At a Glance
The AI Overall Score (VSCA Score) is a single number from 0 to 100 that answers one question:
"How well is my brand performing when consumers ask AI for recommendations?"
When someone asks ChatGPT, Gemini, Perplexity, or Claude about products or services in your category, does the AI mention your brand? Does it speak positively about you? Does it recommend you over competitors?
The AI Overall Score synthesizes all of these factors into one easy-to-understand metric.
Business Impact
AI-driven discovery is rapidly becoming a major revenue channel. Based on our analysis of client performance data, score improvements correlate with meaningful business outcomes:
| Score Change | AI Channel Revenue | Market Share |
|---|---|---|
| +5 points | +10-20% | +0.1-0.3% |
| +10 points | +25-40% | +0.3-0.5% |
| +20 points | +60-100% | +0.5-1.0% |
Results vary by industry, competitive landscape, and baseline score. These ranges reflect patterns observed across our client base, not guaranteed outcomes.
We have filed a patent application for the VSCA methodology.
How to Read Your Score
| Score | Rating | What It Means |
|---|---|---|
| 90-100 | Excellent | AI frequently recommends your brand as a top choice |
| 80-89 | Very Good | Strong AI presence with minor gaps to address |
| 70-79 | Good | Above average performance, clear room for growth |
| 60-69 | Acceptable | Meeting minimum expectations in AI visibility |
| 40-59 | Needs Work | Below average; competitors likely outperforming you |
| 0-39 | Poor | Significant AI visibility problems requiring attention |
The Four Dimensions: VSCA
We call our methodology VSCA - named after the four dimensions it measures:
Visibility - "Are we seen?"
This measures how often and how prominently AI mentions your brand when users ask relevant questions.
What we look at:
- How frequently AI mentions your brand (Exposure Rate)
- How often you're mentioned first (First Mention Rate)
- Your average ranking position when listed
Example: "Out of 100 questions about healthy restaurants, AI mentioned Brand X 63 times, and listed it first 42% of those times."
Business impact: Visibility directly drives traffic. Position #1 in AI responses captures 40% of all clicks, and the top 3 positions capture 68.7%. Being cited by AI drives +35% more organic clicks.
Sentiment - "Are we liked?"
This measures how positively AI describes your brand when it mentions you.
What we look at:
- The balance of positive vs. negative phrases AI uses about your brand
Example: "When AI talks about Brand X, 74% of the descriptions are positive (e.g., 'known for quality,' 'customer favorite') and 26% mention concerns."
Business impact: Sentiment directly drives conversion. Positive AI recommendations increase purchase conversion by +42%. Positive sentiment drives +20-30% higher conversion rates.
Competitiveness - "Are we winning?"
This measures how you stack up against competitors in AI recommendations.
What we look at:
- Your share of all brand mentions in the category (Share of Voice)
- How your sentiment compares to competitors' average
- How often you capture the #1 recommendation spot
Example: "Out of all restaurant brands AI mentioned, Brand X accounted for 23% of mentions and captured 45% of all first-position recommendations."
Business impact: Competitiveness directly drives market share. According to the "10:0.5 rule" from Binet & Field's research, every +10 points of Extra Share of Voice -> +0.5% market share growth.
Authority - "Are we trusted?"
This measures whether AI treats your brand as a reliable information source.
What we look at:
- How often AI cites sources in its responses
- How often your website specifically is cited
Example: "Of all sources AI cited, 6.6% were links to Brand X's official website."
Business impact: Authority drives trust and engagement. AI traffic shows 63% engagement rate and converts at 91% of traditional search rates.
What You Can Do Next
Your AI Overall Score reveals where you stand - but understanding the score is just the beginning. Improving it requires targeted action.
Start with your score:
- Identify which of the four dimensions (V, S, C, A) is dragging your score down
- Monitor how your score trends month-over-month
- Track how competitors are moving relative to you
Go deeper with our analysis:
We offer comprehensive diagnostics that pinpoint exactly why AI platforms are (or aren't) recommending your brand - and what to do about it. Our services include:
- Root cause analysis - Understand the specific content, reputation, and technical factors driving each dimension of your score
- Competitive intelligence - See exactly how competitors are outperforming you in AI recommendations
- Action roadmap - Get a prioritized list of high-impact changes to improve your AI visibility, sentiment, and authority
Ready to improve your score? Contact us to learn how we can help you win in AI-driven discovery.
Get StartedTechnical Methodology Reference
The following sections provide complete technical documentation of the VSCA methodology for analysts, researchers, and those requiring detailed understanding of our calculations.
Calculation Overview
The AI Overall Score (VSCA Score) is built in three stages:
We ask AI platforms questions and analyze their responses:
- How often is the brand mentioned?
- What position does it appear in?
- Is the sentiment positive or negative?
- What sources does the AI cite?
This gives us 9 raw metrics (numbers between 0 and 1)
The 9 metrics are grouped into 4 "dimensions":
Each dimension gets a score from 0 to 1
The 4 dimension scores are combined using a weighted Power Mean, then scaled to a 0-100 score.
Dimension Calculations
Visibility Dimension
| Metric | Symbol | Definition |
|---|---|---|
| Exposure Rate | ER | brand_mentions / total_generic_responses |
| First Mention Rate | FMR | first_mentions / brand_mentions |
| Position Score | PS | 1 / avg_position (capped at 1) |
V' = (phi(ER) x phi(FMR) x phi(PS))^(1/3)
Sentiment Dimension
| Metric | Symbol | Definition |
|---|---|---|
| Net Sentiment | NS | positive_phrases / (positive_phrases + negative_phrases) |
S' = psi(NS)
Competitiveness Dimension
| Metric | Symbol | Definition |
|---|---|---|
| Share of Voice | SOV | brand_mentions / all_brand_mentions |
| Sentiment Gap | SG | brand_sentiment - avg(competitor_sentiment) |
| Position Dominance | PD | brand_first_mentions / all_first_mentions |
C' = (phi(SOV) x sigma(SG) x PD)^(1/3)
Authority Dimension
| Metric | Symbol | Definition |
|---|---|---|
| Citation Rate | CR | responses_with_citations / total_responses |
| Brand Citation Ratio | BCR | brand_citations / total_citations |
A' = (phi(CR) x phi(BCR))^(1/2)
Aggregation
We use a Weighted Power Mean (Holder Mean) to combine dimensions:
M_p = (w_V*V'^p + w_S*S'^p + w_C*C'^p + w_A*A'^p)^(1/p)
Dimension weights are determined via Analytic Hierarchy Process (Saaty, 1980).
Symbol Reference
| Symbol | Name | Description |
|---|---|---|
| phi(x) | Log Transform | Logarithmic transform for diminishing returns |
| psi(x) | Inverse Log | Rewards excellence at high values |
| sigma(x) | Sigmoid | Normalizes values to 0-1 range |
| GM | Geometric Mean | Penalizes imbalanced performance |
References
- Seer Interactive (2025). AIO Impact on Google CTR
- McKinsey (2025). New Front Door to the Internet
- Exposure Ninja (2025). AI Search Statistics
- Bing (2025). How AI Search Is Changing Conversions
- First Page Sage (2025). Google CTRs by Ranking Position
- Binet, L. & Field, P. The Long and the Short of It. IPA.
- Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill.
- UN Human Development Index Methodology