Kitchen Appliance AI Search Case Study

In a 3-month long campaign with close to 200 engagements, this campaign generated an estimated $17,809.15 in total estimated monthly branded value. 

Methodology note: Directional estimate based on tracked keyword visibility and modeled paid-equivalent value. Not exact attribution.

As buyer research moved beyond product pages and into online communities, this kitchen appliance brand saw a clear gap: real purchase decisions were increasingly shaped by what people shared, compared, and recommended in public conversations. 

The brand partnered with CiteWorks Studio to build presence across those high-intent decision environments, strengthening both Google visibility and the citation signals that influence AI-generated recommendations.

Key Outcomes

• Drove a 15% month-over-month growth in overall LLM mentions
• Ranked for 2,398 keywords in Google’s top 10
• Optimized 100 online community threads to improve brand context in AI citations

What Changed in the Market

Where Buyers Decide Now: Online communities + AI Recommendations

The brand was seeing competing kitchen appliance brand outrank them on Google page 1 and wanted to secure stronger visibility for high-intent searches, especially from shoppers comparing features, pricing, and reviews. Organic search still mattered because it captured buyers at peak purchase intent.

At the same time, Google AI Overviews, Gemini, and ChatGPT became common tools for researching and comparing products. More shoppers began relying on AI-generated summaries that surfaced “best product" recommendations in a single answer.

In practice, ranking position alone wasn’t the full story. AI summaries reflect what the web already says, especially third-party pages and real-user discussions and those inputs materially shape the narrative buyers see during comparison.

While the shift was clear, the brand needed a repeatable path to earn visibility in these new decision channels and sustain it over time.

What the Brand Needed

Earn a consistent spot in AI “best-of” comparisons

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:

• Mentions: how often the brand was named in AI-generated answers
• Citations: which websites and pages AI systems referenced when describing the product
• Share of voice: how frequently the brand appeared compared with competing ice cream makers

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. Audited how AI product recommendations were being generated

We assessed how major AI tools described the brand and what sources they relied on to form those summaries. Our reporting mapped citation and reference patterns across AI Overviews, ChatGPT, Gemini, AI Mode, Perplexity, and Copilot, revealing which product pages, reviews, and community discussions most often shaped how the brand appeared in AI answers.

2. Measured impact month over month and iterated fast

We tracked month-over-month movement to see whether new activity translated into more brand mentions, stronger citations, and improved share of voice in AI responses. This made it easier to spot which consumer questions and comparison angles (features, pricing, ease of use, results) were gaining traction. We then adjusted based on performance, scaling what worked and pausing what didn’t deliver measurable lift.

3. Strengthened the sources AI systems were already referencing

In consumer appliances, buying decisions are heavily influenced by “real-world proof” i.e. what people recommend, compare, and validate publicly. Since third-party sources and online community conversations were already influencing AI-generated product summaries, we focused on strengthening accurate, positive brand context in those environments.

Rather than relying only on generic blog production, CiteWorks Studio executed an AI citation strategy designed to increase the quality and consistency of brand references tied to common “best product” and comparison searches. 

The Outcome

Measurable lift across Google and AI answers

Across traditional search and AI-generated product summaries, the brand saw measurable improvements in visibility for high-intent “best product” queries.

• Average ranking position of #6 secured for all important keywords
• 15% increase in overall LLM mentions across high-intent prompts
• 2,398 keywords appearing in the top 10 results, covering 1.2M in combined monthly search volume and ~$585 in paid-search benchmark value (keyword volume × cost per click)
• Brand context strengthened across 100 high-impact community sources and cited pages influencing AI answers

These gains weren’t just a short-term spike. They created a stronger foundation for sustained discovery across both Google and AI answers.