AEO testing best practises
AEO only drives revenue when you treat it like a product experiment: run proper control-vs-test at scale, then repeat to prove causality. Measure deltas in Share of Model, assistant-referred traffic, and sell-through to separate signal from noise and turn wins into a repeatable playbook.
Published: 2025-11-01 | By Stephen Honight, Founder of The Lmo7 Agency
About This Research
This Search Lab case study is part of ongoing research by The Lmo7 Agency into how large language models (LLMs) like ChatGPT, Google Gemini, Claude, and Perplexity handle product discovery, brand mentions, and commercial recommendations. Our research helps consumer brands understand and improve their visibility in AI-driven search.
AEO only drives revenue when you treat it like a product experiment: run proper control-vs-test at scale, then repeat to prove causality. Measure deltas in Share of Model, assistant-referred traffic, and sell-through to separate signal from noise and turn wins into a repeatable playbook.
Why AI Search Visibility Matters
As consumers increasingly use AI assistants to discover and compare products, brand visibility within LLM responses is becoming a critical growth lever. The Lmo7 Agency specialises in helping brands optimise their presence across AI search platforms including ChatGPT, Google Gemini, Amazon Rufus, and Perplexity.
Learn more about our approach in our LLM Visibility Framework or explore our AI search optimisation services.