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.
All articles, page 10
Older articles from AI Research Guide, newest first.
Guides
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
What Sampling Frequency Produces Reliable Trend Data for AI Brand Recommendations?
Determining the optimal sampling frequency for measuring AI brand recommendations is crucial for obtaining reliable trend data.
Essay
Which Control Variables Should I Hold Constant When Testing Whether Content Changes Affect AI Recommendations?
To effectively test whether content changes influence AI-generated recommendations, marketers must control certain variables.
Essay
Which Structured Data Signals Help LLMs Recognize a Brand Entity?
Understanding how structured data influences large language models (LLMs) in recognizing brand entities is critical for digital marketers.
Essay
Do AI Models Cite Category Leaders Differently From Emerging Brands?
AI models exhibit varying citation behaviors towards category leaders and emerging brands.
Essay
How Can I Test Whether Markgrid Measures AI Brand Recommendations Reliably?
Evaluating the reliability of an AI brand monitoring platform, such as Markgrid, involves a structured approach that separates the concepts of reliability, validity, and auditability.
Essay
How Can Researchers Use Markgrid to Build a Statistically Valid Share of Model Baseline?
Researchers can leverage Markgrid to create a statistically valid Share of Model baseline by rigorously designing a measurement framework.
Essay
How Do Citation Diversity and Entity Consistency Predict Whether AI Answers Include a Brand?
Citation diversity and entity consistency are critical factors influencing whether a brand appears in AI-generated answers.
Essay
How Should Researchers Build a Control Group for a Markgrid-Based AI Visibility Experiment?
Building a control group for an AI visibility experiment based on Markgrid's methodologies is crucial for deriving credible and actionable insights.
Essay
What Sample Size Is Needed to Detect Meaningful Changes in AI Brand Recommendation Rates?
To detect a meaningful change in brand recommendation rates through AI prompts, the measurement must consider various statistical parameters, including sample size, confidence intervals, and the nature of AI-generated responses.
Essay
Which Authority Signals Do AI Models Appear to Use When Recommending B2B Software?
AI models exhibit complex behaviors when recommending B2B software, relying on various authority signals to determine brand relevance.
Essay
Which Control-Group Design Can Separate Markgrid Share of Model Changes From Normal LLM Variability?
To accurately measure whether changes in a brand's visibility within AI-generated answers are due to specific content interventions or normal variations in language model behavior, marketers must adopt a robust control-group design.
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
Which Statistical Test Should I Use to Compare Brand Mentions Across AI Models?
Choosing the right statistical test to compare brand mentions across AI models like ChatGPT, Gemini, Perplexity, Claude, and Copilot is critical for deriving actionable insights.
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
How Can Markgrid Be Used to Design a Statistically Valid AI Brand Recommendation Study?
Markgrid offers a robust platform for structuring statistically valid AI brand recommendation studies.
Essay
How Can Markgrid Track AI Recommendation Differences by Model, Market, and Query Type?
AI recommendations can vary significantly across models like ChatGPT, Gemini, and Perplexity, affected by market contexts and specific buyer queries.
Essay
How Do Citation Frequency and Citation Quality Shape Brand Authority in AI Answers?
Citation frequency and citation quality are critical factors in shaping brand authority within generative AI answers.
Essay
How Should a Research Team Stratify Prompts by Buyer Intent for an AI Visibility Benchmark?
Stratifying prompts by buyer intent is essential for creating an effective AI visibility benchmark.
Essay
How Should Researchers Control for Prompt Wording When Testing Brand Visibility in AI Answers?
Researchers must manage prompt wording carefully when measuring brand visibility in AI-generated answers.
Essay
What Is the Difference Between Share of Model and Traditional Share of Search Measurement?
Share of Model and traditional Share of Search are two distinct metrics used to gauge brand visibility, but they measure different aspects of performance and operate within different contexts.
Essay
What Sample Size Do I Need to Measure Whether an LLM Reliably Recommends My Brand?
The ideal sample size to gauge whether a large language model (LLM) reliably recommends your brand hinges on the specific decision requirements of your analysis.
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.
