Effective Answer Engine Optimization (AEO) isn't about traditional keywords. It requires creating hundreds of niche content variations to match conversational queries. Furthermore, it involves a targeted "citation" strategy, focusing on getting mentioned on platforms with direct data licensing deals with specific LLMs (e.g., Reddit for ChatGPT), as these are prioritized sources.

While AI will diminish the value of traditional blog content for SEO, interactive mini-tools are far more defensible. Pages that offer a small piece of product functionality (e.g., a "Text-to-Speech Spanish" generator) provide immediate value that LLMs can't easily replicate, securing long-term organic traffic.

Generative AI has neutralized content volume as a competitive advantage. In fact, inconsistent messaging across many assets can penalize a brand in AI models. This reverses the old SEO playbook, making it critical to focus on fewer, higher-quality pieces with deep expertise and a consistent narrative across all channels.

AI search is the new overpowered marketing channel, with traffic converting up to 17x higher than Google. To get featured, invest heavily in comprehensive "alternatives to [competitor]" and "[your product] vs [competitor]" pages, as these are the bottom-funnel queries AI models cite most often.

A powerful tactic for e-commerce is duplicating a main collection page into numerous niche versions (e.g., "tote bags for women"). Each page uses the same products but has a unique URL, headline, and descriptive copy, effectively creating highly-targeted landing pages for search engines and LLMs.

Brands applying traditional SEO best practices to Generative Engine Optimization (GEO) will fail. LLMs understand semantic meaning, making keyword stuffing obsolete. Similarly, they weigh source authority and consistency over raw backlink volume, invalidating old link-building schemes.

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.

The company used a Python tool to programmatically generate huge volumes of CV example pages. While initially effective, they pushed it too far, creating near-duplicate content (e.g., "sales executive CV" vs. "sales exec CV"). This confused Google's crawlers, hurt rankings, and forced a massive content pruning project to recover traffic.

Scrape questions and conversations from your community forums or Slack channels. Use this data as a prompt to programmatically create hundreds of specific landing pages that answer real user queries. This strategy builds the hyper-niche content required to rank well in conversational AI search engines.