> 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/generating-creative/segments-and-personalization.md).

# Segments and Personalization

## **How Segment-Based Personalization Works in Neon Blue**

Neon Blue personalizes creative by generating multiple content variations and learning which versions perform best for each individual user. As people interact with emails, ads, or on-site content, Neon Blue updates what it knows about them and uses those learnings to improve the next round of generation.

The goal is simple: **use data and generative creativity to deliver content that feels like it was made for each user, without increasing operational overhead.**

The system supports two approaches to segment-based personalization:

1. **Using known segment tags (segments you already maintain)**
2. **Using inferred segments (segments Neon Blue helps discover and validate)**

Both methods allow you to create highly targeted creative without manually building dozens of templates.

***

### **1. Personalizing With Known Segment Tags**

This approach uses segments that your team already tracks, such as:

* “New parents”
* “Ingredient-conscious shoppers”
* “High-intent”
* “Luxury-focused”

Neon Blue receives the user’s segment tag and segment definition via your integration. This information becomes part of the prompt context for generation.

Neon Blue will:

* Generate variations tailored to each segment
* Test those variations across the users who belong to that segment
* Automatically personalize future content for each segment based on performance

This is useful when your organization already has strong internal segmentation definitions and you want Neon Blue to produce consistent, segment-specific content at scale.

***

### **2. Personalizing With Inferred Segments**

Inferred segments allow you to define strategic hypotheses about your audience, then let Neon Blue learn which users actually fit which pattern.

You start by defining the segments you want to explore. For example, a fashion brand may suspect that different customers buy for different reasons:

* Seasonal relevance
* Urgency and offers
* Creative aesthetic and styling
* Sustainability themes
* “Essentials-only” minimalists

You create a custom segment variable in Neon Blue and describe what each segment represents. These definitions guide the generation of segment-specific creative variations.

Neon Blue will then:

* Generate creative variations aligned to each hypothesized segment
* Test these variations across users
* Observe which segment themes resonate with which behavioral patterns
* Infer a user’s likely segment based on performance, events, and traits
* Tag future users with similar patterns and show them the matching variant

Over time, the system probabilistically learns segment fit and continuously refines which creative style or message works for each type of user.

***

### **How Neon Blue Decides What Content to Show**

Regardless of whether segments are known or inferred, Neon Blue’s creative decisioning system follows the same loop:

1. Generate variations (segment-aware prompts if applicable)
2. Allocate variations to users for exploration
3. Measure performance per user and per segment
4. Update the user’s profile and inferred attributes
5. Use those learnings when generating or selecting the next variant

This allows Neon Blue to evolve segment definitions, optimize creative in real time, and scale personalization without requiring manual creative branching.

***

### **What This Enables**

* Personalization without manually building separate templates
* Segment-level testing that adapts over time
* Automatic identification of new “micro-segments” based on behavior
* Creative that becomes more accurate and relevant the longer the system runs