AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
OK, so here's what happened when I took on a Shopify client with over 3,000 products across 8 languages. The brief was simple: "complete SEO overhaul." But what started as traditional keyword research quickly turned into something completely different when I discovered that LLMs were already mentioning their content organically.
Most ecommerce teams are still stuck in the old SEO playbook - using Ahrefs and SEMrush to chase search volumes that are often completely wrong. Meanwhile, AI systems are fundamentally changing how people search and how content gets discovered. Traditional keyword research tools show you what people typed yesterday, but NLP-friendly keywords help you rank for how people naturally think and speak.
After implementing an AI-native keyword strategy for this client, we went from less than 500 monthly visitors to over 5,000 in just three months. More importantly, we generated over 20,000 indexed pages that actually convert because they match natural language patterns.
Here's what you'll learn from this real implementation:
Why traditional keyword volume data is misleading for ecommerce
How to identify NLP-friendly keywords that AI systems understand
My exact workflow for scaling content across thousands of products
The AI automation system that made 20,000+ pages possible
Why semantic keyword clusters outperform traditional keyword lists
If you're running an ecommerce store and wondering why your blog content isn't driving sales, this case study will show you exactly what's working in 2025.
Industry Reality
What every ecommerce team thinks they know about keywords
Walk into any ecommerce marketing meeting and you'll hear the same advice: "Use Ahrefs to find high-volume, low-competition keywords." Most agencies are still selling the same playbook from 2019 - export keyword lists, check search volumes, target exact match phrases.
Here's what the industry typically recommends for ecommerce keyword research:
Volume-first approach: Chase keywords with 1,000+ monthly searches
Exact match targeting: Create pages around specific product + modifier combinations
Competition analysis: Copy what competitors are ranking for
Long-tail focus: Target "best [product] for [use case]" variations
Manual content creation: Write one blog post per keyword cluster
This conventional wisdom exists because SEO tools make it easy to export thousands of keyword ideas. The data looks impressive in client reports. Everyone's doing it, so it must work, right?
But here's where this approach falls apart: search volume data is notoriously inaccurate, especially for ecommerce. Tools might show zero searches for a keyword that actually drives 100+ visits monthly. More importantly, this strategy completely ignores how AI systems and voice search are changing user behavior.
People don't search like robots anymore. They ask questions naturally: "What's the best way to style a midi dress for winter?" instead of "midi dress winter styling tips." Traditional keyword research misses these conversational patterns that are becoming the norm.
The biggest issue? This old approach treats content like a factory assembly line instead of understanding the semantic relationships that actually drive ecommerce sales.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this Shopify client landed on my desk, they were drowning in their own product catalog. Over 3,000 products with virtually no organic traffic. Their previous agency had created maybe 50 blog posts targeting "obvious" keywords, but nothing was moving the needle.
The client sold fashion and lifestyle products across multiple European markets. Eight different languages. Thousands of SKUs. Their challenge wasn't just SEO - it was scale. How do you create relevant content for thousands of products without hiring an army of writers?
My first instinct was to follow the standard playbook. Fire up SEMrush, export keyword lists, create content calendars. I spent weeks building traditional keyword clusters around their products. "Summer dresses 2025," "sustainable fashion trends," "how to style [product category]" - you know the drill.
But then I noticed something interesting in their analytics. A couple dozen mentions were coming from AI-generated responses - people were asking ChatGPT and Claude about fashion advice, and their products were being mentioned organically. This wasn't something we'd optimized for. It just happened naturally.
That's when I realized we were approaching this completely wrong. Instead of trying to guess what people might search for, I needed to understand how AI systems process and understand fashion and product content. The traditional keyword approach was treating symptoms, not the actual problem.
The real challenge wasn't finding keywords - it was creating content that could be understood and recommended by both search engines and AI systems at scale. We needed an entirely different approach.
Here's my playbook
What I ended up doing and the results.
Instead of fighting the scale problem with more manual work, I built what I call an "AI-native keyword discovery system." Here's exactly how it worked:
Step 1: Semantic Content Mapping
First, I exported all their products and collections into CSV files. But instead of traditional keyword research, I analyzed the semantic relationships between products. How do people naturally talk about these items? What problems do they solve? What emotions do they evoke?
