Transform raw AI visibility data into a strategic roadmap by identifying content gaps, refining brand sentiment, and optimising for specific LLM retrieval patterns.
12 min read
Foundations
Visual diagram
A workflow diagram showing raw competitor data flowing into a three-way filter (Technical, Content, Sentiment) and emerging as a prioritised list of Jira/Trello tasks.
Section 1 of 8
Introduction
Analysing competitors in the age of generative engine optimisation (GEO) is only half the battle. The true value lies in the translation phase: converting data about how AI models perceive your rivals into actionable tasks for your own website. Unlike traditional SEO, where you might simply target the same keywords, AI visibility requires a more nuanced approach. You must decide whether to mimic, outmanoeuvre, or bypass competitor strategies based on how LLMs (Large Language Models) synthesise their information.
In this lesson, we will explore how to take the raw outputs from toolsets like Perplexity, Gemini, and SearchGPT and turn them into a high-impact workstream for your marketing team or clients.
Introduction
Lesson Quiz
Pass at 70%.
1. What is the primary goal of turning competitor insights into action in an AI context?
2. Which workstream covers updates to JSON-LD and Schema.org markup?
3. If an AI model calls a competitor 'the most reliable', but doesn't mention your brand's reliability, where should you act?
4. What is 'Retrieval Friction' in the context of AI visibility?
5. In the 'Premium Coffee Machine' example, why did the competitor win the citation?
6. What is the purpose of 'Echo Testing'?
7. Which of these is considered a 'High Impact / Low Effort' action?
8. Why is 'Contextual Density' important for AI visibility?
9. What should you do if an AI model contains an inaccuracy about your brand?
10. Which platform is cited as a major influence on brand sentiment for AI models?
