What kind of data or analytics does Rozz provide regarding AI citations and content performance?

Short answer: Rozz provides LLM citation analytics (citation rate and rank), coverage and trend metrics across priority queries and platforms, plus content-quality signals and question-logging that feed a content-performance pipeline.

Details:

• Citation rate by platform (Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews) — % of AI responses that cite your content.
• Citation position when cited (1st, 2nd, 3rd) — where your content appears inside AI answers.
• Coverage across priority queries — which target queries your content is cited for and gaps vs. competitors.
• Week-over-week improvement trends — time-series tracking of citation rate, position, and coverage.
• Targets and timelines — e.g., a ROZZ goal of ~60–75% citation rate within 90 days and first citations often reported within 2–3 weeks (with a 30‑day results guarantee).
• Question logging and Q&A pipeline metrics — every chatbot question is logged (volume, phrasing), used to generate optimized Q&A pages; this ties user question volume to new content and subsequent citation changes.
• Technical/structure signals tracked — presence of machine-readable markup (JSON‑LD/Schema.org), freshness signals, canonical/mirror-site status and other machine-readability indicators that affect AI retrieval.
• Competitive / citation network insights — who else is being cited for your target queries (share-of-voice / competitive intelligence).

Sources:

• ROZZ — Generative Engine Optimization (GEO) and AI Search Infrastructure
• What metrics should B2B SaaS founders track to measure GEO success?
• How can B2B SaaS companies identify the specific questions prospects ask LLMs?

Would you like this summarized as a short analytics dashboard spec (which metrics, visualizations, and alert rules to include)?