New case study just dropped. I helped an ecommerce store generate $6.7M in total sales last year using nothing but SEO. No paid ads involved. Just organic. I've documented the entire process in a detailed case study that covers every single step we took, in the exact order we executed it. Here's what's inside: - The full technical SEO audit process and what we fixed before touching any content - How we ran the content audit and decided what to delete, merge, update, or leave - The exact process for building topical maps around central entities, not keyword spreadsheets - How we rebuilt category pages from empty product grids into semantic hubs that actually rank - What our product page engineering looks like with entity coverage, schema, and conversion elements - How we structure semantic content briefs including semantic triples, E-A-V triples, and context vectors - The messy middle content strategy that most ecommerce brands completely ignore - Our internal linking architecture and how we push authority to the pages that make money - Visual semantics and how images reinforce entity associations - AI search visibility including featured snippet engineering, AI Overview optimization, and zero-click strategy - Why the whole thing compounds month after month instead of flatlining This is not a surface level overview. Every section explains the reasoning behind the decisions and gives you enough detail to understand how to apply it. Here is the link: https://docs.google.com/document/d/16Un2aLiOzc9WWkepq2pHxoSoYzchnjg-0SFIO8EO_Rw/edit?usp=sharing

After auditing 50+ ecommerce stores this year, I kept seeing the same problem destroy rankings over and over again. Keyword cannibalization. Pages competing against each other. Ranking signals split. And store owners wondering why their content won't rank — despite doing "everything right." So I built a framework we use internally at EcomHolistic to diagnose and fix this at the root. What's inside: → How to find competing pages in GSC (the exact reports we pull) → 5 questions to decide: merge, redirect, or differentiate → Entity-based internal linking to define semantic boundaries → URL consolidation strategies for product variants → Content brief structure that prevents future cannibalization This isn't some generic checklist you'll find on a blog. It's the exact process we use with clients paying us thousands for ecommerce SEO. Sharing it here Grab it below 👇 https://docs.google.com/document/d/11RIoltikBCKXOMK1u9c0N18cmz81Nx1BOifIUf1NbpI/edit?usp=sharing

Been getting a lot of DMs asking: "Ahmad, how do you actually approach ecommerce SEO?" So I sat down and filmed everything. The full 90-day framework. Start to finish. This is what we charge thousands for — now it's free on YouTube. What's inside: → The semantic SEO approach that builds authority FAST → How to structure your store for search visibility → The content strategy that compounds → Real client results (including the $3.5M case study) No gatekeeping. Just the playbook. 🎬 Watch now: https://www.youtube.com/watch?v=-wsVBM5BNzg One ask: If this helps you — share it with one other ecommerce founder who needs it. Questions after watching? Drop them below. I'll answer everything.

A $50M ecommerce brand hired me last year. First thing I told them: "Your keyword strategy is solid. But Google doesn't understand your products." They looked confused. "We rank on page 1 for most of our target keywords." I pulled up their product data. - GMC feeds: half-empty attributes. - Structured data: missing on most pages. - YouTube presence: not much. - Reviews mentioning features: barely any. "You're ranking," I said. "But you're invisible to a lot of AI-powered product discovery." Then I explained the shift. Ecommerce SEO isn't about keywords anymore. It's about whether Google understands your product as an entity. This is the shift most brands missed. Google built the Shopping Graph which contains billions of product listings, helping users find specific products. A semantic database where products exist as entities, not pages with keywords. Every product connects to attributes like price, brand, color, size, rating, and stock. It indexes billions of products and uses machine learning to match them to user queries. When someone asks AI for a product recommendation, it queries this Graph. The Shopping Graph pulls product data from: → YouTube videos → Manufacturer websites → Online shops and product detail pages → Google Merchant Center → Google Manufacturer Center → Product reviews Both structured data (like GMC feeds and schema) and unstructured content (like video transcripts and review text) contribute to it. Their products weren't showing up in these places. Not with the right attributes. So we fixed it. → Completed every GMC feed attribute → Rewrote product pages to naturally describe features and use cases → Created YouTube content for top products → Built a review system that encouraged specific feature mentions → Implemented full product schema markup The focus shifted from keywords to entities, attributes and relationships. Six months later? Their products started appearing in AI-generated recommendations.

Just dropped something I've been working on. A complete 90-day playbook for ecommerce founders who want to: → Stop relying 100% on paid ads → Build organic traffic that compounds → Lower blended CAC over time → Show up on Google AND AI search platforms This is the exact system. Week by week. Day by day. Phase 1 (Days 1-30): Foundation Technical fixes, entity mapping, site architecture, quick wins Phase 2 (Days 31-60): Authority Building Topical maps, semantic content networks, programmatic SEO, AI search optimization Phase 3 (Days 61-90): Scaling Double down on winners, CRO, link building, future planning No fluff. No theory. Just what to do and when. Grab it here: The 90-Day Ecommerce SEO Revenue Playbook If you implement any part of this, drop a comment. I want to hear what's working for you.