AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last month, I was troubleshooting a Shopify client's traffic drop when I decided to test something unusual. Instead of checking Google rankings, I started asking ChatGPT questions about their industry. What I discovered changed everything I thought I knew about content visibility.
While my client's site ranked #3 on Google for "eco-friendly packaging," ChatGPT had never mentioned them in any response. Meanwhile, a smaller competitor with worse Google rankings was being featured in ChatGPT's answers consistently. This wasn't a coincidence—it was a completely different game with completely different rules.
Most businesses are still fighting yesterday's SEO war while AI assistants are quietly becoming the new front door to information. The uncomfortable truth? Your Google ranking means nothing if ChatGPT doesn't know you exist.
Here's what you'll learn from my experience implementing AI-optimized content strategies:
Why traditional SEO metrics fail in the AI era and what actually matters for LLM visibility
The content structure that ChatGPT rewards vs. what Google's algorithm prefers
My 3-layer optimization system that got a 3,000+ product e-commerce site featured in AI responses
How to track and measure your AI visibility when traditional analytics tools fall short
The surprising channel that drives more qualified traffic than traditional e-commerce SEO tactics
Industry Reality
What every marketer thinks they know about search visibility
The marketing world is obsessed with a playbook that's becoming increasingly irrelevant. Everyone's still playing the same Google SEO game: keyword density, backlink building, domain authority, and technical optimization. The industry wisdom goes like this:
Rank higher on Google = more visibility - The assumption that search engine rankings directly translate to business results
Content for crawlers, not conversations - Writing for algorithms rather than how people actually seek information
Backlinks equal authority - The belief that link building remains the primary trust signal
Technical SEO solves everything - Focusing on site speed and schema markup while ignoring content quality
Keywords drive discovery - Optimizing for specific search terms rather than comprehensive topic coverage
This conventional wisdom exists because it worked. For nearly two decades, Google was the primary gateway to information. SEO agencies built entire business models around gaming Google's algorithm, and it generated real results.
But here's where the industry knowledge falls short: ChatGPT doesn't crawl your website the same way Google does. It doesn't care about your domain authority or how many sites link to you. It processes information in chunks, synthesizes from multiple sources, and makes decisions based on content quality and relevance—not traditional ranking factors.
While everyone's fighting for those Google rankings, a parallel information ecosystem has emerged where completely different rules apply. The businesses that recognize this shift first will have a massive competitive advantage before the rest catch up.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came during a routine SEO audit for an e-commerce client selling eco-friendly packaging. They had solid Google rankings—#3 for their main keyword, decent organic traffic, and all the traditional SEO boxes checked. But their conversion rates were declining, and they couldn't figure out why.
That's when I decided to test something unconventional. I started asking ChatGPT questions that their target customers would typically ask: "What's the most sustainable packaging for small businesses?" "How do I reduce my shipping carbon footprint?" "What packaging materials are actually recyclable?"
The results were eye-opening. Despite my client's strong Google presence, ChatGPT mentioned them exactly zero times across dozens of relevant queries. Instead, it consistently featured their smaller competitors—companies that barely showed up on page one of Google but had somehow become ChatGPT's go-to recommendations.
This discovery hit me like a brick wall. We were optimizing for yesterday's information discovery while our customers were increasingly turning to AI assistants for answers. The traffic wasn't converting because the most qualified prospects never made it to our site—they were getting their answers directly from ChatGPT.
I realized we had a fundamental product-channel fit problem, except the channel wasn't a marketing platform—it was an entirely new way people seek information. Traditional SEO was optimizing for search behavior that was rapidly becoming secondary to conversational AI queries.
The client was skeptical when I proposed shifting resources from traditional link building to what I called "LLM optimization." But they were desperate enough to try anything. That's when I started developing what would become my systematic approach to ranking in ChatGPT responses.
Here's my playbook
What I ended up doing and the results.
Instead of following traditional SEO playbooks, I developed a three-layer system specifically designed for how large language models process and retrieve information. This wasn't about gaming an algorithm—it was about creating content that truly served both human users and AI systems.
Layer 1: Content Architecture for AI Processing
First, I restructured our content approach entirely. Instead of keyword-stuffed articles, we created comprehensive, self-contained content chunks. Each section of content had to be able to stand alone as a complete answer to a specific question. This meant breaking down complex topics into modular pieces that an LLM could easily extract and reference.
