Measuring Brand Impact in an LLM-First world: A Practical Framework for Visibility Without Clicks​

As generative AI tools like ChatGPT, Gemini, Claude, and Perplexity redefine how users discover, evaluate, and trust information, one thing is clear: the old KPIs aren’t built for this new terrain.

For global brand marketers, LLMs are no longer edge cases; they’re front-line intermediaries. These AI systems are increasingly the first - and sometimes only - point of content discovery, product consideration and competitor comparison.

But here’s the real shift: there are no clicks. No sessions. No visible referrals. Yet your brand may still be influencing perception, shaping decisions, and establishing authority without ever appearing in your standard analytics.

This article introduces a practical measurement framework for brand performance in a ‘zero-click’ future, providing strategic marketers with the tools to evaluate visibility across LLM ecosystems with the same discipline as they do traditional SEO, media, and CRM.

If we accept that LLMs mediate attention, then we must measure them as such.

The Visibility Gap: Why Traditional Metrics Miss the New Reality

CMOs and marketing leaders face a growing performance paradox:

Your brand may be recommended, compared or cited by AI platforms - yet your dashboards show no corresponding uplift in traffic or attributable conversions.

Why? Because AI-driven discovery breaks the behaviours most marketing systems are built to track:

• Users read and act on summaries without clicking through to your site
• Source citations, when shown, are often stripped of referral tracking
• Engagement and influence happen “off-platform” - outside your owned site, ads, or CRM systems

The result: Blind spots. Untracked influence. And misplaced attribution.

This creates two urgent challenges:

1. Invisible influence:
   Brand consideration or preference may be growing, unseen – reinforced by LLMs but absent from performance dashboards.
2. Misplaced attribution:
   Traffic from branded search, performance media, or even direct visits may stem from AI-assisted exposure - yet no reporting system connects the dots.

Why this matters: Brands must distinguish between gaining traffic and gaining narrative control. In LLM environments, you win by being included in the answer, not clicked in the link.

Measurement models must evolve to track influence, not just interaction.

A New Measurement Stack for LLM Visibility

To measure LLM performance with the same strategic discipline as SEO and media, marketers need a new kind of framework — one built for influence over interaction.

We propose a four-layer model that answers two fundamental questions:

• Are we showing up in the moments that matter?
• And when we do, are we presented accurately and competitively?

Because LLM platforms don’t provide analytics APIs (yet), visibility must be tracked from the outside-in - using structured prompt testing, citation audits, and diagnostic analysis that mirrors how AI systems parse and summarize information.

This is not precision analytics - it’s directional surveillance. But that surveillance can be consistently measured, segmented, and actioned to improve brand performance inside AI-led discovery journeys.

1. Inclusion Metrics: “Are we showing up?”

Inclusion metrics measure your brand’s real-time visibility in response to prompts that users might realistically ask LLMs - questions about your brand, competitors, product recommendations, or category definitions.

These aren’t just reference checks. They are the clearest indicators of whether LLMs publicly acknowledge, compare, or recommend your brand in active conversations.

They are also the most urgent layer of measurement, because they reflect your current presence in zero-click discovery journeys, which are increasingly shaping buyer intent without ever reaching your site.

Important distinction:

• Inclusion Metrics track whether you actually show up in responses – regardless of available content.
• Coverage Metrics (layer 3) measure whether your brand has content that answers the questions users are asking.

Inclusion is about being mentioned, coverage is about earning the right to be mentioned.

Metric: Inclusion Rate

Metric: Citation Rate

2. Representation Metrics: “Are we described accurately and competitively?”

Surfacing in AI-generated responses is only half the battle. The other half, arguably more critical, is how you're portrayed when mentioned. Representation metrics track qualitative aspects of brand visibility: are you accurately described, framed in the right context, and positioned competitively?

Metric: Answer Accuracy Score

Metric: Narrative Consistency Index

3. Coverage Metrics: “Are we answering the right questions?”

Being included in an AI response starts with eligibility. Coverage metrics measure whether your brand has content that addresses the kinds of questions people are asking LLMs - product comparisons, category overviews, or recommendation queries.

This layer doesn’t measure current visibility. It measures potential visibility. It answers: if the AI models wanted to recommend you, would they have good material to work from?

Coverage metrics are especially useful for identifying content gaps that prevent your brand from appearing in AI results, regardless of authority or structure.

Metric: Prompt Coverage Map

Metric: Share of Synthesized Voice (a.k.a Share of AI Voice)

4. Source Metrics: “Are we driving citation-worthy content?”

Inclusion and accuracy depend heavily on what content the LLM can find, trust, and understand. Source metrics assess the underlying ecosystem of signals that shape whether – and how – your brand is referenced.

These metrics reflect three types of influence:

• Trustworthiness: Are the sites citing or hosting your brand considered credible by AI training models?
• Machine-readability: Is your content structured in ways LLMs understand (schema, markup, metadata)?
• Training presence: Is your brand represented inside the datasets that models were trained or aligned on?

Metric: Citation Authority

Metric: Schema Signal Strength

Metric: Training Set Presence (Proxy Metric)

Secondary Signals: Detecting the Business Impact of LLM Visibility

LLMs don’t drive trackable traffic to your site, but they do shape user behaviour. Their influence often appears indirectly, through patterns in other performance channels.

