Independent AI Visibility Score audit of Amanda Natividad using the IdeaLab AI Visibility OS v1.1 framework. Composite: 60/100 · Grade C — Legacy Mode · #12 in database. Amanda Natividad is Chief Evangelist at SparkToro and Founder of Zero Click Marketing, the marketing framework she co-created with Rand Fishkin in 2022. She is also co-author of the forthcoming Zero Click Marketing book (due fall 2026, publisher Damn Gravity). Her X/Twitter account grew from 700 to 100,000 followers organically in under two years — a living demonstration of ZCM in action. Framework by J.L. Marcoux, IdeaLab.ai.
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Key Finding: Grade C Today — Grade B Within 12 Months
60/100 is the honest structural score for a rising entity whose defining content asset is not yet published and whose entity infrastructure is essentially absent. The score does not reflect her trajectory. The ZCM framework is gaining independent citation momentum. Her X growth (700→100K organic in under 2 years) is the most compelling current demonstration of the ZCM framework in action. Wikidata and Person JSON-LD can be deployed this week — before the book even launches.
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Critical Gap: D3 Entity Recognition — 38/100 · Grade D
No Wikipedia. No Wikidata. No Person JSON-LD. Amanda Natividad has coined a named marketing framework, guest lectured at Columbia Business School, Cornell, Stanford, and the University of Washington, and grown an X account to 100K organically — yet AI knowledge graphs cannot confidently resolve her as a distinct entity. Creating a Wikidata entry takes 45 minutes and is the single highest-leverage action available today.
📈 Trajectory Note: This is the only Grade C entity in the current database with a clear, confirmed catalyst for rapid improvement. The ZCM book (fall 2026) will move D2 Content Depth, D4 Citation Network, D5 Topical Authority, and D7 Trust Signals materially. Wikidata + Person JSON-LD + one general media profile can move D3 from 38 to 55+ this week. The 60→70+ path has a specific, implementable roadmap.
* Amanda Natividad (60) is the lowest-scoring entity in the current database and the one with the clearest upward trajectory. The gap vs. peers is structural (no Wikidata, no Wikipedia, book unpublished) rather than substantive. The ZCM book (fall 2026) + Wikidata + Person JSON-LD are expected to move the score to 70–75/100 Grade B within 12 months. Note: her co-author Rand Fishkin scored 87/100 (#3 in database) — the gap illustrates exactly how much impact entity infrastructure and a published book have on AI visibility scores.
Every IdeaLab audit uses the AI Visibility OS v1.1 — a seven-dimension framework that measures how discoverable, citable, and trustworthy an entity is to answer engines like ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Each dimension is scored 0–100, multiplied by its weight, and summed into a composite 0–100 score. The composite maps to a letter grade that describes AI-answer behavior for the entity's core queries.
D1 · BRAND CLARITY
WEIGHT ×1.0 · 10%
Consistent, unambiguous naming and positioning across owned surfaces. AI engines cite entities that resolve to a single canonical identity.
D2 · CONTENT DEPTH
WEIGHT ×2.0 · 20%
Long-form, first-hand, evidence-rich content on owned domains. Depth is the largest single driver of LLM citation frequency.
D3 · ENTITY RECOGNITION
WEIGHT ×1.5 · 15%
Presence in knowledge graphs — Wikipedia article, Wikidata QID, Google Knowledge Panel, sameAs coverage. Anchors the entity for AI retrieval.
The two heaviest weights — D2 Content Depth and D4/D6 Structured Knowledge and Citations — reflect the empirical drivers of AI answer inclusion. D3 Entity Recognition acts as a ceiling: without Wikipedia and Wikidata anchoring, most audits cap at Grade B regardless of the other six dimensions.
GRADE SCALE
A · 85–100
Answer Authority — AI engines cite the entity by default
B · 70–84
Citation Ready / Strong — cited when prompted specifically
C · 60–69
Moderate / Legacy Mode — inconsistent AI visibility
D · 50–59
At Risk — rarely surfaced without exact-name prompts
F · <50
Invisible — no reliable citation surface for AI answers
The per-dimension scores and weighted contributions for this specific audit are shown in the scoring table above. The Top Gap and Top Action highlighted on this page correspond to the lowest-scoring high-weight dimensions — the fixes with the largest projected uplift to the composite score.
Score Methodology FAQ
Frequently Asked Questions About The AI Visibility Score
What is the AI Visibility Score?
The AI Visibility Score is a 0–100 composite metric produced by IdeaLab's AI Visibility OS v1.1 framework. It measures how discoverable, citable, and trustworthy an entity — a brand, creator, product, or expert — is to generative answer engines such as ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. Higher scores correlate with more frequent and more confident AI citations for the entity's core queries.
How is the score calculated?
Each of the seven dimensions (D1–D7) is scored 0–100, multiplied by its weight, and summed. The formula is: Score = (D1 × 0.10) + (D2 × 0.20) + (D3 × 0.15) + (D4 × 0.20) + (D5 × 0.15) + (D6 × 0.20) + (D7 × 0.10). Content Depth (D2), Structured Knowledge (D4), and Social Proof & Citations (D6) carry the largest weights because they are the empirical drivers of AI citation behavior.
What do the letter grades A through F mean?
Grade A (85–100) is Answer Authority — AI engines cite the entity by default. Grade B (70–84) is Citation Ready — cited when prompted specifically. Grade C (60–69) is Moderate / Legacy Mode with inconsistent AI visibility. Grade D (50–59) is At Risk — rarely surfaced without exact-name prompts. Grade F (below 50) is Invisible — no reliable citation surface for AI answers.
Why does Entity Recognition (D3) act as a ceiling?
Without a Wikipedia article, a Wikidata QID, and consistent sameAs coverage across owned surfaces, AI engines cannot reliably resolve the entity to a single canonical identity. In practice this caps most audits at Grade B regardless of how strong the other six dimensions are, because the retrieval layer that grounds AI answers depends on knowledge-graph anchoring.
What is the difference between the Top Gap and the Top Action?
The Top Gap is the single lowest-scoring high-weight dimension for the audited entity — the diagnosis. The Top Action is the concrete, prioritized fix that addresses that gap and delivers the largest projected uplift to the composite score. Together they turn a static score into an execution roadmap.
How often is an AI Visibility Score refreshed?
IdeaLab audits are refreshed when the entity ships meaningful changes — a Wikipedia article goes live, new JSON-LD schema is deployed, an llms.txt is published, or major press citations land — and on a rolling quarterly cadence to capture drift in AI-crawler policy, knowledge-graph coverage, and citation surface.
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