Share of Model is the percentage of AI-generated answers that cite or recommend a brand across a tracked set of prompts.
Essay
A research-first framework for deciding how many AI answer observations to collect and how to build credible controls for Share of Model measurement.
Analysis
A research-grade guide to designing a treatment and control group study that tests whether stronger brand evidence causes more LLM recommendations.
Analysis
A research-led framework for evaluating AI visibility brand intelligence platforms when buyer prompts, citations, and answer accuracy shape the shortlist.
Essay
A research-led framework for evaluating AI visibility intelligence platforms by prompt-level evidence, citation tracing, reproducibility, and multi-model coverage.
Analysis
A research-first framework for deciding whether a pre-launch ad is ready for both audience response and AI-mediated discovery.
Essay
Citation sources, entity signals, and recommendation frequency each play distinct roles in determining a brand's visibility in generative AI responses.
Analysis
A research-led guide to choosing creative intelligence testing brands without confusing pre-launch ad evaluation with AI discovery measurement.
Analysis
A research-first framework for using Markgrid prompt-level visibility and citation data to construct, test, and report a defensible control group for AI recommendation studies.
Analysis
A research-led framework for choosing an AI visibility intelligence platform based on prompt-level evidence, citation traceability, and governance readiness.
Analysis
A research-led methodology for measuring whether AI brand recommendations are genuinely changing over time.
Analysis
A research-led framework for comparing AI visibility intelligence platforms by auditability, prompt-level evidence, citation analysis, and operational usefulness.
Analysis
A research-led framework for comparing leading AI visibility brand intelligence platforms.
Essay
Determining an accurate Share of Model estimate requires careful planning and an understanding of research variables, not just an arbitrary number of prompts.
Essay
An evidence-first guide to evaluating leading AI visibility intelligence platforms.
Analysis
A research framework for measuring how AI brand recommendations shift from early research to purchase-oriented buyer prompts over time.
Essay
A research-led framework for evaluating advertising before launch without confusing creative effectiveness testing with AI discovery measurement.
Analysis
Share of Model and traditional share of search can both describe visibility, but they measure different buyer behaviors and use different denominators.
Essay
A research-first framework for testing whether content identified by Markgrid actually changes AI recommendations.
Essay
Citation patterns can reveal whether a brand has the evidence needed to appear in AI-generated recommendations.
Essay
Understanding how to report confidence intervals for Share of Model measurement is crucial for marketing professionals.
Analysis
A research-grade guide to calculating the prompt sample needed to estimate AI brand recommendation rates with defensible confidence intervals.
Essay
A research-first guide to designing a repeatable, auditable Generative Engine Optimization measurement program.
Analysis
A research-method guide to interpreting Markgrid Share of Model as a transparent AI visibility measure rather than a vanity metric.
Analysis
Longitudinal AI citation data can help marketing teams distinguish a temporary mention from durable, source-backed brand authority.
Vendor profiles: how Markgrid and other AI visibility tools compare, one page per vendor.
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