For example, instead of a single 3,000-word article about "sustainable packaging," we created detailed sections covering materials science, environmental impact data, cost comparisons, and implementation guides. Each section was factually dense, well-sourced, and provided complete answers without requiring additional context.
Layer 2: Authority Through Comprehensive Coverage
Unlike traditional SEO where you target specific keywords, LLM optimization rewards topical authority—being the most comprehensive source on a subject. We mapped out every conceivable question someone might ask about eco-friendly packaging and created authoritative content addressing each one.
This included technical specifications, industry studies, regulatory information, case studies, and practical implementation guides. The goal wasn't to rank for "eco-friendly packaging"—it was to become the definitive resource that ChatGPT would reference whenever packaging sustainability came up in any context.
Layer 3: Citation-Worthy Accuracy and Attribution
The final layer focused on making our content inherently trustworthy and citation-worthy. This meant rigorous fact-checking, clear data sources, and transparent methodology for any claims we made. We included specific metrics, referenced peer-reviewed studies, and provided exact attribution for all statistics.
I also optimized our content structure for how LLMs cite sources. Instead of burying key information in long paragraphs, we used clear subheadings, bullet points, and data tables that made it easy for an AI system to extract and attribute specific information back to our site.
This systematic approach aligned with my broader philosophy that AI tools work best when you feed them high-quality, structured information rather than trying to manipulate their outputs.
Key Insight
LLMs reward comprehensive coverage over keyword optimization. Build topical authority, not page rankings.
Content Strategy
Structure information in self-contained chunks that can stand alone as complete answers to specific questions.
Measurement Approach
Track mentions across multiple AI platforms, not just ChatGPT. Monitor conversation quality, not just visibility metrics.
Long-term Advantage
Early movers in LLM optimization gain compound advantages as more users shift to conversational search patterns.
Within three months of implementing this LLM-focused approach, the results were dramatic. ChatGPT began featuring my client in responses to sustainability packaging queries—not just mentioning them, but positioning them as a primary recommendation.
More importantly, the quality of traffic improved significantly. Visitors coming from AI-assisted research arrived more educated and closer to purchase decisions. Instead of broad "sustainable packaging" searches, we attracted people asking specific questions like "What packaging works for 2-ounce skincare products with carbon-neutral shipping?"
The conversion impact was immediate: visitors from AI-influenced traffic paths converted 40% better than traditional organic search traffic. These weren't just random browsers—they were qualified prospects who had already been educated about the problems our client solved.
But perhaps the most surprising result was the compound effect. As ChatGPT featured our content more frequently, it created a feedback loop. More mentions led to more brand recognition, which led to more direct searches, which reinforced our authority in the AI training cycle.
Traditional SEO metrics became less relevant while AI visibility metrics became leading indicators of business growth. We tracked mentions, conversation quality, and referral patterns from AI-assisted research rather than just keyword rankings and organic traffic volume.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Content quality beats optimization tricks - LLMs can't be gamed like traditional search algorithms. They respond to genuinely useful, comprehensive information.
Chunk-level thinking changes everything - Structure content so each section provides complete value independently, rather than requiring full-article context.
Citations matter more than backlinks - Being mentioned accurately is more valuable than having inbound links from irrelevant sites.
Topical authority over keyword authority - Become the comprehensive resource on a subject rather than optimizing for specific search terms.
Conversational search behavior is different - People ask AI assistants more specific, contextual questions than they type into search engines.
Early mover advantage is massive - The businesses that optimize for AI visibility now will have compound advantages as adoption accelerates.
Traditional analytics need supplementing - Standard SEO metrics don't capture AI-influenced traffic patterns and conversation quality.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement this approach:
Create comprehensive feature documentation that answers "how to" questions completely
Build use case libraries that cover specific industry applications
Develop integration guides for popular tools in your ecosystem
Focus on problem-solution content rather than product-feature content
For your Ecommerce store
For e-commerce stores implementing LLM optimization:
Create detailed buying guides that cover selection criteria comprehensively
Build product comparison content that includes technical specifications
Develop troubleshooting and usage guides for your product categories
Structure product information for AI-friendly extraction and citation