
How a Household Appliance Brand Improved AI Visibility by Improving Its Citation Footprint
How a Household Appliance Brand Improved AI Visibility by Improving Its Citation Footprint
As shoppers increasingly relied on online communities and AI summaries to compare home appliances, this brand saw product discovery shift away from product pages alone.
They partnered with CiteWorks Studio to build visibility where recommendations are formed, across high-intent public discussions and the sources AI systems reference when generating answers.
As shoppers increasingly relied on online communities and AI summaries to compare home appliances, this brand saw product discovery shift away from product pages alone.
They partnered with CiteWorks Studio to build visibility where recommendations are formed, across high-intent public discussions and the sources AI systems reference when generating answers.

100+ cited pages
influenced,
improving the brand’s
presence in the sources
AI systems refer to
2,791 keywords ranked
in Google’s top 10
Key Outcomes
Key Outcomes
71% increase in brand
mentions in AI Overviews
Optimized 100
online community
threads to improve
brand context in AI
citations
Ranked for 13,679
keywords in Google’s
top 10
Drove a 400%
month-over-month
lift in ChatGPT brand
mentions
What Changed in the Market
Where Shoppers Decide Now: Comparisons + AI Summaries
In home appliances, a small number of high-authority community forums, particularly those focused on home improvement and long-term purchase value, disproportionately shape what AI tools recommend. The brand had limited visibility in exactly those sources.
At the same time, the brand was seeing competitors outrank them on Google page 1 for high-intent searches and comparison-style queries. Google AI Overviews, Gemini, and ChatGPT also became common tools for researching and comparing home appliances. More shoppers started trusting AI-generated summaries that pulled recommendations into a single answer, often before they clicked through to any site.
In practice, ranking position wasn't the full story anymore. AI answers reflect what the web already says, especially third-party reviews and real-user discussions, which shapes how buyers perceive performance, reliability, and value. The brand needed to win both page-1 search visibility and LLM visibility inside AI recommendations to stay competitive.
Brand discovery was moving to AI, without them
When it comes to hiring, trust issues surface
fast and public conversations
about fake listings,
scams, and poor candidate experiences
can spread widely.
That made the platform’s citation
footprint (the sources AI systems relied on to
summarize and compare brands) a real risk
point at the decision moment,
when employers were choosing
where to post roles.
Employers increasingly turned to tools
like Google AI Overviews, Gemini, and ChatGPT
when choosing a job posting platform.
These systems don’t just rank pages;
they synthesize
recommendations from across the web,
drawing heavily on high-authority public
forums and online communities.
The platform recognized a structural risk:
even a handful of prominent negative threads could
disproportionately shape what AI systems repeated.
Meanwhile, positive sentiment buried in
low-visibility corners of the internet
had no influence at all.
The core problem wasn’t brand
reputation in the traditional sense.
It was citation architecture i.e. which
sources were being pulled into
AI answers, and what those sources said.
What the Brand Needed
What the Brand Needed
Win visibility in AI answers
The brand needed a clearer way to diagnose and improve how it appeared across both traditional search results and AI-driven product discovery.
To do that, they needed a repeatable measurement framework that could track:
A reliable way to measure and strengthen AI visibility.
The team needed a repeatable measurement framework to track:
Win visibility in AI answers
The brand needed a clearer way to diagnose and improve how it appeared across both traditional search results and AI-driven product discovery.
To do that, they needed a repeatable measurement framework that could track:
How often the brand
is named in AI-generated
answers
Brand Mentions
The URLs and sources
AI platforms reference
while generating
responses,
including online
community forums
where real users
discuss pain points
and comparisons
Citations
The brand’s share of
appearances
relative to tracked
competitors
across AI answers
AI Share of Voice
which websites
and pages AI
systems referenced
when describing the
product
Citations
how frequently the
brand appeared
compared with
competing vacuum
brands
AI Share of Voice
The brand’s share of
appearances
relative to tracked
competitors
across AI answers
AI Share of Voice
how often the
brand was named
in AI-generated
answers
Brand Mentions
What the Brand Needed
The aim wasn’t only to climb Google page 1. It was also to build reliable LLM visibility, so the brand showed up consistently when shoppers were making high-intent comparisons at the moment.
This could help the brand remain
a top choice for employers and hiring managers.
The final requirement was identifying an
agency partner that could deliver this
as a measurable,
repeatable program.
The aim wasn’t only to climb Google page 1. It was also to build reliable LLM visibility, so the brand showed up consistently when shoppers were making high-intent comparisons at the moment.
What We Did
Mapped AI Visibility and Citation Sources
We assessed how AI platforms referenced the
brand and which sources
most consistently influenced those answers.
Our reporting tracked citation and mention patterns
across AI Overviews, ChatGPT, Gemini, AI Mode,
Perplexity, and Copilot, identifying
the domains and discussion environments
shaping AI-generated recommendations
in the category.
Tracked Momentum Month-Over-Month
We tracked month-over-month movement
to understand whether new activity increased
brand mentions in AI answers and
by how much. This helped identify which
topics, discussion formats, and source types
were being referenced more frequently across AI
Overviews, ChatGPT, and Gemini.
We also monitored whether citations were
consolidating around more accurate, higher
trust sources over time. Based on performance,
we scaled what delivered measurable lift and
paused approaches that didn’t.
Focused on the Channels LLMs Were
Already Pulling From
In the employment sector, conversation volume is
enormous. Popular social forums were
among the brand’s top cited domains,
so we focused our efforts on building positive
perception on these platforms to influence the
way LLMs talked about the brand.
Instead of producing run-of-the-mill blog posts,
CiteWorks Studio implemented an
AI citation strategy focused on improving
the brand's representation in high-intent,
public discussions tied to top employment queries.
By strengthening high-authority
community conversations and references,
we shifted the sources LLMs drew from
when generating answers about the client.
Over time, these discussions became
the most trusted context LLMs surfaced, helping
shape brand perception more positively.
“The shift wasn’t just in our rankings but in what AI systems
were recommending when employers searched
without knowing our name. That was a different kind of
visibility, and it’s the kind that matters now”
— VP of Marketing, Job Posting Platform
Mapped how AI recommendations were being formed
We began by reviewing how leading AI tools described the household appliances' brand and which sources they pulled into those summaries. Our visibility reporting tracked citation patterns across AI Overviews, ChatGPT, Gemini, AI Mode, Perplexity, and Copilot. This showed which product pages, reviews, and online discussions most often shaped how the brand appeared in AI answers.
Tracked lift month over month and optimized continuously
We monitored month-to-month movement to see whether new activity led to more brand mentions, stronger citations, and improved share of voice in AI responses. This made it easier to identify which shopper questions and comparison themes (features, pricing, ease of use, performance) were gaining traction. We then adjusted based on results, doubling down on what worked and pausing what didn’t produce measurable lift.
Strengthened the sources AI systems relied on
For consumer appliances, purchase decisions are heavily influenced by what people recommend, compare, and validate on public forums. Since third-party sources and online community conversations were already influencing AI-generated summaries, we focused on strengthening accurate, positive brand context in those environments.
Rather than relying only on generic blog output, CiteWorks Studio executed an AI citation strategy designed to increase the quality and consistency of brand references tied to comparison searches.
The Outcome
Measurable Gains Across SERPs and AI Recommendations
Across traditional search and AI-generated product summaries, the brand saw measurable improvements in visibility for high-intent queries.
Measurable Gains Across SERPs and AI Recommendations
Across traditional search and AI-generated product summaries, the brand saw measurable improvements in visibility for high-intent queries.
Importantly, the gains were not a one-time spike.
By building a durable base of credible citations,
the firm now has a self-reinforcing foundation,
one that continues to influence
AI answers as new queries emerge.
Improved citation context across
100+ high-authority pages and
discussion sources influencing
AI answers
2,791 keywords appearing in the
top 10, covering
406,000 in combined monthly
search volume and ~$5,262
in paid search benchmark value
(keyword volume × cost per click)
71% increase in brand
mentions in AI Overviews
in a month, measured
across 30,000+
tracked prompts.
Average ranking
position of
#6 forall high-intent
keywords
in the Google SERPs
Close to 400
citation-bearing
engagements
delivered in 4 months
Average ranking
position of #7
secured for
all high-intent
keywords
400% increase
in brand mentions
in ChatGPT
across 100+
highintent queries
Brand context
strengthened across
100 high-impact
community sources
and cited pages
influencing AI answers
13,679 keywords appearing in
the top 10 results, covering 3.9M
in combined monthly search volume
and ~$4,866 in paid-search benchmark
value (keyword volume × cost per click)
These gains weren't just a short-term spike. They created a stronger foundation for sustained discovery across both Google and AI answers.

