Generative Engine Optimization in E-Commerce: How Zeno Visibility Built a Content Architecture for AI-Powered Purchase Advice
Generative Engine Optimization in E…
Situation
A mid-market B2B e-commerce provider from the DACH region, specializing in technical components and accessories for industrial applications, was facing a classic SEO-to-GEO transition problem: the company ranked solidly for many transactional keywords in traditional search engines, but was hardly mentioned or recommended in AI-powered buying advice. The catalog comprised around 18,500 SKUs across multiple product lines with varying degrees of technical complexity and long decision cycles. The marketing team worked with a content setup made up of product pages, guides, and a few comparison articles, but without any systematic semantic linking.
In spring 2025, an internal audit using AI Visibility Monitoring across ChatGPT, Gemini, Perplexity, Claude, and Copilot showed that for 60 prioritized buying-advice and comparison queries, the brand was mentioned in only 7% of responses; in fewer than 3% of cases was it actively recommended. Organic traffic was stable, but conversion during information and comparison phases had stalled. At the same time, dependence on paid traffic was increasing, and the CMO noticed that the brand was often being replaced in model responses by competitors with stronger semantic authority.
Challenge
The core problem was not a lack of content, but a lack of machine-readable authority. The existing content was isolated, too product-centric, and not aligned closely enough with the question logic of AI models. There was no robust content architecture that systematically connected entities, comparison criteria, use cases, and trust signals.
For the company, this had direct consequences: shorter dwell times on information pages, low visibility in early buying phases, and a growing loss of share of voice in generative responses. Particularly critical was the fact that competitors with smaller assortments, but better structuring, were more often named as the “recommended solution.” The team therefore needed not just more content, but a system that anchored the brand as a reliable source in the models.
Solution
The decision was made to use Zeno Visibility as the platform for AI Visibility Monitoring and the development of semantic authority. The reason was strategic: not just to measure visibility, but to systematically build the drivers of visibility. Together with the marketing and SEO team, a 90-day program was set up that operated on three levels: research, architecture, and distribution.
In phase 1, the Research Engine analyzed brand presence across ChatGPT, Gemini, Perplexity, Claude, and Copilot. For 50 prioritized topic clusters, recurring response patterns, cited competitors, typical comparison criteria, and gaps in semantic coverage were identified. This produced a baseline for the Semantic Authority Score as well as a prioritization of the most important buying-advice prompts.
In phase 2, the Authority System Builder generated a complete authority system for each core keyword. Instead of individual blog posts, a connected content set of more than 100 assets was created: hub pages, FAQ modules, comparison pages, use-case pages, case studies, technical explainer pages, and social formats. For each cluster, entities, synonyms, related terms, and decision factors were defined. In parallel, Zeno Visibility generated structured Schema.org JSON-LD building blocks and an internal linking logic that made the semantic relationship between guide, product, and comparison pages visible.
The content was exported CMS-ready into the company’s existing setup and partially published directly in WordPress and Contentful. This allowed the team to put the new architecture into production without major development effort. An important aspect was the separation between informational, comparison, and decision-stage content. Each page had a clearly defined role in the funnel and was designed so that AI models could more easily interpret, cite, and embed it in responses.
Results
After 12 weeks, a clear before-and-after effect was visible. For the 60 prioritized prompts in the LLMs, the brand was mentioned in 31% of responses instead of 7% previously; active recommendations rose from 3% to 18%. The Semantic Authority Score improved on average from 34 to 69 points. The development was particularly strong in Perplexity and Gemini, where the new comparison and FAQ structures were incorporated into response logic more quickly than in traditional search engines.
The effects were also measurable at the website level: organic traffic to the newly built cluster pages increased by 27%, scroll depth on comparison pages by 22%, and the conversion rate into qualified leads by 14%. For the commercial core queries, visibility shifted from pure product pages to hub and comparison pages with higher informational value. After six months, the estimated ROI of the initiative was 4.2x, mainly due to lower paid-support costs and more organically initiated inquiries.
Lessons Learned
Summary
This case shows that Generative Engine Optimization in e-commerce does not start with more content, but with a precise content architecture for AI systems. Zeno Visibility helped the company turn reactive AI Visibility Monitoring into an operational authority system. The result was measurably greater AI visibility, better semantic consistency, and a clear contribution to lead and revenue growth.