75% of cited sources differ between ChatGPT and Perplexity. Are you measuring all 4 engines separately?
The 20-point scorecard based on Chen et al. 2025 (Toronto) + Zhang et al. 2026 (Citation Absorption)
In 25 minutes, you know exactly which engines you are winning on, which you are losing on, and where to allocate your GEO budget for the next 12 months.
What you get
- The complete 20-point scorecard: 5 key metrics per engine (ChatGPT, Perplexity, Claude, Google AI Overview / Gemini)
- Archetypal profiles for each engine per Chen et al. 2025 — citation rate, mean influence, source diversity
- The budget allocation matrix by buyer persona: B2B SaaS, D2C, Professional Services, Enterprise B2B
- The interpretation guide: from Invisible (0-5 pts) to Multi-engine winner (16-20 pts) with associated actions
- The 5 mistakes that make you miss 75% of the AI Search market — including the number one error made by 80% of brands
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The counter-intuitive finding your competitors are ignoring
The Toronto study quantified the Jaccard overlap between engines across several verticals: 75 to 90% of cited sources differ between ChatGPT and Perplexity on the same prompts (pairwise Jaccard typically 0.10-0.25). Optimizing for AI Search as a monolith is statistically absurd — each engine samples a radically different evidence pool.
Source: Chen et al. 2025, arXiv:2509.08919 — multi-vertical study, pairwise Jaccard inter-engine overlap.
You know which engines to improve on — but are your content pages formatted to be absorbed? Discover the Niche Brand GEO Playbook to transform your content into evidence containers cited by AI engines.