AI & Automation
Personas
Ecommerce
Time to ROI
Medium-term (3-6 months)
Last month, I was working on an SEO project for a B2C Shopify store that needed a complete keyword strategy overhaul. The client's budget was tight, and they were already spending hundreds monthly on SEMrush and Ahrefs subscriptions.
After hours of clicking through expensive tool interfaces and drowning in overwhelming data exports, I had a decent list. But something felt off. The process was expensive, time-consuming, and honestly? Overkill for what we needed.
That's when I discovered something that completely changed how I approach keyword research for Shopify collections. Instead of traditional SEO tools, I built an entire keyword strategy using AI research capabilities - and the results were shocking.
The same client went from 500 monthly visitors to over 5,000 in just 3 months, and we did it with a fraction of the tool costs everyone else was paying.
In this playbook, you'll learn:
Why traditional SEO tools often fail for collection-level research
My exact AI-powered keyword research workflow
How to map keywords to product collections effectively
The specific prompts that generated 10x better results
How to validate keyword opportunities without expensive subscriptions
If you're tired of paying for multiple SEO tools that give you more confusion than clarity, this approach will change everything. Check out more ecommerce growth strategies in our playbook collection.
Industry Reality
What every SEO consultant recommends
Walk into any SEO agency and they'll tell you the same story: you need SEMrush for competitor analysis, Ahrefs for backlink research, and Google Keyword Planner for search volumes. The "professional" approach looks like this:
Start with expensive tool subscriptions - Usually $200-500+ monthly for decent access
Export thousands of keyword suggestions - Most of which are irrelevant to your specific collections
Manually filter through overwhelming data - Spending hours sorting wheat from chaff
Cross-reference multiple tools - Because no single tool gives you the complete picture
Build keyword clusters manually - Grouping related terms for each collection page
This conventional wisdom exists because SEO agencies need to justify their retainers. Complex tools and processes make the work seem more valuable and specialized.
But here's where this approach falls short in practice: most Shopify store owners don't need enterprise-level keyword research. They need focused, actionable insights for their specific product collections. When you're managing 50-200 collections (not 50,000 pages), the traditional approach is like using a sledgehammer to crack a nut.
The real problem? Tools give you data, not insights. You still need human intelligence to understand search intent, map keywords to collections, and prioritize opportunities. That's where most people get stuck - drowning in data but starving for strategy.
I realized there had to be a better way to combine the research depth I needed with the strategic thinking that actually drives results.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The breakthrough came when I was working with a B2C Shopify client who had a massive challenge: over 3,000 products across 200+ collections, virtually no organic traffic, and a tight budget that couldn't support multiple SEO tool subscriptions.
My first approach was textbook SEO consulting. I fired up SEMrush, dove into Ahrefs, and started the usual keyword research dance. After hours of work, I had a spreadsheet with thousands of potential keywords, but something felt fundamentally wrong.
The process was expensive - Multiple tool subscriptions were eating into the project budget. Time-consuming - Endless manual filtering and cross-referencing. Overkill - Most of the data was irrelevant to their specific niche and collection structure.
Worse yet, the keyword volumes these tools showed were often completely wrong. They'd show "0 searches" for terms that were actually driving 100+ visits monthly to competitor sites. The disconnect between tool data and real-world results was massive.
That's when I remembered something: I had a dormant Perplexity Pro account sitting unused. On a whim, I decided to test their research capabilities for keyword work.
The difference was immediate and shocking. Instead of just spitting out generic keyword lists, Perplexity understood context, search intent, and competitive landscape in ways that traditional tools couldn't match.
Within a few hours, I had built a more comprehensive, strategic keyword list than I'd ever achieved with traditional tools. The insights weren't just broader - they were smarter and more actionable.
Here's my playbook
What I ended up doing and the results.
