Pet Insurance AI Search Case Study
How a Pet Insurance Brand Increased Recommendation-Stage Visibility Across Search and AI
How a Pet Insurance Brand Increased Recommendation-Stage Visibility Across Search and AI
Methodology Note:
Directional estimate based on tracked
keyword visibility and modeled paid-equivalent value.
Not exact attribution.
In just 3 days, with only 25 engagements, this campaign generated an estimated $362,569.07 in total estimated monthly branded value. That included $53,080 in organic keyword value and $309,488.28 in LLM-cited pages value.
In just 3 days, with only 25 engagements, this campaign generated an estimated $362,569.07 in total estimated monthly branded value. That included $53,080 in organic keyword value and $309,488.28 in LLM-cited pages value.
Methodology note:
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
For pet insurance providers, speed matters because purchase decisions are often shaped before a buyer ever lands on a provider’s website.
Pet owners researching coverage are comparing plans, reading reviews, checking community discussions, and increasingly asking AI tools to summarize their options. In that environment, brands benefit most when they can influence trusted decision-shaping sources quickly and with minimal wasted activity.
This campaign was built around that principle. Rather than pursuing broad awareness, CiteWorks Studio focused a small number of deliberate engagements on the public sources most likely to influence both buyer evaluation and AI-generated recommendations.
The result was a faster, more efficient expansion of the brand’s visibility across the places pet owners actually use to decide who to trust.
Key Outcomes
Delivered in 3 days with only 25 engagements:
#6 as the average
ranking position
across the tracked set
23 high-authority
citation opportunities
activated during the pilot
113 high-value
keywords ranking in
Google’s top 10
185 total keywords
where the brand
gained visibility
What Changed in the Market
Pet insurance discovery no longer happens in one place. Buyers still use Google for searches such as “best pet insurance,” vet-cost questions, and breed- or condition-related coverage research. But those searches are only one part of the decision process.
Before choosing a provider, many pet owners also validate options through public discussions, educational creator content, and third-party review platforms. At the same time, AI systems are increasingly generating recommendations from those same sources. That means a brand can have solid search performance and still miss high-intent recommendation visibility if it is not well represented in the public environments shaping both buyer perception and AI answers.
In this category, trust is not a bonus. It is central to conversion. Buyers want proof points that feel independent, credible, and easy to evaluate before they commit to a policy.
What the Brand Needed
The brand did not just need broader awareness. It needed stronger performance in the sources that influence decision-making.
That meant improving three practical signals:
Increasing visibility in the
environments where buyers
actively compare providers
and decide which brands
feel most credible
Competitive consideration
Increasing visibility in the
environments where buyers
actively compare providers
and decide which brands
feel most credible
Competitive consideration
Strengthening the brand’s
representation in the
public pages and
discussions AI systems
use when generating
summaries and
recommendations
Strengthening the brand’s
representation in the
public pages and
discussions AI systems
use when generating
summaries and
recommendations
Citation presence
Appearing more often when pet owners explored pet insurance, vet costs, coverage options, and provider comparisons
Research visibility
What the Brand Needed
The objective was simple: become more visible where trust is formed and where purchase decisions start to narrow.
What We Did
Concentrated effort on high-impact evaluation surfaces
We identified the search and public-discussion environments most likely to shape how pet owners evaluate insurance options, especially around coverage decisions, provider comparisons, and vet-cost concerns. That allowed the campaign to prioritize the moments where visibility could influence action fastest.
Improved brand presence in trust-led third-party environments
We strengthened how the brand appeared across the sources buyers rely on for validation, including public discussions, creator-led pet education, and review-driven platforms. This improved the consistency of the brand’s presence in the places where recommendations are often formed.
Verified performance through measurable visibility signals
We tracked keyword movement, citation opportunity activation, and visibility across AI-relevant source environments to confirm that the campaign was producing real commercial discovery gains rather than just surface-level exposure.
“Our goal was not just to rank better. We wanted to be more visible in the places pet owners rely on when researching coverage, comparing providers, and deciding who to trust.”
— Marketing Team, Pet Insurance Brand
The Outcome
The campaign gave the brand a broader and more commercially useful visibility footprint across both search and AI-influenced discovery. As the brand gained a stronger presence in trusted public discussions, creator-led education, and third-party review surfaces, it improved how it showed up during the comparison stage of the buyer journey.
That shift matters because pet insurance buyers rarely move from search directly to conversion. They validate, compare, and revisit options before acting. By improving visibility in those trust-heavy environments, the campaign increased the brand’s chances of being considered earlier and more consistently throughout that process.
113 high-value
keywords ranking in
Google’s top 10
185 total keywords
where the brand
appeared
#6 average ranking
position across the
tracked set
23 high-authority citation
opportunities activated
The result is a stronger foundation for ongoing discovery as more pet insurance decisions begin with a combination of search, public validation, and AI-generated recommendations.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
Measurable, Repeatable
Programme
Build a durable foundation of
credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge.
Measurable, Repeatable
Programme
Build a durable foundation
of credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge.
Citation Architecture
Review
Identify which high-authority
community sources are and
aren't working in your favour
across AI platforms.
