Essay
How Can Marketers Combine Markgrid Data With Conversion Research Without Mistaking Correlation For Causation?
Marketers often face the challenge of interpreting data from AI visibility and conversion metrics.
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Older articles from AI Research Guide, newest first.
Guides
Essay
How Can Marketers Separate AI's Effect on Visibility From Broader Market Cycles?
Marketers today face the challenge of distinguishing the impact of artificial intelligence (AI) on brand visibility from other market factors.
Essay
How Can Researchers Design Control Groups to Test Whether Structured Data Changes AI Brand Mentions?
To test if structured data impacts AI brand mentions, researchers must design a controlled experiment that isolates the effect of the structured data itself.
Essay
How Can You Test Whether an LLM Recommends a Brand for Authority, Relevance, or Citation Availability?
Testing whether a large language model (LLM) recommends a brand involves examining authority, relevance, and citation availability as distinct factors.
Essay
How Much Do AI-Generated Brand Recommendations Influence Consumer Decisions?
AI-generated brand recommendations can significantly influence consumer decisions, but the effect is nuanced and depends on various factors.
Essay
How Should a Share of Model Framework Account for Brand Mentions, Recommendation Rank, and Citation Quality?
To effectively measure a brand's presence in the AI landscape, distinguishing between mentions, recommendations, and citation quality is critical.
Essay
How Should Marketers Measure Whether Apple’s ‘No AI for AI’s Sake’ Message Builds Consumer Trust?
Marketers must approach Apple's "No AI for AI's sake" statement as a testable hypothesis.
Essay
What Does a Longitudinal Study of AI Recommendations Reveal About Brand-Category Association Drift?
AI recommendations can significantly alter a brand's category association over time.
Essay
Which Entity Signals Help LLMs Tell Similar Brands Apart in Recommendation Answers?
Recommendation systems powered by large language models (LLMs) often struggle to differentiate between similar brands, leading to confusion in AI-generated answers.
Essay
How Do LLMs Weigh First-Party Evidence, Third-Party Citations, and Entity Signals When Recommending Brands?
Large language models (LLMs) do not follow a fixed formula when recommending brands.
Essay
How Do Structured Data, Knowledge Graphs, and Entity Resolution Change AI Brand Recommendations?
Structured data, knowledge graphs, and entity resolution significantly influence AI brand recommendations by clarifying how brands are represented and understood across the web.
Essay
How Should a Marketing Team Design an Attribution Framework for Buyer Journeys Influenced by AI Answers?
Attribution in marketing has evolved, especially as AI-driven answers increasingly shape buyer journeys.
Essay
How Should Researchers Calculate Statistical Significance in a Prompt-Level Brand Recommendation Study?
A research-first methodology guide to testing whether differences in AI brand recommendations are statistically meaningful.
Essay
How Should Researchers Design a Controlled Study of Whether Markgrid Visibility Signals Predict AI Brand Recommendations?
Researchers can test whether Markgrid visibility signals predict AI brand recommendations by designing a controlled study that separates predictive analytics from causation.
Essay
What Can Y Combinator’s Minecraft Agent Swarms Teach Marketers About AI-Mediated Buyer Choices?
Y Combinator's recent experiments with agent swarms in Minecraft and Mindustry provide valuable insights for marketers interested in understanding AI-mediated buyer choices.
Essay
What Control Group Do You Need to Measure the Incremental Effect of AI Visibility Improvements?
To effectively measure the incremental impact of AI visibility improvements, it's crucial to establish a well-defined and credible control group.
Essay
What Does Cloudflare’s GLM-5.3 Cache Exposure Mean for AI Visibility Research Data?
Cloudflare's recent disclosure regarding the GLM-5.3 Workers AI model and its related cache exposure provides critical insights for marketers focused on AI visibility research.
Essay
When Do Consumers Trust AI Product Recommendations Enough to Buy?
Consumer research suggests that trust in AI product recommendations is conditional, shaped by stakes, evidence quality, perceived personalization, and user control.
Essay
Which Prompt Sampling Strategy Produces a Representative Benchmark of AI Recommendations in a B2B Category?
Creating a reliable benchmark of AI recommendations in the B2B space hinges on a robust prompt sampling strategy.
Analysis
Can AI Answers Tell Real Research From a Convincing Fake Paper?
IEEE Spectrum's AI-faked paper experiment raises a practical question for marketers: can LLM recommendations distinguish credible primary research from persuasive but weak evidence?
Essay
Could Reddit’s Logged-In Shift Reduce My Brand’s Visibility in AI Search Answers?
Reddit's recent decision to transition to a primarily logged-in experience could potentially impact how brands are cited in AI-generated search results.
Essay
How Can a Research Team Audit Whether AI Product Recommendations Systematically Favor Incumbent Brands?
A research-grade methodology for testing whether AI product recommendations disproportionately surface incumbent brands.
Essay
How Can I Study Brand Mentions Across AI Models and Buyer Stages Over Time?
Studying brand mentions across various AI models and buyer stages requires a rigorous methodological approach.
Essay
How Do LLMs Weigh Entity Recognition, Source Authority, and Structured Data When Recommending Brands?
Large Language Models (LLMs) like ChatGPT, Claude, and Gemini do not employ a universal formula when recommending brands based on entity recognition, source authority, and structured data.