Here's the exact AI-powered workflow I developed that replaced my entire SEO tool stack:
Phase 1: Collection Architecture Analysis
Instead of starting with keywords, I began by understanding the client's collection structure. Using Perplexity's research mode, I analyzed:
How competitors organized their product categories
What collection-level search terms were driving traffic in their industry
Which product groupings had the highest commercial intent
Phase 2: Intent-Based Keyword Research
This is where the magic happened. I developed specific research prompts for Perplexity that uncovered:
Commercial Collection Keywords: "Research the most valuable product category search terms for [industry] with high buyer intent. Include search volume estimates and competitive difficulty."
Long-tail Collection Opportunities: "Find specific product collection search phrases that [competitor sites] are ranking for but have medium competition levels."
Seasonal and Trending Collections: "Analyze trending search patterns for [product type] collections over the past 12 months, including seasonal variations."
Phase 3: Competitive Gap Analysis
Using AI research, I identified collection pages where competitors were weak. This wasn't about copying their strategy - it was about finding undefended territory in the search landscape.
Phase 4: Search Intent Mapping
The most powerful part of this approach was mapping search intent to collection pages. AI helped me understand:
Which keywords indicated browsing vs. buying intent
How to structure collection page content for different search stages
What supporting content each collection needed to rank
Phase 5: Content Architecture
Finally, I used the research to create a content plan that aligned perfectly with how people actually searched for products in this niche. This included optimizing existing collection pages and creating new ones for high-opportunity keywords we'd discovered.
The entire process took 3 days instead of 3 weeks, cost a fraction of traditional tool subscriptions, and delivered more strategic insights than I'd ever achieved with conventional methods.
Strategic Research
Perplexity's research capabilities revealed search opportunities that traditional tools completely missed
Intent Mapping
AI helped decode the difference between browsing and buying keywords for each collection
Competitive Gaps
Found undefended collection categories where competitors had weak or missing pages
Cost Efficiency
Replaced $400+ monthly tool costs with a $20 AI subscription that delivered better insights
The results spoke for themselves. Within 3 months of implementing this AI-driven keyword strategy:
Organic traffic increased from 500 to 5,000+ monthly visits
20,000+ collection and product pages indexed by Google
Significant improvement in collection page rankings for target keywords
90% reduction in keyword research costs compared to traditional tool approaches
But the real win wasn't just the numbers. The quality of insights was dramatically better. Instead of drowning in irrelevant keyword suggestions, we had a focused, strategic approach that mapped directly to the client's business goals.
The AI research revealed collection opportunities that traditional tools had completely missed, including several high-value, low-competition keyword clusters that became major traffic drivers.
Most importantly, this approach was sustainable. Instead of constantly paying for multiple tool subscriptions, the client could maintain and expand their keyword strategy with minimal ongoing costs.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience completely changed how I approach keyword research for Shopify collections. Here are the key lessons I learned:
Tools give you data, AI gives you insights - Traditional SEO tools excel at providing raw data, but AI research platforms understand context and intent in ways that transform that data into actionable strategy.
Collection-level research needs collection-level thinking - Most keyword tools are built for individual page optimization, not the hierarchical structure of ecommerce collections.
Search volume numbers are often wrong - Don't trust volume estimates blindly. Real-world results often differ dramatically from tool predictions.
Intent mapping is more valuable than keyword volume - Understanding why people search matters more than how many people search.
Competitive gaps are goldmines - AI research excels at finding opportunities that competitors have overlooked.
Cost doesn't equal quality - Expensive tools aren't automatically better. Sometimes a different approach delivers superior results at a fraction of the cost.
Speed matters for competitiveness - The faster you can identify and act on keyword opportunities, the better your chances of capturing market share.
If I were starting this project today, I'd rely even more heavily on AI research and spend less time with traditional tools. The insights are simply better, and the cost efficiency is undeniable.
This approach works best for stores with clear product categories and defined target audiences. It's less effective for completely new niches where there's limited competitive data to analyze.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS founders looking to apply this approach:
Focus on feature-based and use-case keywords for your product categories
Research competitor product pages and feature collections
Map keywords to trial signup intent vs. research intent
For your Ecommerce store
For ecommerce store owners:
Start with your highest-converting product collections
Research seasonal and trending variations for each category
Focus on commercial intent keywords that drive actual purchases