Prompt Labels & Taxonomy
Labels are tags on your prompts that let you ask questions like "what's my visibility on branded queries vs generic ones?" or "how do I perform on early-funnel discovery prompts vs purchase-ready ones?"
Your prompts aren't all the same — some ask about your brand directly, others are category-level questions where you hope to appear. Labels make this structure explicit so every chart and metric can be sliced by the dimensions that matter to your strategy.
What Labels Are
Labels are flat, cross-cutting tags that hang directly off prompts. They are not another level in your brand, topic and prompt structure; they sit alongside it. Think of labels as dimensions for filtering and grouping, similar to tags on a blog post.
Each prompt carries exactly one value per label dimension. Labels are auto-derived when prompts are created.
Branded vs Generic
The Branded label answers: does this prompt mention your brand by name?
- Branded: "Is the Example Brand Trail 2 good for running?" — directly asks about you
- Generic: "Best sustainable running shoes 2026" — category query where you compete for inclusion
This split matters. A prompt that names your brand is a test of whether AI knows who you are; a generic prompt is where you compete to be included. Prompts that name your brand are kept out of your headline Visibility Score, since an answer to them will almost always mention you.
Intent Taxonomy
The Intent label maps each prompt to a stage in the buyer journey. A2ZReach uses an 11-layer taxonomy:
| Code | Intent Layer | Example Prompt |
|---|---|---|
| D | Discoverability | "Best running shoe brands" |
| P | Problem-First | "Shoes for plantar fasciitis" |
| C | Consideration | "Example Brand vs Competitor A comparison" |
| E | Expert / Authority | "What do podiatrists recommend?" |
| S | Customer Service | "Example Brand return policy" |
| A | Asset Intelligence | "Example Brand sustainability practices" |
| L | Loyalty / Expansion | "New Example Brand releases 2026" |
| I | Identity / Aspiration | "Eco-friendly runner lifestyle brands" |
| T | Temporal / Seasonal | "Best summer running shoes 2026" |
| X | Ecosystem / Integration | "Running shoes compatible with Strava" |
| RC | Reputation / Crisis | "Example Brand quality complaints" |
How Labels Enable Breakdowns
Once every prompt carries labels, all metrics become sliceable:
- Visibility by intent: Are you strong on Discoverability but weak on Consideration? That tells you buyers find you but don't shortlist you.
- Visibility by branded vs generic: If branded is 95% but generic is 20%, AI knows who you are but doesn't recommend you to category shoppers.
- Exclusion by intent: Which buyer journey stages exclude you most? Focus GEO efforts where the gap is largest.
Auto-Derived
Labels are assigned automatically when prompts are created — Branded is derived from your brand name and domain, Intent from the prompt's phrasing and layer code. Editing a label by hand is not available yet.