How I Do Keyword Research 100% Free in 2026
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Unlock the exact free workflow I use to build full keyword research lists without spending a penny.
Most SEOs massively overcomplicate keyword research, but once you see this process you will never rely on paid tools again.
Inside the video I break down how to map search intent, reverse-engineer competitors, and extract every keyword your niche needs using nothing but Google, sitemaps, and Notebook LM.
Whether you run an e-commerce store, a local business or an affiliate site, this method works for every industry and lets you outrank competitors without brute-forcing links.
What This Video Covers
You’ll learn how to identify top, middle and bottom-of-funnel keywords using real entity-driven examples.
You’ll see why most websites fail because they only target one intent cluster instead of building full topical depth.
I’ll walk you through how to reverse-engineer competitor sitemaps to uncover every page they rank for.
You’ll also learn three free methods to expand your keyword list: Alphabet Soup, Google Dorks and Notebook LM mind-maps.
By the end, you’ll know how to design a complete URL structure that increases topical authority and reduces the need for backlinks.
What You’ll Learn
• How Search Intent Mapping (subject) determines ranking velocity (relation) through funnel-aligned keyword clusters (object).
• Why Bottom-of-Funnel Pages (subject) drive revenue (relation) by matching commercial investigation queries (object).
• How Competitor Sitemaps (subject) reveal missing topical entities (relation) via structural reverse-engineering (object).
• How Notebook LM Mind-Maps (subject) create semantic clarity (relation) with hierarchical topic grouping (object).
• Why Topical Supports (subject) strengthen page authority (relation) through internal relevance flow (object).
• How Google Alphabet Soup (subject) uncovers long-tail demand (relation) using predictive search expansion (object).
• How Google Dorks (subject) expose informational content gaps (relation) through advanced operator filtering (object).
• Why Overlapping Query Variants (subject) trigger cannibalisation (relation) when semantic duplication occurs (object).
• How Local Service Entities (subject) shape keyword lists (relation) through geo-modifier demand patterns (object).
• How URL Structure Planning (subject) improves crawl predictability (relation) by aligning site architecture with query clusters (object).
• Why Competitor Content Velocity (subject) indicates ranking difficulty (relation) through depth-based topical advantage (object).
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