)
* Experiment assignment table (with experiment and variant identifiers)
* Persona assignment table (with persona identifiers and confidence)

These tables:

* Are updated on a regular cadence (for example, daily).
* Can be made available to internal tools and BI platforms.
* Provide a complete record of the interaction between users and Neon Blue’s experiments.

***

#### 6. Using tags in downstream systems

**In the data warehouse:**

* Join assignment tables to `events` and `orders` to measure performance by gate, experiment, variant, and persona.
* Combine persona tags with internal segments to understand interactions between preferences and lifecycle stage.

**In experimentation platforms:**

* Use Neon Blue’s assignment tables as “source-of-truth exposure logs” and configure analysis-only experiments in an internal experimentation platform, if desired. 

**In messaging platforms and CRMs:**

* Join persona tags and assignment tags to messaging IDs via the mapping tables described earlier.
* Use persona segments to tailor campaigns outside of Neon Blue-managed flows.

***

#### 7. Guidelines for interpreting tags

* Experiment tags are deterministic for a given user and context; persona tags are probabilistic and improve over time. 
* Persona tags should be treated as hints about preference, not absolute ground truth.
* Confidence scores can be used to:
  * Filter to high-confidence personas for activation
  * Down-weight low-confidence segments in analysis