AI KPIs: Turning mentions into strategy in the age of LLMs by Brightspot
[IMG alt="Illustration depicting analytics and content capabilities needed to track performance in AI search
"]https://searchengineland.com/wp-content/seloads/2025/10/Brightspot-20251013-header.jpg[/IMG]
For years, marketers measured digital success through impressions, backlinks and clicks. If you ranked high in search results and won the click, you had visibility and control of the funnel. But that landscape is already shifting.
Large Language Models (LLMs) like ChatGPT, Claude, Gemini and Perplexity are rapidly becoming the first place decision-makers go for answers. These systems don’t return a page of links; they generate a synthesized response. Whether your brand is included, or ignored, in that answer increasingly determines your relevance in the buying journey.
This changes the marketer’s playbook. Visibility is no longer only about ranking on Google. It’s about whether you’re present in AI-generated responses, how you’re framed, and what sources are credited. In this new paradigm, being mentioned is the new click.
The challenge for marketers isn’t simply tracking this new set of KPIs. It’s knowing how to interpret the signals and translate them into action. Let’s look at four core AI KPIs: mentions, sentiment, competitive share of voice and sources. We will explore how each can directly shape strategy.
The first KPI is the simplest: how often are you mentioned inside LLM responses? If you’re absent from common category or evaluation queries, things like “top SaaS tools for analytics” or “best project management platforms,” then you’re essentially erased from the conversation before it begins.
But mentions are more than a vanity metric. They are a diagnostic tool. Patterns in where you appear, and where you don’t, can tell you which parts of your content strategy are resonating and which areas need reinforcement.
If mentions are low in early-stage educational queries, invest in thought-leadership content that positions you as a voice in defining the category. If mentions are absent in solution-oriented queries, build assets that explain your differentiators more clearly. Mentions are the first signal of where your brand is visible, and where it’s invisible.
For marketers, mentions are the equivalent of oxygen. Without them, everything else is moot. With them, you can begin to shape how buyers see you.
The second KPI is sentiment. Being mentioned is good, but how you’re described is what really sticks. LLMs add qualifiers to their responses based on available information: “fast,” “trusted,” “expensive,” “hard to use.” These adjectives reflect the narrative that exists in the data the model has absorbed.
Negative sentiment highlights messaging gaps to address. If you’re framed as costly, consider publishing ROI calculators, pricing comparisons or case studies that show value delivered. If you’re seen as complex, invest in content that simplifies onboarding stories or customer success examples. Positive sentiment, on the other hand, shows you what narratives to amplify. If you’re consistently described as “trusted,” weave that trust theme into campaigns, analyst briefings and customer storytelling.
Sentiment analysis transforms LLM outputs into a real-time market perception barometer. For marketers, that’s invaluable. It gives you a constant read on how your positioning is landing without waiting for lagging indicators like surveys or analyst reports.
Mentions and sentiment don’t mean much without context. The real question is: how do you compare to your competitors?
Competitive share of voice is about measuring your brand’s presence in LLM responses alongside peers in your space. If you’re mentioned in 30% of relevant queries, but your top competitor appears in 70%, you’re playing catch-up. If you both appear equally often but their sentiment is glowing while yours is flat, they’re winning the perception battle.
These insights turn into a battle map. If competitors are dominating certain categories of questions, that points to content and messaging investments you need to make. If their sentiment is consistently stronger, it suggests you need to double down on proof points or sharpen your differentiators. On the flip side, if you’re leading in areas they’re weak, that’s a narrative advantage you can emphasize in campaigns.
For marketers, competitive share is a strategy guide. It shows where you need to defend, where you can attack, and where you’re already winning.
The final KPI is sources. Mentions tell you if you’re in the story. Sentiment tells you how you’re framed. Competitive share tells you how you stack up. But sources reveal who the AI trusts to tell the story.
When an LLM cites a competitor’s whitepaper or an industry analyst’s report rather than your own content, it’s a clear signal: you’re not seen as the authority. Conversely, if your blog post or research study is the cited source, you’ve secured a position as the trusted voice.
This is where content engineering comes into play. If you want your sources to be cited, they must be comprehensive, structured and credible. Think FAQ-style pages, data-driven reports, or clearly attributed expert commentary. By publishing content that AI can recognize as authoritative, you shift from simply being mentioned to being the foundation of the answer.
For marketers, this is the ultimate form of influence. When your resources are the citations behind the AI’s output, you control the conversation.
