The audit covers paid channels, SEO and content, as well as AI assistant answers as a new source of visibility. It ends with a 6-month plan and a measurement system.
Inputs
- Access to Search Console and analytics (read-only)
- Data from advertising accounts
- List of the most important products and customer segments
Process
- 01Paid channelsCampaign structure, acquisition costs, attribution model.
- 02SEOVisibility and technical health, including rendering for bots that do not execute JavaScript.
- 03ContentAssessment of which content adds the company's own expertise and which repeats publicly available information.
- 04AI visibilityA question panel in ChatGPT, Perplexity, Gemini and Claude: is the brand cited as a source?
- 05MeasurementThe Performance report in Search Console (traffic from AI Overviews and AI Mode is included in the “Web” search type), attribution and measures shared with product.
Example application
A B2B company with 50–249 employees is losing traffic from informational queries despite stable rankings. The audit shows that service descriptions are generated in the browser by JavaScript and that blog content repeats publicly available information. The 6-month plan covers server-side rendering of the most important pages, rebuilding the key content on the basis of the company's own data and experience, and a monthly question panel in AI assistants.
An illustrative scenario showing how the method works.
Diagnostic questions
- Which queries lost traffic over the last 12 months despite stable rankings?
- Is the content of the most important pages available in HTML without executing JavaScript?
- For how many customer questions is the brand cited as a source in AI answers?
Which established models is the method based on?
The Growth and Visibility Audit combines elements of proven models with implementation practice. We name the source models together with their authors; the description of the method is Lemur Media's own work.
| Model | Author and year | What we use |
|---|---|---|
| Bullseye | Gabriel Weinberg, Justin Mares, “Traction”, 2014 | Testing and selecting acquisition channels |
| E-E-A-T | Google, Search Quality Rater Guidelines (2022 update) | Content quality criteria |
| Documentation on AI features in Search | Google Search Central | Requirements for appearing in AI Overviews and AI Mode |
Deliverables
- Prioritised report
- 6-month action plan
- Question panel for monthly measurement
Limitations of the method
Measuring visibility in AI assistants is sample-based: answers vary between sessions and model versions.