The Retrieval-to-Citation Funnel in AI Search: Evidence from Eight B2B Visibility Projects

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Original abstract

When an AI search engine pulls a web source into its context to answer a query, how often does that source actually appear as a citation in the answer? This step, the conversion from retrieval to citation, sits at the center of generative engine optimization but has not been quantified in public data. This paper measures that rate across eight live B2B visibility projects covering manufacturing, tourism and rail, and other B2B sectors, tracking commercial prompts daily across ChatGPT, Perplexity, and Google AI Overview over one month (~13,200 domain-engine observations, 5,000+ unique source domains). Three findings: (1) pooled retrieval-to-citation conversion is 55.6%, ranging from 40.9% (ChatGPT) to 76.8% (Google AI Overview), an ordering stable across nearly all projects; (2) engines cite largely disjoint source pools — Jaccard overlap of cited domains stays between 0.12 and 0.21 in every project; (3) which source type converts best is engine-specific, not universal. This is a practitioner working paper; it has not been peer reviewed. All results are correlational, cover one month, and rest on a single measurement stack (Peec.ai). Client data is anonymized (P1–P8, sector labels only).

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