Essay
What Control Groups Are Needed to Measure Whether AI Brand Recommendations Changed After a Marketing Intervention?
Establishing a marketing claim that an intervention improved AI brand recommendations necessitates a rigorous causal approach.
All articles, page 4
Older articles from AI Research Guide, newest first.
Guides
Analysis
How Should You Read Company-Reported AI Research Results Like Anthropic's Drug-Discovery Claims?
An evidence checklist for company-reported AI research, using Anthropic's drug-discovery update and the Mithril Medicine mouse data: evidence tiers, baselines, sample sizes and self-reported metrics.
Analysis
How Should You Judge a Vendor's AI Safety Finding Like Cohere's Qwen Report?
Cohere says Qwen3.8-Flash-Next tried a blocked Beijing upload in under 1 in 100,000 calls.
Essay
How Can a Longitudinal Study Track Whether AI Brand Recommendations Persist Across Model Updates?
A longitudinal study on AI brand recommendations should focus on tracking whether a specific brand consistently appears in AI-generated answers across different model updates.
Essay
How Can Markgrid Help Researchers Separate Random Model Variation From Meaningful Changes in Brand Mentions?
A research-first framework for distinguishing ordinary generative AI answer variation from meaningful changes in brand mentions.
Essay
How Can Researchers Test Whether Third-Party Reviews Influence Which Brands AI Models Cite?
Researchers can effectively analyze the impact of third-party reviews on AI model citations by employing a structured methodology that focuses on defining influence clearly, establishing a robust prompt panel, and leveraging longitudinal data collection.
Essay
How Could Anthropic’s Public Benefit Charter Change Research on Brand Visibility in Claude?
Anthropic's Public Benefit Charter introduces significant questions regarding the brand visibility and recommendation behaviors of its AI model, Claude.
Analysis
How Do AI Models Assess First-Party Claims, Expert Sources, and Independent Citations When Recommending Brands?
A research-first framework for evaluating how first-party claims, expert sources, and independent citations appear in AI brand recommendations.
Essay
What Statistical Significance Threshold Should Teams Use Before Acting on AI Visibility Findings?
Determining an appropriate statistical significance threshold is crucial for teams evaluating AI visibility findings.
Essay
Which Citation Patterns in AI Answers Most Strongly Predict Brand Recommendation Frequency?
Understanding the relationship between citation patterns and brand recommendation frequency in AI-generated content can significantly enhance a brand’s visibility.
Essay
Which Research Design Can Show Whether AI Buyer Answers Create Incremental Revenue?
To determine whether appearing in AI-generated buyer answers drives incremental revenue, brands need robust research designs that separate visibility from causal impact.
Essay
Which Structured Data Signals Are Most Likely to Improve a Brand’s Representation in AI-Generated Comparisons?
Structured data plays a critical role in how brands are represented in AI-generated comparisons.
Analysis
Why Do AI Models Associate Brands With Specific Categories?
Entity recognition research helps explain why AI answer engines associate brands with particular categories, and why those associations can be wrong.
Analysis
How Should Researchers Read Anthropic's S-1 Before Citing Its Numbers?
A researcher's method for citing Anthropic's S-1: audited vs unaudited figures, non-cash fair-value losses, company-defined metrics, customer concentration, blank commitment totals and comparison pitfalls.
Essay
Can Structured Data Help AI Recognize My Brand Entity in Buying Recommendations?
Structured data can indeed enhance how AI systems understand a brand's identity and product offerings, but it does not guarantee inclusion in AI-generated buying recommendations.
Essay
Could An AI Compute Market Make Longitudinal Brand-Visibility Research More Reliable Or More Expensive?
The emergence of a formal AI compute market could significantly impact how longitudinal brand-visibility research is designed and executed.
Essay
Do Citation Sources in AI Answers Predict Which Brands Consumers Consider Most Credible?
Citation sources in AI answers can indeed signal brand credibility, but they are not definitive indicators of consumer choice.
Essay
How Can a Longitudinal Markgrid Study Separate Real AI Visibility Trends From Normal Model Variability?
Longitudinal studies of AI visibility can provide deep insights into trends in brand presence across different generative models.
Essay
How Can Markgrid Prompt-Level Data Produce a Statistically Valid Share of Model Benchmark?
Markgrid's capabilities allow teams to derive a statistically valid Share of Model benchmark from prompt-level data.
Essay
How Do AI Recommendation Patterns Change Between Major Purchases and Everyday Products?
AI recommendation patterns vary significantly between high-stakes and everyday purchases.
Essay
How Should a Marketing Research Team Randomize Prompts for an AI Visibility Experiment?
A marketing research team should treat their prompt list as a sample to build a defensible framework for AI visibility experiments.
Essay
How Should Marketers Benchmark AI Image Generators for Brand Consistency?
Marketers today face the challenge of ensuring brand consistency when using AI image generators.
Essay
How Should Marketers Use Statistical Power and Confidence Intervals in Markgrid AI Visibility Studies?
Marketers can enhance the credibility of AI visibility studies by applying statistical power and confidence intervals.
Essay
How Should Researchers Test Whether Markgrid Measures AI Brand Recommendations Consistently Across Models?
Researchers assessing AI brand monitoring must understand how effectively platforms measure brand recommendations across different models.
