GEO Keyword Mining System

Problem

A common GEO blocker is not a lack of content, but a lack of intent coverage: how a brand is mentioned in AI answers, which industry trends and pain points matter, which search intent a user is in, and which long-tail questions are worth prioritizing. Stacking brand terms can miss transactional, comparative, and scenario-based questions.

How it works

The skill structures keyword discovery into four layers:
- Brand layer: extract brand mentions from websites, business records, and AI answers.
- Industry layer: collect generic, trend, standard, pain-point, and comparison terms.
- Intent layer: generate terms around navigation, informational, transactional, comparison, geo, and scenario intents.
- Long-tail layer: mine low-competition, high-conversion questions from support notes, sales scripts, and search suggestions.

The workflow starts with company, industry, product/service, and target region. It probes AI platforms with core industry terms, captures follow-up questions, builds the four-layer keyword set, scores terms by search frequency, competition, and conversion value, and outputs P0, P1, and P2 priorities with content-matching guidance. The intent layer is treated as the core and should receive the most attention.

Boundaries

It fits GEO content planning when the company, industry, and target region are known. Output quality depends on input richness, AI follow-ups, customer questions, and platform terms; long-tail and intent terms should be validated against real business context.