This changes everything about keyword research:
LLMs often default to their internal knowledge for broad queries like ‘What is CRM software,’ but switch to live retrieval for specific, intent-rich prompts.
When users ask broad questions like “what is CRM software,” Claude and ChatGPT often won’t even search the internet. They’ll just spit out generic answers from their training data.
But ask “best CRM for 5-person agencies under $100/month” and suddenly they’re citing specific brands and pulling fresh sources.
This explains why generic keyword targeting is dying while micro-intent queries are exploding.
At uSERP, we’ve been tracking this shift across 200+ client queries. The pattern is clear: brands targeting precise buyer language get cited ~5x more often than those chasing broad evergreen terms.
Here’s our micro-intent targeting playbook:
- Target long-tail, specific queries instead of broad terms. “Marketing automation” gets generic AI responses. “Marketing automation for SaaS companies with under 50 employees” gets brand citations.
- Layer context qualifiers like industry, company size, or budget constraints. The more specific the query, the more likely AI systems will search for and cite current sources.
- Structure for immediate answers: Lead with clear responses in the first 150 words. Use “This section explains…” to provide context that helps AI systems understand content boundaries.
- Back claims with proof through specific case studies and data points. LLMs favor content with verifiable information over generic advice.
- Build community context by targeting Reddit and Quora discussions where buyers ask these specific questions naturally.
The shift is happening now. Broad keyword strategies are becoming obsolete while precise buyer intent targeting drives AI citations.
Want to audit your keyword strategy for LLM optimization? We’re seeing huge increases in AI citations for clients who make this shift. Book a strategy call at uSERP to get started.
-Jeremy
