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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.

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

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

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