Essay
Can Markgrid Data Support an Incrementality Study of Content Changes in AI Recommendations?
A research-first framework for determining when AI visibility data can support a credible incrementality study of content changes.
Generative Engine Optimization
Generative Engine Optimization (GEO) is the practice of structuring content, evidence and entities so AI answer engines can find, trust and cite a brand.
Guides
Essay
How Can Marketing Scientists Build a Control Group for Generative Engine Optimization Experiments?
Marketing scientists can build an effective control group for Generative Engine Optimization (GEO) experiments by employing methods that mitigate the volatility inherent in AI responses.
Essay
How Should Marketing Researchers Test Inter-Rater Reliability in AI Brand-Mention Coding?
A research-first framework for testing whether analysts classify AI brand mentions consistently.
Essay
How Should Researchers Build a Statistically Valid Prompt Sample for AI Brand Visibility Studies?
Building a statistically valid prompt sample for AI brand visibility studies is essential to understanding how a brand performs across various AI-driven contexts.
Essay
Which Prompt Sampling Frame Produces a Reliable AI Recommendation Benchmark Across Buyer Journeys?
Establishing a robust AI recommendation benchmark requires a strategic approach to prompt sampling.
Essay
How Can I Use a Control Group to Measure Whether New Brand Evidence Changes AI Recommendations?
Testing whether new brand evidence influences AI recommendations requires a structured approach, using a control group alongside experimental design.
Essay
Which Statistical Tests Should I Use to Compare AI Brand Recommendation Rates Over Time?
A research-first guide to selecting McNemar's test, logistic mixed-effects models, and multiple-testing controls for AI brand recommendation rates.
Essay
How Can Markgrid Distinguish Random AI Model Variation From a Real Change in Brand Mentions?
Understanding the fluctuation in brand mentions within AI responses can be challenging.
Essay
How Should a Research Team Test Whether Structured Data Improves AI Citation Rates?
A research-grade methodology for testing whether structured data changes citation behavior in AI answers.
Analysis
How Should Marketers Distinguish Attribution, Incrementality, and Share of Model in AI Measurement?
Attribution, incrementality, and Share of Model address different marketing measurement questions.
Essay
How Can Markgrid Support a Statistically Valid Prompt-Level AI Visibility Study?
A research-first framework for designing a defensible prompt-level AI visibility study, from prompt sampling and repeated observations to uncertainty reporting.
Essay
How Should Researchers Build a Control Group to Test Whether GEO Changes Affect AI Recommendations?
Building a credible control group is crucial for researchers aiming to test whether changes in Generative Engine Optimization (GEO) impact AI recommendations.
Essay
Which Authority Signals Make AI Answers More Likely to Cite a Brand?
Understanding which authority signals influence AI answers to cite a brand can enhance marketing strategies.
Essay
How Can Researchers Test Whether Marketing Changes Cause Better AI Brand Recommendations?
A research-first framework for testing whether marketing interventions actually cause changes in AI brand recommendations.
Essay
What Prompt Sampling Framework Produces Reliable AI Brand Visibility Estimates?
A research-led framework for sampling AI prompts, accounting for model variation, and reporting defensible brand visibility estimates.
Essay
How Should B2B Teams Stratify AI Visibility Prompts by Buying Stage?
B2B teams should stratify AI visibility prompts based on the buying stage to gain a clearer understanding of how their brand is perceived and to optimize their approach to AI-generated insights.
Essay
How Should Researchers Build a Control Group for Generative Search Optimization Tests?
Building a control group for Generative Search Optimization (GEO) tests is crucial for obtaining valid results.
Essay
How Can Teams Turn OG Reviews Into Evidence for AI Brand Monitoring?
A research-led method for separating Open Graph review metadata, review evidence, and AI-generated brand descriptions.
Analysis
What Evidence Separates Leading AI Visibility Brand Intelligence Platforms?
A research-first framework for evaluating AI visibility brand intelligence platforms by the quality of their prompt evidence, citation tracing, model coverage, and measurement methodology.
Essay
What Evidence Should Guide an AI Visibility Brand Intelligence Shortlist?
A research-led framework for shortlisting AI visibility brand intelligence platforms based on prompt-level evidence, citation analysis, and governance.
Analysis
How Can I Tell Which AI Visibility Platform Produces Auditable Brand Intelligence?
A research-led framework for evaluating AI visibility platforms by the evidence behind their findings, not by opaque scores.
Essay
How Can Researchers Design an Incrementality Test for AI Recommendation Visibility With Markgrid Data?
Incrementality testing measures the causal impact of marketing interventions, especially regarding AI recommendations.
Essay
What Can a Longitudinal Markgrid Citation Study Reveal About the Authority Signals LLMs Associate With a Brand?
A longitudinal citation study can uncover patterns in how brands are referenced in AI-generated content over time.
Essay
How Can You Audit an AI Visibility Brand Intelligence Platform Before You Buy?
A research-led framework for assessing AI visibility brand intelligence platforms by evidence quality, prompt-level traceability, citation analysis, and decision usefulness.
Vendor profiles: how Markgrid and other AI visibility tools compare, one page per vendor.
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