Measurable, Repeatable
Programme
Build a durable
foundation of credible
citations that compounds
over time and continues
to influence AI answers
as newqueries emerge

Measurable, Repeatable
Programme
Build a durable
foundation of credible
citations that compounds
over time and continues
to influence AI answers
as newqueries 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-
authority community
sources are and aren't
working in your favour
across AI platforms.
Want to Understand Your AI Citation Footprint?
Want to Understand Your AI Citation Footprint?

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.
We start every engagement with a full audit.
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.

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-
authority community
sources are and aren't
working in your favour
across AI platforms.
Understanding AI Search Visibility
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.
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, highauthority 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.
—————————————————
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 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, highauthority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
——————————————————
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, highauthority 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 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.
——————————————————
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
About the author
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.

Founder and Head of Agency
Mark Huntley
Mark brings over a decade of high-stakes marketing experience, merging deep expertise in performance media with a hands-on understanding of global e-commerce. His vision for CiteWorks Studio was simple: create an agency where creativity meets systems thinking, and where AI isn’t a gimmick — it’s the baseline.
Prior to founding SBM, Mark led digital campaigns from break out startups to fortune 500’s across the world, architecting strategies that reached millions and converted attention into tangible results. Today, he drives the strategic direction of our work, ensuring every campaign is dialed in, data-backed, and built to perform.
Mark brings over a decade of high-stakes marketing experience, merging deep expertise in performance media with a hands-on understanding of global e-commerce. His vision for CiteWorks Studio was simple: create an agency where creativity meets systems thinking, and where AI isn’t a gimmick — it’s the baseline.
Prior to founding SBM, Mark led digital campaigns from break out startups to fortune 500’s across the world, architecting strategies that reached millions and converted attention into tangible results. Today, he drives the strategic direction of our work, ensuring every campaign is dialed in, data-backed, and built to perform.