From Impressions to Influence: Attributing Pipeline to AI Citations
TL;DR Every attribution model in commercial use — last touch, first touch, linear, time decay, Shapley, Markov, even marketing mix...
Benchmarking Citation Share vs Competitors: A Reproducible Method
TL;DR • Every control in the standard 2026 AI-visibility audit — incognito, signed out, cookies cleared, run it twice —...
Share of Model: Measuring Category Dominance Across Five Engines
TL;DR Share of model borrows its credibility from share of voice and share of search. Both of those had a...
Variance, Drift and Noise: Why Your AI Visibility Score Moves
TL;DR Your score moves for four reasons and only one of them is you. On the 2026 evidence, the three...
Prompt-Space Sampling: Designing a Statistically Valid Citation Audit
TL;DR Every AI visibility audit carries two errors. One is sampling error, which more runs genuinely fix. The other is...
Visibility Is a Distribution, Not a Number: Sampling AI Answers Properly
TL;DR The field agreed on the right sentence and drew the wrong conclusion. The standard response to "AI visibility is...
Standardising AI Visibility Metrics: Toward a 2027 Industry Benchmark
TL;DR On 3 August 2026 the IAB published Measuring Visibility in the AI Era, the first industry framework for AI...
Building an Experience-Led Content Engine That AI Rewards
TL;DR The content-operations canon — calendars, briefs, freelance benches, velocity targets — is a set of devices for protecting one...
Original Interviews and Primary Sources: The 2027 Link-Earning Format
TL;DR An interview is not a research method. It is a publication event: the moment a source with no URL...
Lived Proof: Why Reddit-Style Corroboration Beats Polished Pages
TL;DR Reddit supplies somewhere between 0.1% and 46.7% of an answer engine's citations depending entirely on which engine you ask....