The temptation with any new metric is to build elaborate frameworks and dashboards. But the value of AI KPIs lies less in the infrastructure and more in the insights.
Mentions highlight visibility gaps. Sentiment exposes how you’re really perceived. Competitive share shows you where rivals are winning ground. Sources reveal who has authority.
Together, they form a compass. They help highlight performance and point you toward action:
Marketers who use AI KPIs this way will be able to get ahead in the AI era, and they’ll actively help shape it.
It may feel early. The tooling isn’t standardized, and there’s no polished dashboard that marketers can log into and get all this in one view. But that’s precisely why early movers have the advantage.
Think back to the early 2000s, when SEO was still experimental. The brands that learned to optimize before the playbook was written ended up owning search visibility for years. We’re at the same moment now with AI KPIs. Waiting for the tools to catch up means letting competitors set the baseline while you play defense.
The actions don’t have to be complex. Even a lightweight process like running a set of prompts, logging responses and looking at mentions, sentiment, share and sources over time yields intelligence that can shape marketing and content strategies right now.
The rise of LLMs doesn’t eliminate the value of clicks, impressions or backlinks, but it does redefine what visibility means. Increasingly, your brand’s story is being told inside AI-generated responses long before a buyer reaches your website.
That’s why these KPIs matter. Being mentioned is the new click. But the real advantage comes not from counting those mentions, but from using them to make smarter decisions, closing visibility gaps, reframing perception, benchmarking competitors, and owning citations.
For marketers, this is about translating AI signals into strategy. The brands that learn to do this now will have a better chance to survive the shift to AI-driven search.
Continue reading...
[IMG alt="Illustration depicting analytics and content capabilities needed to track performance in AI search
"]https://searchengineland.com/wp-content/seloads/2025/10/Brightspot-20251013-header.jpg[/IMG]
For years, marketers measured digital success through impressions, backlinks and clicks. If you ranked high in search results and won the click, you had visibility and control of the funnel. But that landscape is already shifting.
Large Language Models (LLMs) like ChatGPT, Claude, Gemini and Perplexity are rapidly becoming the first place decision-makers go for answers. These systems don’t return a page of links; they generate a synthesized response. Whether your brand is included, or ignored, in that answer increasingly determines your relevance in the buying journey.
This changes the marketer’s playbook. Visibility is no longer only about ranking on Google. It’s about whether you’re present in AI-generated responses, how you’re framed, and what sources are credited. In this new paradigm, being mentioned is the new click.
The challenge for marketers isn’t simply tracking this new set of KPIs. It’s knowing how to interpret the signals and translate them into action. Let’s look at four core AI KPIs: mentions, sentiment, competitive share of voice and sources. We will explore how each can directly shape strategy.
Mentions: The visibility test
The first KPI is the simplest: how often are you mentioned inside LLM responses? If you’re absent from common category or evaluation queries, things like “top SaaS tools for analytics” or “best project management platforms,” then you’re essentially erased from the conversation before it begins.
But mentions are more than a vanity metric. They are a diagnostic tool. Patterns in where you appear, and where you don’t, can tell you which parts of your content strategy are resonating and which areas need reinforcement.
- Making mention usable: Break mentions down by type of query. Are you showing up in broad “what is” or “how to” questions, or only in head-to-head competitor comparisons? Are you included in trend discussions but missing from buying-decision queries? That breakdown highlights where to expand your authority.
If mentions are low in early-stage educational queries, invest in thought-leadership content that positions you as a voice in defining the category. If mentions are absent in solution-oriented queries, build assets that explain your differentiators more clearly. Mentions are the first signal of where your brand is visible, and where it’s invisible.
For marketers, mentions are the equivalent of oxygen. Without them, everything else is moot. With them, you can begin to shape how buyers see you.
Sentiment: The market’s echo
The second KPI is sentiment. Being mentioned is good, but how you’re described is what really sticks. LLMs add qualifiers to their responses based on available information: “fast,” “trusted,” “expensive,” “hard to use.” These adjectives reflect the narrative that exists in the data the model has absorbed.
- Making sentiment usable: Capture the language used around your brand. Track whether descriptors skew positive, neutral or negative. Note recurring themes — are you consistently framed as “enterprise-grade” but also “complex”? Are you praised for “innovation” but dinged for “cost?”
