GEO Tactics:
Actionable Frameworks from Every Episode

If no: rewrite the opening paragraph to lead with the answer, not the context.

If keyword-only: add a clear question-and-answer section targeting the use-case intent.

If just invisible: open it. If it reveals proprietary methodology: consider blocking.

Traffic-primary pages: optimise for AI extraction. Conversion-primary: protect and drive clicks.

If yes: open to AI crawlers and measure revenue per visitor, not just traffic.

In Google Search Console, filter by your brand name. What percentage of branded queries result in a click to your site without paid ads? If you're already capturing 90%+ organically, bidding on your brand name is mostly a tax.

Search your brand name in Google. Are competitor ads appearing above your organic result? If yes, you must bid defensively — the cost of not bidding is losing clicks to competitors. If no, evaluate whether bidding adds incremental value.

Create separate campaigns for branded and non-branded keywords. This gives you clean ROAS data for each segment and lets you set different bid strategies. Most accounts mix them, which hides the true cost of each.

Take your monthly branded ad spend. Subtract the incremental clicks you receive beyond what you'd get organically (use the 'auction insights' report to estimate). The remainder is your brand tax — money paid to Google for traffic you'd have received anyway.

In a low-competition period, pause branded keyword bidding for 2 weeks. Measure the change in total branded traffic (organic + paid combined). If total traffic drops less than 10%, your branded ads are delivering minimal incremental value.

Any budget freed from unnecessary branded bidding should be reinvested in original research and PR outreach — the inputs to AI citation. A single cited study can deliver branded visibility across ChatGPT, Perplexity, and Google AI Mode simultaneously.

Publish original data, a survey, or a benchmark study. AI models cite primary research far more than opinion pieces. Even a small-n study (50–100 respondents) with a clear finding is citable.

Find journalists who cover your category in publications that AI models cite (TechCrunch, Forbes, industry verticals). Use their recent bylines to understand what angles they cover.

Lead with the most counterintuitive finding from your research. Journalists and AI models both prefer surprising, specific numbers over general claims. Include the methodology in your pitch.

When coverage lands, link to it prominently from your own site. Add the journalist's quote to your homepage or relevant product page. This creates a citation loop that AI models follow.

Re-run your ARR audit. Check if your brand now appears in AI responses that cite the publication that covered you. Track which platforms picked up the coverage.

If yes: inject original data, proprietary findings, or first-hand experience that cannot be found elsewhere. Generic how-to guides are the most vulnerable.

If no: commission a survey, run an experiment, or publish internal benchmarks. Even a 50-respondent study with a clear finding is citable.

If no: add an author bio with specific credentials, case study experience, and links to corroborating work. Anonymous content is increasingly invisible to AI.

If no: develop a named framework for your approach and publish it with a clear methodology section. Named frameworks are cited by name by AI models.

If no: it lacks the external validation signal AI models use to distinguish authoritative from generic content. Prioritise PR outreach for this page.

Run your 20 most important category queries in ChatGPT, Claude, and Perplexity. Document every source cited. These are the publications and brands that AI models have already concentrated on in your space. Your goal is to appear in or alongside them.

Of the 10–20 sources identified, choose the top 3 by citation frequency. Invest in getting your brand mentioned in those sources specifically — through PR, contributed research, or expert commentary. One mention in a heavily-cited source is worth 100 mentions in uncited sources.

The brands AI concentrates on are typically the ones that defined the category's data vocabulary. Publish a benchmark, survey, or study that establishes the metrics your category is measured by. When journalists write about your category, they'll cite your numbers.

Concentration means being the answer AI gives for one specific, high-intent query in your category. Identify the single most valuable question a buyer in your category asks, and make your brand the unambiguous answer to that question across all AI platforms.

Run your top 20 category queries monthly in ChatGPT, Claude, and Perplexity. Track what percentage of responses mention your brand. This is your concentration score. A rising score means you're winning the consolidation. A falling score means you're being displaced.

Move all marketing content (copy, briefs, campaign specs, email sequences) into a GitHub repository. Markdown files, YAML configs, and JSON data structures. Version control gives you a full history of every change and makes AI agent collaboration auditable.

Create a GitHub Issue for every content brief. Assign it to an AI agent (Claude Code, Codex) via a PR. The agent drafts the content, you review the diff. This makes every AI contribution reviewable, commentable, and reversible — exactly like a code review.

Assign AI agents to: first drafts of templated content, SEO metadata generation, A/B variant creation, image alt text, social copy from long-form articles, and email subject line testing. Reserve human judgment for strategy, brand voice, and final approval.

Set up GitHub Actions to run automated checks on every PR: broken links, SEO schema validation, readability scores, brand voice consistency (via an AI reviewer), and plagiarism detection. Catch issues before they reach production.

Connect your GitHub repo to your CMS or deployment pipeline. After publishing, feed performance data (CTR, conversion, engagement) back into the repo as structured data. AI agents can then use historical performance to improve future drafts autonomously.

e.g. 'What is the best CRM for a 10-person sales team?'

e.g. 'CRM comparison for small teams', 'CRM pricing under $50/user', 'CRM integrations with Slack', 'CRM onboarding time', 'CRM reviews 2026'

A page ranking #47 for 'CRM onboarding time' can be cited in the AI Overview for 'best CRM for small teams' — even if it doesn't rank for the primary query at all.

The final AI Overview cites sources from across all sub-queries, giving 62% of citations to content that traditional SEO would consider invisible.

For each of your top 20 target queries, list the 5–10 supporting questions a user might ask. These are your fan-out sub-query targets.

Which sub-topics do you already have dedicated pages for? Which are missing? Missing sub-topics are citation gaps — pages that could be cited but don't exist yet.

Each sub-topic should have its own page with authoritative, structured content. A single paragraph buried in a long-form guide is not sufficient — agents need a dedicated, parseable source.

Sub-topic pages are now AI Overview citation candidates. Add FAQ schema, clear H2/H3 structure, and a TL;DR summary at the top. Make them easy for Gemini 3 to parse and cite.

Grok 4.20 now achieves the lowest hallucination rate ever recorded and is replacing Perplexity as the top AI search recommendation. Monitor your citation presence in Grok alongside ChatGPT, Claude, and Gemini.

Run your top 20 queries in ChatGPT, Claude, Perplexity, and Grok. Track which sub-topic pages are being cited and which are not. Fan-out citation patterns shift as models update — monthly monitoring is essential.