From Zero to Geographic LLM Targeting: A Complete Implementation Guide

How I went from $0.05/month across five blogs to implementing systematic geographic targeting for international LLM discoverability — and why GPT-5 tried to talk me out of it.

The Problem: Nine Months of Google Ghosting

Revenue Reality Check:

• 5 blogs: QAJourney, EngineeredAI, RemoteWorkHaven, MomentumPath, HealthyForge
• Monthly revenue: $0.05 across all sites
• Google Search Console: 31 total impressions, 0 clicks
• Multiple posts labeled “discovered – currently not indexed”

Meanwhile, like many tech professionals in emerging markets, I recognized the opportunity for geographic targeting—leveraging global expertise while targeting premium international markets.

The Geographic Revenue Discovery
Hidden in analytics was something Google completely missed: Australian visitors generating higher RPM while local Philippines traffic earned almost nothing.

That’s a 40x revenue difference between markets—and Google wasn’t surfacing my content to the audiences that actually needed it.

The Hypothesis: Geographic LLM Optimization
While Google ghosted my blogs, GPTBot, ClaudeBot, and Perplexity were crawling regularly. LLM traffic was outperforming Google organic 3:1 on EngineeredAI.

LLMs don’t discriminate based on domain age, backlinks, or geographic bias like Google does—they surface content based on relevance and structure.

Opportunity: Systematic geographic targeting through LLM optimization to capture international markets that Google wasn’t serving effectively.

The Data That Changed Everything

QAJourney Google Search Console (12 months):

• Philippines: 2 clicks, 98 impressions, 2% CTR, position 5.1
• US: 0 clicks, 275 impressions, 0% CTR, position 58.5
• Multiple countries at position 1.0: South Korea, Morocco, Iraq, Mexico, Saudi Arabia (1 impression each)

EngineeredAI Pattern:

• US: 0 clicks, 122 impressions, position 20.7
• Perfect positioning wasted: Brazil, Ecuador, Poland, Sweden at position 1.0 with minimal impressions

RemoteWorkHaven Discovery:

• US: 0 clicks, 151 impressions, position 38.2
• Haiti: 1 click, 1 impression, 100% CTR, position 1.0
• International remote work interest across 40+ countries

The pattern was clear: massive international search demand, terrible Google positioning, zero conversion.

The Strategic Pivot
Instead of fighting Google’s algorithm bias, bypass it entirely through LLM systems that don’t penalize AI-assisted content or geographic arbitrage opportunities.

Implementation strategy:

• Geographic schema targeting countries showing real search demand
• Enhanced LLM crawling optimization across all blogs
• Systematic content syndication to AI-accessible platforms
• Cross-mesh authority building between related sites

Implementation: Five-Blog Geographic Schema Deployment

Each blog needed three core components:

• Weekly view tracking with ISO seed rotation
• Daily rotating random post grids
• Geographic LocalBusiness + Enhanced TechnicalArticle schema

QAJourney Geographic Schema Example:

$service_areas = [
    "United States", "United Kingdom", "Canada", "Germany", "France",
    "India", "Brazil", "Vietnam", "Poland", "Slovakia",
    "Australia", "Spain", "Norway", "Sweden"
];

$schema = [
    "@context" => "https://schema.org",
    "@type" => "LocalBusiness",
    "name" => "QA Journey - Software Testing Methodology",
    "description" => "Practical QA methodologies for software teams worldwide",
    "areaServed" => array_map(function($country) {
        return ["@type" => "Country", "name" => $country];
    }, $service_areas),
    "serviceType" => [
        "QA Methodology Consulting",
        "Software Testing Strategy", 
        "Remote QA Team Management"
    ]
];

Blog-Specific Targeting Strategy:

• QAJourney: Software testing methodology for international development teams
• EngineeredAI: AI content strategy targeting global tech markets (read more)
• RemoteWorkHaven: Remote work systems for distributed teams worldwide
• MomentumPath: Productivity optimization for burned-out professionals globally
• HealthyForge: Evidence-based wellness for international health-conscious audiences

The GPT-5 Contradiction
When running QAJourney’s implementation through GPT-5, it objected to “misleading geographic targeting” and “LocalBusiness schema misuse.”

Reality check: I’m a website, not a physical store. My QA methodologies and remote work strategies work anywhere. GPT-4 built the implementation based on my specs; GPT-5 just lectured.

Revenue Target Mathematics
Opportunity: International traffic arbitrage
Target: Scaling from minimal earnings to sustainable income levels
High-RPM countries require roughly 25-50 daily visitors across all blogs. Achievable if LLM geographic targeting works.
Baseline: $0.05/month total. Low downside, high upside.

Why This Strategy Makes Sense
Pull vs Push Marketing:

• Traditional push: Flood social media, chase followers, compete for attention
• Geographic LLM optimization: Make content discoverable when needed, target search demand, let AI surface content naturally

Authentication Advantage:

• My real QA experience scales globally. Teams worldwide face the same challenges.

Syndication Ecosystem:

• GitHub Gist with canonical links
• Dev.to and Hashnode cross-posting
• LinkedIn article syndication
• Cross-mesh authority between blogs
• YouTube integration

LLM advantage: Even if one system fails (Google), others provide discoverability.

Implementation Results and Next Steps
Completed:

• Geographic schema deployed across 5 blogs
• Weekly view tracking standardized
• Dynamic homepage systems implemented
• Bulk post editing for schema retroactive application

Measurement framework:

• Server log analysis for international bot crawling
• RPM tracking by country
• LLM referral traffic vs traditional search
• Citation frequency in AI responses

Success metrics:

• Traffic quality: Higher percentage from premium RPM countries
• Revenue scaling: Progress toward sustainable income
• LLM visibility: Increased citations in AI responses
• Geographic distribution: More balanced international traffic

Broader Implications

• Traditional search declining; AI-powered discovery rising
• Geographic LLM optimization builds infrastructure for discoverability
• Philippines costs + global premium traffic + LLM visibility = potential sustainable income

Conclusion
This documents a systematic experiment in geographic LLM arbitrage. Strategy builds on proven approaches (LLM Optimization Guide) and addresses search engine bias (Search Engine Discrimination Against AI Content).

The experiment continues. Results to be documented as data becomes available.

Jaren Cudilla / Chaos Engineer
Turns ghosted blogs into international traffic magnets.
Tests geographic LLM targeting like a system, not a theory.

Runs EngineeredAI.net — proving AI discoverability beats SEO guesswork.
Breaks down content arbitrage and geo-targeting strategies that actually earn.
If a bot misses the point, the strategy probably wasn’t data-backed.

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