Citation Architecture
Review
Identify which high-
authoritycommunity
sources are and
aren't working in your
favour across AI platforms.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across
different AI systems.
——————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high-authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
——————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley
Founder and Head of Agency
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or
company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
—————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is
the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret,and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
—————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.
Founder and Head of Agency
Mark Huntley
For pet insurance providers, speed matters because purchase decisions are often shaped before a buyer ever lands on a provider’s website.
Pet owners researching coverage are comparing plans, reading reviews, checking community discussions, and increasingly asking AI tools to summarize their options. In that environment, brands benefit most when they can influence trusted decision-shaping sources quickly and with minimal wasted activity.
This campaign was built around that principle. Rather than pursuing broad awareness, CiteWorks Studio focused a small number of deliberate engagements on the public sources most likely to influence both buyer evaluation and AI-generated recommendations.
The result was a faster, more efficient expansion of the brand’s visibility across the places pet owners actually use to decide who to trust.
Key Outcomes
Delivered in 3 days with only 25 engagements:
113 high-value
keywords ranking in
Google’s top 10
185 total keywords
where the brand
gained visibility
6 as the average
ranking position
across the tracked set
23 high-authority
citation opportunities
activated during the pilot
What Changed in the Market
Pet insurance discovery no longer happens in one place. Buyers still use Google for searches such as “best pet insurance,” vet-cost questions, and breed- or condition-related coverage research. But those searches are only one part of the decision process.
Before choosing a provider, many pet owners also validate options through public discussions, educational creator content, and third-party review platforms. At the same time, AI systems are increasingly generating recommendations from those same sources. That means a brand can have solid search performance and still miss high-intent recommendation visibility if it is not well represented in the public environments shaping both buyer perception and AI answers.
In this category, trust is not a bonus. It is central to conversion. Buyers want proof points that feel independent, credible, and easy to evaluate before they commit to a policy.
What the Brand Needed
The brand did not just need broader awareness. It needed stronger performance in the sources that influence decision-making.
That meant improving three practical signals:
Appearing more often
when pet owners
explored pet insurance,
vet costs, coverage
options, and provider
comparisons
Research visibility
Strengthening the brand’s
representation in the
public pages and
discussions AI systems
use when generating
summaries and
recommendations
Citation presence
Increasing visibility in the
environments where buyers
actively compare providers
and decide which brands
feel most credible
Competitive consideration
The objective was simple: become more visible where trust is formed and where purchase decisions start to narrow.
What We Did
Concentrated effort on high-impact evaluation surfaces
We identified the search and public-discussion environments most likely to shape how pet owners evaluate insurance options, especially around coverage decisions, provider comparisons, and vet-cost concerns. That allowed the campaign to prioritize the moments where visibility could influence action fastest.
Improved brand presence in trust-led third-party environments
We strengthened how the brand appeared across the sources buyers rely on for validation, including public discussions, creator-led pet education, and review-driven platforms. This improved the consistency of the brand’s presence in the places where recommendations are often formed.
Verified performance through measurable visibility signals
We tracked keyword movement, citation opportunity activation, and visibility across AI-relevant source environments to confirm that the campaign was producing real commercial discovery gains rather than just surface-level exposure.
“Our goal was not just to rank better. We wanted to be more visible in the places pet owners rely on when researching coverage, comparing providers, and deciding who to trust.”
— Marketing Team, Pet Insurance Brand
The Outcome
The campaign gave the brand a broader and more commercially useful visibility footprint across both search and AI-influenced discovery. As the brand gained a stronger presence in trusted public discussions, creator-led education, and third-party review surfaces, it improved how it showed up during the comparison stage of the buyer journey.
That shift matters because pet insurance buyers rarely move from search directly to conversion. They validate, compare, and revisit options before acting. By improving visibility in those trust-heavy environments, the campaign increased the brand’s chances of being considered earlier and more consistently throughout that process.
113 high-value
keywords ranking in
Google’s top 10
185 total keywords
where the brand
appeared
6 average ranking
position across the
tracked set
23 high-authority citation
opportunities activated
The result is a stronger foundation for ongoing discovery as more pet insurance decisions begin with a combination of search, public validation, and AI-generated recommendations.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
Citation Architecture
Review
Identify which high-
authoritycommunity
sources are and
aren't working in your
favour across AI platforms.
Measurable, Repeatable
Programme
Build a durable foundation
of credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge..
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or
company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
—————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is
the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret,and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
—————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley, J.D. is the founder of CiteWorks Studio and a growth strategist focused on AI-driven discovery, citation architecture, and high-intent demand capture. With more than
a decade of experience across performance media, global
e-commerce, affiliate publishing, and search-led growth, he has built and scaled marketing systems that influence how brands are found, trusted, and chosen in competitive categories. His work centers on the signals that shape AI recommendations, including authority sources, prompt-cluster positioning, and recommendation rank across the moments that actually drive revenue.
Through CiteWorks Studio, Mark helps companies strengthen visibility, credibility, and decision-stage performance in an internet increasingly shaped by AI systems.
Founder and Head of Agency