I used Perplexity Pro to research conversational patterns around their product categories. Not search volume data - actual conversations. "How do people describe midi dresses when talking to friends?" "What questions do customers ask about sustainable fabrics?" This gave me the natural language patterns that AI systems understand.
Step 2: NLP-Friendly Keyword Architecture
Instead of keyword lists, I created semantic clusters based on:
Intent patterns ("looking for," "trying to find," "need help with")
Natural modifiers ("versatile," "comfortable," "perfect for")
Conversational phrases ("what goes with," "how to wear," "best option for")
Problem-solution language ("struggling to find," "always wanted," "finally found")
Step 3: AI Content Automation at Scale
Here's where it gets interesting. I built a custom AI workflow that could generate unique, contextually relevant content for each product. Not generic templates - actual valuable content that matched natural language patterns.
The system combined:
Product data and specifications
Industry knowledge base from their archives
Custom brand voice prompts
NLP-optimized content structure
Step 4: Multilingual Semantic Optimization
The real breakthrough was extending this across all 8 languages. Instead of translating keyword lists, I mapped semantic concepts that work across cultures. "Comfort" might translate differently, but the concept of "comfortable clothing for long days" is universal.
Each piece of content was architected to serve multiple discovery methods: traditional search, AI recommendations, voice queries, and visual search. This wasn't about gaming algorithms - it was about creating genuinely useful content that matched how people naturally think and speak about products.
Knowledge Foundation
Building a comprehensive industry knowledge base from their 200+ archived resources instead of generic AI training data
Semantic Architecture
Creating content clusters based on natural language patterns rather than traditional keyword groupings
Multilingual Scaling
Extending NLP optimization across 8 languages using semantic concept mapping instead of direct translation
Automation Workflow
Developing custom AI systems that generate contextually relevant content matching conversational search patterns
The numbers speak for themselves. In three months, we went from under 500 monthly organic visitors to over 5,000. But the real success wasn't just traffic - it was the quality of that traffic.
Traditional keyword-focused content typically converts at 1-2% for ecommerce. Our NLP-optimized content was converting at 4-6% because it matched genuine user intent. People weren't just visiting - they were buying.
We generated over 20,000 indexed pages across all languages. More importantly, Google was ranking these pages for conversational queries we never explicitly targeted. Searches like "comfortable dress for office that doesn't wrinkle" were finding our content organically.
The multilingual performance was particularly impressive. Markets where they previously had zero organic presence were now driving 15-20% of total traffic. The semantic approach transcended direct keyword translation.
But perhaps the most interesting result was the AI mentions. Without actively targeting AI systems, our content was being referenced in ChatGPT, Claude, and Perplexity responses about fashion advice. This "dark traffic" was driving qualified visitors who couldn't be tracked through traditional analytics.
The automation system was handling content updates for new products automatically. What used to require weeks of manual work now happened in hours, maintaining consistency across thousands of pages.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven critical lessons from implementing NLP-friendly keywords at scale:
Search volume data is misleading: Some of our best-performing pages target "zero volume" keywords that actually drive hundreds of monthly visits
Conversational intent beats exact match: "Comfortable dress for work meetings" outperforms "women's office dress" every time
AI systems prefer semantic depth: Content that explains context and relationships ranks better than keyword-stuffed pages
Automation enables consistency: Manual content creation at this scale would have taken years and cost 10x more
Brand voice still matters: AI-generated content must sound authentically human and match your brand personality
Multilingual requires concept mapping: Don't translate keywords - translate the underlying semantic relationships
Quality compounds at scale: Each well-optimized page strengthens the semantic authority of related content
What I'd do differently: Start with semantic research before building the automation. Understanding natural language patterns is more important than perfecting the technical implementation.
When this approach works best: Large product catalogs, conversational product categories, and businesses targeting multiple markets. When it doesn't: Single-product companies or purely technical/B2B products where search behavior is more systematic.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, focus on problem-solution language patterns, feature benefit relationships, and integration use cases rather than product specifications.
For your Ecommerce store
For ecommerce stores, prioritize lifestyle context, product relationships, and natural shopping language over technical product attributes and exact-match modifiers.