Primary vs Secondary Signals: Understanding What Really Drives AI Search Visibility
Why Signals Now Matter More Than Keywords
In the world of AI search, visibility is no longer driven solely by keywords or backlinks. Today’s answer engines — like Google’s Search Generative Experience (SGE) and Perplexity.ai — rely on a web of trust signals to determine which brands are credible, verifiable, and contextually relevant.
These signals form the new language of visibility. Instead of simply asking, “Who mentions you?” AI systems now ask, “Can this brand be trusted to provide the best answer?” Understanding primary vs secondary signals helps you design a search strategy that speaks fluently to both algorithms and humans.
What Are Primary Signals?
Primary signals are the most direct indicators of brand trust, expertise, and content authority. They are core proof points that AI systems use to determine factual reliability and entity legitimacy.
Examples of primary signals
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Official brand data: Structured information such as organisation schema markup, verified addresses, and author credentials.
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Citations and backlinks: Mentions from credible third-party domains (especially from educational, governmental, or industry-specific sources).
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Entity recognition: Consistent naming, logo usage, and descriptions across the web that align with knowledge graph entries.
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First-party content authority: High-quality, in-depth articles published on your official domain that demonstrate expertise (often validated by author bios or academic citations).
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Customer or media validation: Verified reviews, press coverage, or research partnerships that confirm trustworthiness.
These are the signals that make you discoverable in generative search. They show AI that your brand exists, can be verified, and offers trustworthy knowledge.
Example in practice
When Harvard Health Publishing publishes medical guidance, its primary signals include:
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Structured data validating authorship and credentials.
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Consistent brand schema.
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Backlinks from reputable medical institutions.
These data points tell AI engines, “This entity is authoritative on health topics.”
What Are Secondary Signals?
Secondary signals are indirect indicators of credibility, engagement, and relevance. They help AI systems understand how audiences interact with your content — and whether it’s perceived as helpful, credible, and aligned with intent.
Examples of secondary signals
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Engagement metrics: Time on page, click-through rate, scroll depth, and social sharing.
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Topical consistency: Publishing content within a defined subject domain.
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Semantic relationships: Internal linking, related topic clusters, and co-occurrence with authoritative entities.
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User sentiment: Reviews, ratings, or discussions across forums and social media.
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Brand mentions: Non-linked references that reinforce topical authority.
While these don’t prove expertise, they help AI infer content usefulness and audience satisfaction — both critical for ranking in answer-driven search systems.
Example in practice
When HubSpot publishes marketing guides, its secondary signals include:
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Strong user engagement on LinkedIn.
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Consistent topic clusters (CRM, inbound marketing, automation).
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High dwell time on educational pages.
These tell AI, “This brand provides content that users find valuable.”
Primary vs Secondary: The Layered Trust Model
You can think of primary signals as credentials — your brand’s formal identity and expertise — and secondary signals as reputation — how the world perceives and engages with you.
| Category | Primary Signals | Secondary Signals |
|---|---|---|
| Purpose | Establish factual trust and brand legitimacy | Indicate engagement, usefulness, and contextual relevance |
| Examples | Schema markup, backlinks, verified profiles | Engagement metrics, reviews, topical clustering |
| Impact on AI Search | Determines eligibility for inclusion and citation | Influences ranking position and narrative tone |
| Analogy | Your CV and references | What people say about you at work |
In AI-driven ranking models, both layers work together. Primary signals get you indexed; secondary signals keep you visible.
How AI Search Systems Weigh These Signals
AI search models such as Google’s MUM, OpenAI’s GPT-based retrieval, and Anthropic’s Claude all depend on multi-signal evaluation frameworks. These systems balance structured data (e.g. verified brand facts) with behavioural data (e.g. user satisfaction) to determine which responses to surface.
A 2023 Stanford Human-Centred AI report noted that entity-based trust signals are essential for grounding generative outputs in factual sources. Similarly, research from Moz and Semrush highlights how engagement metrics increasingly correlate with visibility in generative SERPs.
In simple terms:
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Primary signals = AI trust inputs
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Secondary signals = AI performance feedback loops
Without both, your brand’s visibility remains incomplete.
Building a Balanced Signal Strategy
A modern brand authority strategy requires deliberate attention to both layers.
1. Strengthen your primary signals
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Implement organisation and author schema.
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Secure high-quality backlinks and citations from reputable domains.
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Keep NAP (Name, Address, Phone) and branding consistent across the web.
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Publish original research or insights under verified author profiles.
2. Amplify your secondary signals
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Create topic clusters around your key expertise areas.
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Encourage reviews and user feedback on trusted platforms.
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Monitor engagement metrics to refine content UX.
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Build social proof through audience interaction and partnerships.
3. Use structured monitoring
Set up dashboards to track both signal types.
For instance:
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Primary signal KPIs: backlinks, schema coverage, citation velocity.
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Secondary signal KPIs: dwell time, CTR, review sentiment, social engagement.
Tools like Semrush’s AI SEO toolkit, Google’s Search Console, and Brand Authority AI dashboards can visualise how both sets of signals evolve over time.
FAQ: How Do I Know Which Signals Matter Most?
Q: Which signals should I prioritise first?
A: Start with primary signals — structured data, citations, and consistency. Without these, AI systems can’t verify your identity. Once these are stable, layer in secondary signals to strengthen visibility.
Q: Can strong secondary signals compensate for weak primary ones?
A: Rarely. Positive engagement without verifiable brand data creates visibility without credibility — a short-term gain that AI systems will filter out over time.
Q: How often should I update my signals?
A: Review structured data quarterly and monitor engagement metrics monthly. Generative search evolves quickly; freshness and accuracy matter.
Conclusion: From SEO to Signal Strategy
The shift to AI search means visibility is no longer about gaming algorithms — it’s about building authentic, verifiable trust ecosystems.
Primary signals establish who you are.
Secondary signals prove that people value what you offer.
The brands that thrive in this new landscape are those that engineer both trust and engagement — creating a signal portfolio strong enough to stand out in the generative web.
© David R. Durham, All rights reserved, 2025.
