Keyword tools are useless for identifying the ultra-long-tail queries (often 60+ words) that drive Answer Engine Optimization. The best source for this content is your own first-party data. Analyze support tickets, sales call transcripts, and Reddit threads to discover the highly specific questions your customers are actually asking.
Every customer call is a potential blog post. An AI workflow systematically redacts all sensitive and identifying information from call transcripts, then rewrites the core use-case discussion into an SEO-optimized article. This creates a scalable content machine fueled by real customer problems, generating thousands of posts.
Users often ask LLMs specific feature, integration, and use-case questions ('can your product do X?'). These are frequently answered in help center articles. Optimizing this content for AEO—especially for long-tail queries—allows you to win high-intent traffic that traditional SEO often overlooks.
Instead of guessing keywords, an LLM analyzes customer call transcripts to identify the exact terms customers use to describe their needs. These keywords are then automatically added to Google Ads campaigns, creating a closed-loop system that ensures marketing spend is aligned with the authentic voice of the customer.
Users now ask AI models highly specific, long-form questions, not short search terms. HubSpot's CEO advises creating more detailed content with better citations and case studies to provide authoritative answers for these complex queries and remain visible.
While long-tail SEO has become less effective, it's a primary strategy in AEO. Users ask longer, more conversational questions (25 words on average vs. 6 for search). Companies can win by creating content that answers very specific, niche questions that have never been searched for before.