Canonical identity
Write one factual sentence that names the entity, category, core offering, audience and outcome. Keep the creative layer, but make sure the boring sentence exists.
Define the condition
Meaning makes you understood.
Meaning is the model of what the brand is and what it should be associated with. It is not the tagline. It is the explicit identity, category, audience, offer, problem set, topical scope and relationship graph that a machine can resolve without guessing.
MeaningMakes you understood
Define the condition:
- Canonical identity and category
- Audience, journey and target prompts
- Topics, use cases and relationships
- Disambiguation where entities can collide
Core check: Can a machine accurately explain what we are, who we are for, what we do and what we should be known for?
Open the full Meaning diagram ↗Why it matters
Makes you understood
If the brand is vague, every downstream step gets harder. Retrieval can surface the wrong category. Third-party mentions can reinforce the wrong association. Structured data can make an unclear fact more explicit without making it more correct.
Core question
Write one factual sentence that names the entity, category, core offering, audience and outcome. Keep the creative layer, but make sure the boring sentence exists.
What to do
Define Meaning deliberately.
Audience and journey
Map who buys, who uses, what triggers the need, and how questions change from discovery through evaluation and action.
Prompt universe
Build a representative prompt set around category, problem, comparison, recommendation, alternatives, use cases, trust and transaction.
Entity relationships
Map the people, products, categories, audiences, problems, topics, markets and places that define the brand.
Disambiguation
Identify collisions with similar brands, products, people or places, then use names, aliases, context and relationships to resolve the right entity.
How to measure it
Use a baseline, not a vibe.
- Accuracy of model descriptions
- Correct category and audience association
- Prompt groups where the brand appears in the right context
- Frequency of wrong or missing facts
- Consistency of entity relationships across sources
Next step
Connect the pillar to the rest of MAPS.
Meaning works best when the other conditions support it. Use the full audit to see whether the constraint actually sits here or somewhere else.