Studies, experiments, reports, and benchmarks from the Ranking Wolf Lab — examining how ChatGPT, Gemini, Perplexity, and emerging generative engines select, surface, and cite local businesses.
Ranking Wolf publishes research only when the dataset and methodology can be documented and shared. Every item below carries its real status. We measure what can be measured, distinguish observations from hypotheses, and publish methodology before conclusions. Frameworks are labeled as frameworks, not as study findings, and no figures are published from a study that has not been completed.
A commissioned study of how ChatGPT, Gemini, and Perplexity recommend local businesses across El Paso, Horizon City, and Las Cruces. Dataset and methodology will be published with the findings.
A planned analysis of the citation, schema, and entity signals that tip ChatGPT toward naming a specific business.
Mapping where modern LLMs source their answers — directories, news outlets, Wikipedia, social. The hidden citation graph behind generative answers.
A planned repeatable benchmark for Gemini's local recommendation behavior across service industries.
How Perplexity selects, cites, and orders sources — and what patterns separate businesses that get cited from those that don't.
Our working framework of the ranking-factor categories that determine whether an LLM surfaces a business in a generative answer.
The same standard applies to client work: measured where measurable, documented, and reported without invented numbers. Our services cover organic search, local search and Google Maps, and AI search visibility.
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