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

  1. 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.


  2. 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.


  3. 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

  1. 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.


  2. 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.


  3. 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.