[ CASE STUDY ]
Household Appliance AI Search Case Study
How a Household Appliance Brand Improved AI Visibility by Improving Its Citation Footprint
In a 3-month long campaign with close to 200 engagements, this campaign generated an estimated $122,454.73 in total estimated monthly branded value. That included $119,757.18 in organic keyword value and $2,697.55 in LLM-cited pages value.
Methodology Note
Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.
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.
[ KEY OUTCOMES ]
Results at a Glance
What we achieved from a 3-month long campaign with 200 engagements:
100
online community threads optimized to improve brand context in AI citations
13,679
keywords ranked in Google’s top 10
400%
month-over-month lift in ChatGPT brand mentions
[ MARKET CONTEXT ]
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.
[ THE CHALLENGE ]
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:
Citations
Which websites and pages AI systems referenced when describing the product
AI Share of Voice
How frequently the brand appeared compared with competing vacuum brands
Brand Mentions
How often the brand was named in AI-generated answers
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.
[ OUR APPROACH ]
What We Did
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 ]
Results
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.
#7 average ranking position secured for all high-intent keywords
400% increase in brand mentions in ChatGPT across 100+ high-intent queries
100 high-impact community sources and cited pages with strengthened brand context 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.
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
Citation Architecture Review
Identify which high-authority community 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.
[ LEARN MORE ]
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.
Founder & Head of Agency
[ ABOUT THE AUTHOR ]
Mark Huntley
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.