The revenue data across five blogs was telling me something I did not want to hear. Nine months in, total monthly earnings were sitting at $0.05 across all five sites. Google Search Console showed 31 total impressions and zero clicks. Multiple posts were stuck at “Discovered, currently not indexed” across every domain. But buried inside the analytics was a detail that reframed the whole picture: Australian visitors were generating significantly higher RPM than local Philippines traffic. The geographic spread of who was actually finding the content, and what that traffic was worth, was the real signal.
That discovery led me to try something I was not entirely sure would work: systematic geographic targeting through LLM optimization, bypassing Google’s algorithm entirely and building for the crawlers that were already showing up in my server logs.
The Problem With Chasing Google
The GSC data across the network made the Google problem concrete. On QAJourney, the Philippines was generating 2 clicks and 98 impressions at a 2% CTR from position 5. The US had 275 impressions, zero clicks, and a position of 58. Countries like South Korea, Morocco, Iraq, Mexico, and Saudi Arabia were sitting at position 1 with a single impression each. Perfect positioning in markets I was not reaching, and terrible positioning in the market I was trying to reach.
EngineeredAI showed the same pattern. The US had 122 impressions and zero clicks at position 20. Brazil, Ecuador, Poland, and Sweden were sitting at position 1 with minimal impressions. RemoteWorkHaven had 151 US impressions at position 38, and Haiti had a single impression with a 100% CTR at position 1. Across 40 plus countries there was real international search demand, terrible Google positioning, and zero conversion.
While Google was doing this, LLM crawlers were not. GPTBot, ClaudeBot, and Perplexity were crawling the network consistently and without the geographic and domain-age bias that was suppressing Google performance. That gap was the strategic opening. Instead of continuing to optimize for an algorithm that was actively working against AI-adjacent content, I could build for systems that evaluated content on structure and relevance instead.
What I Tried and What I Was Thinking at the Time
I want to be honest about where I was when I built this. I had a hypothesis and some server log data, but I was figuring out the implementation as I went. The idea was that if LLMs do not penalize based on domain age or geographic origin, and if I could signal clearly which markets my content was relevant to, I might be able to capture international traffic that Google was not serving.
The implementation strategy I landed on had four components. Geographic schema targeting countries that were already showing real search demand in GSC. Enhanced LLM crawling optimization across all blogs using the same manual schema approach I had already built on EAI. Systematic content syndication to AI-accessible platforms. Cross-mesh authority building between related sites to strengthen the network signal.
For the schema piece, I moved away from LocalBusiness as the primary type — which was the wrong semantic choice for content sites — and used WebSite with areaServed to signal geographic relevance without misrepresenting what the sites actually are. A blog is not a local business. The schema should reflect that.
The implementation for each site used areaServed with a rotating array of target countries based on where real demand signals existed in the data:
php
$service_areas = [
"United States", "United Kingdom", "Canada", "Germany", "France",
"India", "Brazil", "Vietnam", "Poland", "Slovakia",
"Australia", "Spain", "Norway", "Sweden"
];
$schema = [
"@context" => "https://schema.org",
"@type" => "WebSite",
"name" => "QA Journey - Software Testing Methodology",
"description" => "Practical QA methodologies for software teams worldwide",
"url" => "https://qajourney.net",
"areaServed" => array_map(function($country) {
return ["@type" => "Country", "name" => $country];
}, $service_areas)
];The country list was not arbitrary. It came directly from GSC data showing where impressions existed even at low volume, combined with RPM data showing which markets were worth prioritizing. Australia showing up with higher RPM than Philippines traffic was the first signal. The GSC position data across high-income countries at position 1 with minimal impressions showed there was organic demand that just was not getting surfaced.
Blog-Specific Targeting Logic
Each site in the network got a targeting strategy matched to its content and the international audience most likely to find it relevant.
QAJourney targeted software testing methodology for international development teams, with heavy weight on the US, UK, India, Germany, and Vietnam where software development is concentrated and QA methodology content has clear professional utility. The GSC data already showed position 1 in several of these markets with near-zero impressions, which meant the content was ranking but not being surfaced at volume.
EngineeredAI targeted AI content strategy for global tech markets, with the US, UK, Canada, and Australia as primary targets given the higher RPM and the concentration of AI practitioners in those markets. The full breakdown of what I found on EAI specifically is in the LLM optimization guide.
RemoteWorkHaven targeted remote work systems for distributed teams worldwide, with broad geographic coverage reflecting that remote work is by definition a global topic. The GSC data showed international interest across more than 40 countries, which made the targeting strategy wider than the other sites.
MomentumPath and HealthyForge used similar logic applied to productivity and wellness content respectively, with targeting weighted toward English-speaking markets and high-RPM regions where that content category has the most monetization potential.
What GPT-5 Said When I Ran This Past It
I want to include this because it is an honest part of the story. When I described this approach to GPT-5 to pressure-test the logic, it pushed back. The argument was that geographic schema on content sites was not a standard use case and might not produce the intended signal. That is a fair critique and worth including here because I do not want this post to read as a confident endorsement of something I was genuinely uncertain about.
My counter was that the alternative was continuing to get zero clicks from Google while LLM crawlers were already finding the content. Running an experiment with geographic schema cost nothing and the downside risk was minimal. The data I had already justified the test even if the outcome was uncertain.
The honest answer to whether it worked definitively is that attribution is difficult. LLM traffic increased across the network in the period after implementation, but isolating geographic schema as the variable responsible for that is not something I can do cleanly. What I can say is that the pattern of LLM traffic outperforming Google organic held and strengthened, and the international spread of that traffic matched the targeting I had built.
The Cross-Network Effect
One thing that became clearer through this process is that the five sites function better as a network than as isolated properties. When LLM crawlers follow internal links across the mesh, the geographic and topical signals compound. A crawl that starts on QAJourney and follows links to EngineeredAI and RemoteWorkHaven builds a richer picture of the network’s topical authority than any single site would produce alone.
The cross-network link structure I documented separately is part of the same system. Geographic schema is one signal. The internal mesh is another. The GitHub Gist mirrors and syndication channels are a third. None of them work as well in isolation as they do together.
What I was building, without fully naming it at the time, was a content infrastructure designed to be legible to retrieval systems rather than optimized for a single search engine’s ranking algorithm. Geographic targeting through LLM optimization was one layer of that, not the whole thing.