These supporting metrics help marketers connect the dots between LLM visibility and downstream signals of intent, awareness, or engagement.
These are not deterministic indicators, but valuable signals that your inclusion in AI answers is influencing real users.

How to use these metrics:

• Monitor shifts in these patterns after changes in Inclusion or Citation KPIs
• Pair business analytics with LLM prompt testing to build visibility-impact correlations
• Use with caution – any one signal is directional, not causal

What they’re not:

These signals won’t prove causality and aren’t replacements for direct LLM measurement, but when seen in parallel with high LLM Inclusion or Citation Rates, they help complete the attribution picture. This allows smart teams to layer LLM visibility into the broader marketing performance picture without attribution paralysis.

Low-Lift Entry Point: Baseline Your Brand Narrative in LLMs

If you’re not ready to build or resource a full LLM visibility framework yet, start with a simple diagnostic: establish your brand’s baseline presence and positioning across major language models.

Ask a few popular LLMs:

“How does [My Brand] show up in AI responses in comparison to competitor brands?”

This gives you the clearest initial view of how your brand is framed across major AI platforms, as the response will be based on the same knowledge set used to answer more specific audience questions.

The response to this question will give you:

• A live snapshot of your baseline narrative as interpreted by LLM models
• An indication of variance across different AI platforms
• An immediate signal of whether your brand is recognised, positioned well or even overlooked entirely.

This qualitative scan primes content, PR, and brand teams to close perceptual or authority gaps, before you operationalize a full KPI-led approach.

Example: An AI prompt revealing ChatGPT’s perception of Gravity Global, indicating current messaging and PR strengths as well as growth opportunities.

Embedding Measurement into the Marketing Discipline

Once you’ve defined your LLM brand visibility framework, the next step is operationalising it across your core marketing functions. That means treating LLM visibility with the same level of discipline as SEO, media performance, and digital reputation management.

Forward-thinking teams are embedding these capabilities in five key ways:

1. Establish a Prompt-based LLM Visibility Baseline
   • Allocate 2–3 weeks to run cross-platform prompt tests, covering ChatGPT, Google AI Overviews/AI Mode, Claude, Perplexity, Gemini and others
   • Track brand presence, narrative accuracy, and tone across 20–50 high-relevance prompts
   • Aim for at least 10 prompts per core product, audience, or category you want to track
   • Include competitor names for benchmarking visibility and positioning
2. Add LLM KPIs to SEO and Content Dashboards
   • Integrate LLM-specific visibility metrics like Inclusion Rate, Citation Rate, and Answer Accuracy Score into your reporting dashboards.
   • Visualise trendlines across quarters and pair with topical content performance
   • Highlight shifts in Share of Synthesized Voice across products and journeys to correlate to demand signals
3. Incorporate LLM Visibility into Stakeholder Reviews
   • CMOs and senior product or brand leads should receive quarterly visibility updates that include:
   • Prompt inclusion trends
   • Representation risks (misinformation, absent data, outdated positioning)
   • Competitive visibility performance vs key rivals
4. Align with Attribution & Analytics Teams
   • Work with analytics and insights teams to correlate brand visibility in LLMs with emerging interaction patterns:
   • Mid-funnel behaviour changes (e.g. quicker path to action)
   • Increased branded search or direct type-in traffic
   • Creative performance spikes on paid or organic media
   • Customer feedback or buyer comments that reference "seeing it recommended by AI"
   • This enables directional attribution – connecting AI visibility with demand response.
5. Use Campaigns to Feed the Training Loop
   • Rather than viewing brand campaigns purely as outbound visibility drivers, see them also as training data moments for AI ecosystems.
   • Campaigns tell the world who you are – and they teach language models to repeat it.
   • • View paid, earned and thought-leadership campaigns as opportunities for training data inputs
     • Measure post-campaign inclusion lift for product names, executive leadership or brand terms
     • Ensure structured content is present to assist real-time LLM data input and user browsing (e.g. Perplexity, which can crawl the web live to support a user response)
     • Ensure campaigns generate content-rich evidence that LLMs can read, cite, and propagate

Conclusion: Visibility Is Being Redefined – Measurement Must Catch Up

You can’t optimize what you don’t measure – and right now, many brands are flying blind in the world of LLM visibility.

Generative AI is reshaping how users discover, evaluate, and decide — not by clicking, but by reading AI-generated answers. These are brand-defining moments. And they increasingly happen off-platform.

The challenge for modern marketing leaders is measurement maturity: building the scaffolding to track brand presence, evaluate influence, and validate narrative control in conversation-first discovery ecosystems.

Brands that accept the limits of old metrics - while leaning into new methods - will not only future-proof their visibility but gain early-mover advantage in how this new frontier is benchmarked.

AI may not send traffic. But it sends recognition, reputation, and trust.
Start measuring it.

Rob Welsby

Global Head of SEO

Leading Gravity Global’s SEO practice area, Rob first started working in organic search nearly 20 years ago, and in that time has seen seismic shifts in how brands need to evolve and adapt to maintain strong organic search visibility. From site speed and UX to ‘mobile-first’, machine-learning algorithms and the future of AI, Rob and team enjoy nothing more than geeking out over data and seeing real-world commercial-impact for our clients based on our recommendations.​