Glossary
Brand visibility check
A fast baseline before committing to continuous tracking.
Output is decision-ready: LLM-Score, SoV, quotes, and priority fixes.
Definition
A brand visibility check is a one-time Getllmspy run that measures how often and how accurately major LLMs mention your brand in a defined scenario. You provide brand context and niche, we run the prompt pack across selected model coverage, and return a dated snapshot you can share internally.
Example output view
| Segment | Visibility | Correctness | Action |
|---|---|---|---|
| Commercial prompts | 31% | 65% | Add product comparison pages |
| Educational prompts | 44% | 79% | Scale this format to new topics |
| Competitor prompts | 22% | 58% | Improve battlecard fact blocks |
Mini chart (visibility by segment):
Commercial 31% ▇▇▇▇▇
Educational 44% ▇▇▇▇▇▇▇
Competitor 22% ▇▇▇▇
This helps prioritize the next sprint by segment, not by guesswork.
How it's computed
The engine runs prompts in parallel, stores raw outputs, then scores mention presence, correctness, sentiment, and competitor overlap. Optional fanout queries improve robustness. Weekly monitoring uses the same logic with recurring cadence and alerting.
Quick baseline math
If your brand appears in 38 of 100 prompt-model answers and 27 of those mentions are factually correct:
Visibility rate = 38 / 100 = 38%
Correct mention rate = 27 / 38 = 71%
You can now separate a reach issue (low visibility) from a quality issue (low correctness).
How to read it
Use the first run as a baseline: where you win, where you are invisible, and where models disagree. Re-run after major content or PR changes to measure real movement. Keep the same prompt pack to preserve comparability.
What to do right after the first check
- Pick one target segment where visibility is low but revenue impact is high.
- Collect 5-10 failing answers and classify root causes.
- Patch source pages (facts, structure, citations), not prompts only.
- Re-run the same prompt pack in 7-10 days.
- Promote fixes only if they improved both visibility and correctness.
Baseline quality bands (first run)
| Metric | Weak baseline | Promising baseline |
|---|---|---|
| Visibility rate | <25% | 35%+ |
| Correct mention rate | <60% | 75%+ |
| Segment dispersion | One segment dominates | Balanced by intent cluster |
Use these bands to set realistic quarter goals before launching continuous monitoring.
Add one governance rule: keep the same denominator (pack + model set) for at least one month before reporting trend claims.
When to use
- Vendor selection bake-offs.
- Pitch prep for agencies selling GEO retainers.
- Post-incident verification that fixes stuck.