Negative sentiment highlights messaging gaps to address. If you’re framed as costly, consider publishing ROI calculators, pricing comparisons or case studies that show value delivered. If you’re seen as complex, invest in content that simplifies onboarding stories or customer success examples. Positive sentiment, on the other hand, shows you what narratives to amplify. If you’re consistently described as “trusted,” weave that trust theme into campaigns, analyst briefings and customer storytelling.
Sentiment analysis transforms LLM outputs into a real-time market perception barometer. For marketers, that’s invaluable. It gives you a constant read on how your positioning is landing without waiting for lagging indicators like surveys or analyst reports.
Competitive Share: The benchmark that matters
Mentions and sentiment don’t mean much without context. The real question is: how do you compare to your competitors?
Competitive share of voice is about measuring your brand’s presence in LLM responses alongside peers in your space. If you’re mentioned in 30% of relevant queries, but your top competitor appears in 70%, you’re playing catch-up. If you both appear equally often but their sentiment is glowing while yours is flat, they’re winning the perception battle.
- Making competitive share usable: Track not only how often you appear relative to competitors, but also the nature of those appearances. Which types of queries favor them over you? Which attributes are assigned to them versus you?
These insights turn into a battle map. If competitors are dominating certain categories of questions, that points to content and messaging investments you need to make. If their sentiment is consistently stronger, it suggests you need to double down on proof points or sharpen your differentiators. On the flip side, if you’re leading in areas they’re weak, that’s a narrative advantage you can emphasize in campaigns.
For marketers, competitive share is a strategy guide. It shows where you need to defend, where you can attack, and where you’re already winning.
Sources: Who the AI trusts
The final KPI is sources. Mentions tell you if you’re in the story. Sentiment tells you how you’re framed. Competitive share tells you how you stack up. But sources reveal who the AI trusts to tell the story.
When an LLM cites a competitor’s whitepaper or an industry analyst’s report rather than your own content, it’s a clear signal: you’re not seen as the authority. Conversely, if your blog post or research study is the cited source, you’ve secured a position as the trusted voice.
- Making source insights usable: Audit which domains and documents are being cited when your category is discussed. Are trade publications showing up more than your own site? Are competitors’ research reports being favored?
This is where content engineering comes into play. If you want your sources to be cited, they must be comprehensive, structured and credible. Think FAQ-style pages, data-driven reports, or clearly attributed expert commentary. By publishing content that AI can recognize as authoritative, you shift from simply being mentioned to being the foundation of the answer.
For marketers, this is the ultimate form of influence. When your resources are the citations behind the AI’s output, you control the conversation.
From signals to strategy
The temptation with any new metric is to build elaborate frameworks and dashboards. But the value of AI KPIs lies less in the infrastructure and more in the insights.
Mentions highlight visibility gaps. Sentiment exposes how you’re really perceived. Competitive share shows you where rivals are winning ground. Sources reveal who has authority.
Together, they form a compass. They help highlight performance and point you toward action:
- Fill gaps with new content.
- Reframe narratives with stronger proof.
- Defend share with sharper positioning.
- Earn trust by publishing resources built to be cited.
Marketers who use AI KPIs this way will be able to get ahead in the AI era, and they’ll actively help shape it.
Why acting now matters
It may feel early. The tooling isn’t standardized, and there’s no polished dashboard that marketers can log into and get all this in one view. But that’s precisely why early movers have the advantage.
Think back to the early 2000s, when SEO was still experimental. The brands that learned to optimize before the playbook was written ended up owning search visibility for years. We’re at the same moment now with AI KPIs. Waiting for the tools to catch up means letting competitors set the baseline while you play defense.
The actions don’t have to be complex. Even a lightweight process like running a set of prompts, logging responses and looking at mentions, sentiment, share and sources over time yields intelligence that can shape marketing and content strategies right now.
Conclusion: Mentions as strategy
The rise of LLMs doesn’t eliminate the value of clicks, impressions or backlinks, but it does redefine what visibility means. Increasingly, your brand’s story is being told inside AI-generated responses long before a buyer reaches your website.
That’s why these KPIs matter. Being mentioned is the new click. But the real advantage comes not from counting those mentions, but from using them to make smarter decisions, closing visibility gaps, reframing perception, benchmarking competitors, and owning citations.
For marketers, this is about translating AI signals into strategy. The brands that learn to do this now will have a better chance to survive the shift to AI-driven search.
Continue reading...