Understanding the GEO Score
The synthetic metric that summarizes your AI visibility
Definition
The GEO Score (Generative Engine Optimization) is a composite 0-100 indicator measuring your visibility in conversational AI engine responses. It's calculated separately for each keyword, then aggregated into a project-level average.
Calculation components
- Mention frequency (weight 40%): how many times your brand appears in AI responses for this keyword.
- Citation position (weight 20%): is your brand mentioned first, in the middle or at the end of the response? A head mention carries more weight.
- Sentiment (weight 15%): positive, neutral or negative tonality associated with your brand in the mention's context.
- Technical citability (weight 15%): elements on your pages that favor AI citation (schema.org, Q&A structure, FAQ, quantified statistics).
- Engine diversity (weight 10%): a homogeneous score across all five engines is better than a dominant score on only one (too much dependency on a single provider).
Threshold interpretation
- 80-100: Dominant — you're mentioned in most AI responses, with positive sentiment, on multiple engines. Goal: maintain and capitalize.
- 60-79: Solid — regular presence in responses. Marginal improvement possible via targeted optimizations.
- 40-59: Average — you appear sometimes but not systematically. Content effort and schema markup needed.
- 20-39: Weak — competitors largely outperform you. High priority to remedy via GEO-optimized content and citability reinforcement.
- 0-19: Critical — invisible on this keyword. Probably a topic you haven't published much on, or whose content isn't structured for citation.
Time evolution
A keyword's GEO Score can vary significantly between scans, because AI responses aren't strictly deterministic (temperature variations, model updates, seasonal effects). PivotSEO averages each scan's results to smooth these variations. For serious analysis, compare over 3+ scans.
Tip :Improve your GEO Score: the three most effective levers are (1) add a structured FAQ with JSON-LD FAQPage schema, (2) publish content dense in citable quantified statistics, and (3) obtain mentions on sources frequently cited by AIs (Wikipedia, Reddit, specialized forums, reference media).
