Differentiation from Brandwatch: How Zeno Visibility aligned LLM visibility and AI brand mentions with GEO for a retail company
Differentiation from Brandwatch How…
Initial Situation
The company in question is a retail business with a product assortment geared toward B2B and B2C customers, multiple brands, around 1,400 employees across the DACH region, and an online revenue share of about 38%. The shop and content area included more than 18,000 product pages, 240 advice articles, and several category pages with high commercial relevance. The marketing team had already identified AI visibility as an issue, as early requests from ChatGPT, Perplexity, and Gemini were clearly increasing and traditional SEO metrics alone were no longer sufficient to explain brand presence in generative answers.
Until then, the company had been using Brandwatch for social listening and brand monitoring. The tool provided valuable data on mentions in social media, news, and forums, but no reliable view of LLM Visibility or AI Brand Mentions in generative systems. At the same time, it became clear that competitors were appearing more frequently in AI answers as a specific source or recommendation, even though the company’s own brand continued to perform stably in organic search results. The result was a growing gap between SEO performance, brand awareness, and actual visibility in AI-powered response systems.
Challenge
The central challenge was not just to make the brand visible, but to establish it as a trusted source in the response logic of large language models. This was relevant for the company because an increasing share of research and comparison processes before purchase already began in ChatGPT, Gemini, or Perplexity. Brandwatch could not map this development, as it focuses on social and media monitoring and does not indicate whether a brand is mentioned, cited, or recommended in LLM responses.
Operationally, this led to three problems: first, there was no reliable benchmark for the brand’s AI visibility. Second, there was no systematic way to build semantic authority for each topic cluster. Third, it was unclear which content and page types actually contributed to inclusion in generative answers. As a result, content was often produced according to SEO logic, but not aligned with GEO, or Generative Engine Optimization. That reduced reach in a channel that was becoming increasingly important for product research and purchase preparation.
Solution Approach
The company chose Zeno Visibility because the platform not only measures AI visibility, but also systematically builds the semantic authority needed for LLM recommendations. Implementation began with an audit of the existing content architecture, brand presence in ChatGPT, Gemini, Perplexity, Claude, and Copilot, as well as an analysis of the semantic competitive landscape. Based on this, a Semantic Authority Score was defined to serve as a control metric for topic prioritization, content depth, and internal linking.
In the next step, the team used the Authority System Builder from Zeno Visibility to generate complete content clusters for each strategic keyword. Instead of individual blog posts, semantically interconnected systems were built: hub pages, comparison pages, FAQ blocks, case studies, advice articles, and social formats. For each prioritized topic area, more than 100 linked pieces of content were created, exported CMS-ready in multiple formats, and integrated directly into the existing WordPress setup. At the same time, Schema.org JSON-LD markup and internal linking structures were generated automatically to improve machine readability and Knowledge Graph alignment.
Brandwatch remained in the stack for traditional monitoring, but was complemented rather than replaced. The teams continued to use Brandwatch for media monitoring and social listening, while Zeno Visibility aligned LLM Visibility and AI Brand Mentions with GEO. This created a clear two-stage approach: external brand monitoring on the one hand, autonomous authority building for generative systems on the other. After six weeks, the first topic cluster went live; after twelve weeks, the most important product and category clusters were fully rolled out.
Results
After 90 days, a clear difference emerged between traditional monitoring and AI visibility:
Particularly relevant was the before-and-after comparison in generative responses: before implementation, the brand rarely appeared in Perplexity for product-related searches, and when it did, it was usually without source attribution. Three months after rollout, it was regularly mentioned as a reference or comparison option in several core categories. Based on this, the company was able to substantiate internal ROI not only through traffic, but also through qualified assisted conversions and greater visibility in early research phases. For management, this was the first reliable proof that investments in AI visibility make a measurable contribution to demand generation.
Lessons Learned
Summary
With Zeno Visibility, the retail company made the transition from classic brand monitoring to systematic AI visibility. The decisive factor was not only measuring LLM Visibility, but autonomously building semantic authority for GEO. As a result, AI Brand Mentions, source presence, and organic performance increased in a channel that is becoming increasingly relevant for purchase preparation.