Entity-based Search and Brand Authority Building in the LLM Era – Lesson Preview
Most keyword research workflows stop at “export from one tool, sort by volume, pick targets.” In this pre lesson, you’ll see why that approach breaks down in a semantic, entity-driven search landscape, and how to build a true keyword universe that can support serious content and topical mapping.
You’ll walk through the first (and most critical) step of semantic keyword research: compiling a robust, multi-source database of queries, entities, and patterns for your brand and competitors. The instructor shows how to combine ranked keywords, competitor terms, People Also Ask data, trending topics, and your own pattern-based research into one backbone dataset, whether you work in Google Sheets or BigQuery/Python.
Finally, you’ll learn how to distinguish business vs organic competitors, use APIs and no-code tools to scale data gathering, and feed everything into a semantic keyword universe that powers better topical maps, content briefs, and content quality. The core message: data in = data out. If you get this step right, every downstream SEO and content decision becomes more strategic and defensible.
What you’ll learn (why it matters)
- Build a multi-source keyword universe because one tool alone misses critical queries.
- Differentiate business vs organic competitors because each drives a different SEO and partnership strategy.
- Leverage autosuggest and question data because it reveals real user journeys and intent gaps.
- Use pattern-based keyword generation because entities and query patterns uncover scalable, programmatic opportunities.
- Incorporate trending and review-based terms because fresh, user-language data keeps your strategy current.
- Decide when to move from Sheets to BigQuery/Python because scale and complexity demand the right data stack.
Key concepts (with mini-definitions)
- Semantic keyword research — Analysing queries as connected concepts and entities, not isolated strings.
- Keyword universe / semantic keyword universe — The master database of all relevant queries, entities, and patterns for your brand or niche.
- Business competitors — Companies you compete with for both traffic and revenue/market share.
- Organic competitors — Sites you only compete with for search traffic (e.g. media, affiliates, creators).
- Pattern-based keywords — Queries built from repeatable patterns (e.g. “how to + product” or “buy + product + online”) combined with entities.
- Content gap keywords — Terms your competitors rank for that your site has not yet targeted.
- Trending keywords — Rising-interest queries identified via tools like Google Trends and Exploding Topics.
- People Also Ask / question-based keywords — Question queries pulled from SERP features that show how users explore a topic.
Tools mentioned
Google Search Console, Search Analytics for Sheets, Google BigQuery, Python, SE Ranking, SEMrush, Ahrefs, Pemavor autocomplete keyword tool, DataForSEO, Google Autosuggest, Google Maps Platform (Query Autocomplete, Place Autocomplete via Places API), Google Colab script for Autosuggest, Keywords Everywhere, AlsoAsked, KeywordsPeopleUse, AnswerThePublic, Backlinko keyword tool, SEO Minion Chrome extension, Apify, Google Trends, Google Trends API, PyTrends (unofficial), Exploding Topics, Google Cloud Natural Language API (Entity Analysis), social platforms (YouTube, TikTok, Reddit) as data sources, SEMrush Keyword Magic Tool, Google Ads Keyword Planner, TikTok Ads, YouTube Ads, internal site search, review site scrapers.
Practice & readings
- Follow the Google Autosuggest Google Colab script to expand 100+ seed keywords into clustered suggestion sets.
- Reproduce the People Also Ask collection workflow using SEO Minion or an Apify scraper, then merge outputs into your keyword universe.
- Apply the EAV model and pattern-based approach from earlier lessons to create your own branded and non-branded keyword patterns, then enrich them with search volume and difficulty data.
Key insights & takeaways
- Relying on a single keyword source guarantees blind spots; depth comes from combining ranked, competitor, PAA, trend, and pattern-based data.
- Distinguishing business from organic competitors changes how you treat them—from pure rivals to potential allies or amplifiers.
- Pattern-based, entity-focused research turns messy exports into scalable, programmatic keyword systems.
- The robustness of your keyword universe directly controls the quality of topical maps, content briefs, and final content.
- Data in = data out: investing in data collection quality is the fastest way to improve every downstream SEO decision.
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